Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms

Abstract

The integration of the IoT into healthcare opens new horizons while introducing ethical and legal challenges to preserving patients’ privacy. This paper provides a comprehensive review of privacy-preserving mechanisms in IoT-based healthcare systems, analyzing key challenges such as secure data transmission, decentralized processing, privacy-preserving analytics, and user-centric control. We classify existing privacy solutions into a structured comparative framework, highlighting their integration strategies, security measures, and technical implementations in scalable architectures. Additionally, we discuss emerging trends and open research challenges that require further exploration. This study is a valuable reference for researchers, practitioners, and policymakers seeking to develop and enhance privacy-preserving solutions in IoT-based healthcare environments.

1. Introduction

Global outbreaks of pandemics and health crises, such as COVID-19, have underlined the vital role of the healthcare sector in safeguarding the well-being of patients and preserving public health [1]. Traditionally, healthcare activities depended on paper-based health records and direct face-to-face interactions between patients and healthcare providers [2]. In any case, there has been a considerable shift towards digital healthcare, which involves the integration of technology into medical processes and practices.

Developing new medical technologies transforms the healthcare industry, simplifies the process of the collection, storage, and analysis of patients’ data by healthcare professionals, and leads to efficient and customized services [3]. Furthermore, digital healthcare enables proactive and preventive healthcare by supporting remote consultations, tracking, and monitoring [4].

Along with the benefits introduced by rapid digitization, the storage of patients’ sensitive data presents new worries regarding the unauthorized accessing or misuse of these data. New potential threats, data breaches, and challenges have arisen in the healthcare sector that might affect patients’ data privacy and security [5]. These challenges encourage the application of strict frameworks such as the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) [6], making trust and confidentiality crucial for healthcare providers.

The integration of the IoT in healthcare encounters multiple privacy challenges related to confidentiality, integrity, and access control. Various studies have explored different privacy-preserving techniques to address these concerns. This paper reviews the existing literature on these challenges and the corresponding solutions, organizing them into a structured framework. The review highlights how different approaches contribute to safeguarding healthcare data while maintaining system efficiency.

This paper discusses the issues surrounding privacy preservation in Internet of Things (IoT)-based healthcare systems and covers different parts of privacy preservation for healthcare data through the following sections. Section 2 provides the background on essential concepts and requirements for understanding digital healthcare. In Section 3, we present a framework focused on enhancing confidentiality, integrity, and access management to ensure healthcare data privacy. The section discusses advanced privacy protection techniques, including the technical aspects of implementation and associated challenges. Section 4 describes the methodology used in this review, detailing the systematic approach for reviewing existing literature on privacy-preserving techniques in IoT-based healthcare systems. Section 5 presents a literature review showing the current state of privacy preservation.

In Section 6, Section 7, Section 8 and Section 9, a range of topics are covered, including scalable architectural systems, secure data sharing, patient monitoring, and the integration of IoT devices that preserve user privacy. Section 10 explores open issues and future trends, while Section 11 focuses on challenges and opportunities in IoT-enabled healthcare innovations and concludes the paper with an overview of future research paths in this area.

2. Background on Concepts and Requirements

In the following section, we discuss the balance between the critical flow of information and the preservation of core privacy rights and the aspects of data management that have been developed. Furthermore, we present the many-sided protections applied to physical spaces, medical records, and digital health data. Also, we discuss the difficulties related to responsible access management and review the HIPAA privacy framework, which considers balancing individual privacy protection with enabling efficient healthcare. This section aims to cover questions concerning ethical and technological challenges to ensuring the security of health information within the digital era.

2.1. Dimensions of Privacy and Privacy Protection

Scholars and experts, as well as countries, define the concept of privacy from different perspectives. As a fundamental human right, privacy includes the right to be free from interference and intrusion. It supports many other rights essential to preserving personal freedom and dignity, protecting identities, managing interactions, and dealing with surroundings. Furthermore, privacy allows us to build solid boundaries to control who can access our personal spaces, belongings, communications, and data. Privacy takes different shapes across our lives, including physical, informational, and communication privacy [7] Figure 1.

• Physical privacy: The capability of people to set boundaries regarding their belongings and actions and control access to their physical space.
• Informational privacy: The capability of people to control access to their sensitive information, including financial and medical records.
• Communication privacy: The capability of individuals to control access to their private and personal communications, such as phone calls, emails, and messages.

Privacy in the healthcare sector includes the condition that patients’ information, like their medical history, health conditions, or even treatment details, should be protected from non-authorized access. Due to the transition towards digitization in healthcare, data must be exchanged with multiple networks [8], including insurance and financial organizations; therefore, ensuring the privacy of the data is paramount. IoT technologies pose exceptional challenges to traditional privacy guarantees. Due to the variable nature of these systems, which involve continuous data flows, communication between different devices, and variable access control, they need careful strategies to be implemented to ensure secure access. In the era of IoT technology, the complexity of protecting patient data has increased, necessitating the development of new standards to address traditional issues related to privacy and dealing with information-rich environments.

Privacy in healthcare presents in many ways [8], including the following:

• Electronic privacy: Focuses on protecting electronic health records and any other health data so that they remain safe and protected from any unauthorized access or leakage. These records hold detailed information, including the patient’s medical history, diagnoses, determined treatments, vaccination dates, allergies, radiological images, and medical test results [9].
• Digital health-related data privacy: This term covers a broader range of digital health information, extending beyond traditional electronic health records. It includes data gathered from wearable devices, remote monitoring devices, genetic testing results, and a wealth of other digital health information that, while not always included in the electronic health record, plays an important role in facilitating a fuller picture of a patient’s health status.

2.2. Privacy Requirements in Healthcare

Effective privacy management in healthcare is based on a solid framework of rules, regulations, and policies that define how medical data are collected, managed, stored, and shared. Examples of these frameworks include the GDPR, HIPAA, and the ETSI EN 303 645 standard [10] for cybersecurity in the IoT. Compliance with these privacy requirements is critical to developing successful healthcare privacy solutions. A lack of compliance could compromise the strategies employed to protect the confidentiality, integrity, availability, and accountability of healthcare data. The following requirements serve as the foundation for securing sensitive medical information in healthcare:

• Confidentiality Requirement: Healthcare data remain confidential (not disclosed to unauthorized parties) through access control and encryption techniques.
• Integrity Requirement: Solid data validation methods are implemented to provide data accuracy and consistency.
• Availability Requirement: The backup and disaster recovery policy guarantees that approved entities have access to the healthcare information.
• Accountability Requirement: Audit trails and monitoring mechanisms manage accountability, confirming that the use and disclosure of healthcare data are in compliance with laws and regulations.
• Access Control Requirement: Access control ensures that only authorized parties have access to patient medical information on a need-to-know basis.
• Data Minimization Requirement: The collection of patients’ personal information is limited only to that which is necessary for the provision of healthcare services.

2.3. HIPAA: Operationalizing Privacy Requirements

A comprehensive regulatory framework is in place under the HIPAA, a United States (U.S.) law introduced in 1996. The HIPAA aims to safeguard individuals’ health information by ensuring its confidentiality, integrity, and security while facilitating its effective flow to address essential medical needs. This law establishes a set of rules that govern how healthcare practices comply with privacy and security requirements, determined by numerous studies. These guidelines include the following [11]:

• Protected Health Information (PHI): The healthcare system’s main operating entities, like hospitals, financial institutions, insurance providers, and many others, transfer health data and refer to it as PHI.
• Acceptable Uses and Disclosures: The Privacy Rule governs how appropriate entities can use and disclose PHI. Most uses and disclosures are subject to individual consent under these rules.
• Minimum Necessary Standard: The participating entities in healthcare must request, use, and disclose the minimum amount of health information to achieve their goal and guarantee that only needed information is disclosed and used to fulfill this purpose.
• Individual Rights: When it comes to personal PHI, people have certain rights that include accessing it, asking for changes, obtaining an account of disclosures, and placing restrictions on its use.
• Notice of Privacy Procedures: The covered entity must be provided with a detailed notice of their legal responsibilities, privacy policies, the usage of PHI, the procedure of disclosure, and the mandatory rights for each individual.
• Safeguards: Protecting the privacy, accuracy, and accessibility of PHI requires covered organizations to establish administrative, technological, and physical protections designed to prevent unauthorized access, use, or disclosure.
• Business Associate Agreements: A formal agreement with third parties is a must for covered entities so they can handle the PHI on their behalf and make sure that other partners adhere to the HIPAA’s specified privacy and security rules.
• Breach Notification: In the U.S., if PHI is involved in a breach, the covered entities are responsible for notifying the Department of Health and Human Services (HHS), any impacted parties, and sometimes the media. The HHS is a federal agency tasked with protecting the health of Americans and ensuring the privacy and security of their health information. Within the HHS, the Office for Civil Rights (OCR) is responsible for enforcing the HIPAA rules, including breach notification requirements. Typically, the notification discloses the full situation and outlines the steps taken to mitigate the effects.
• Implementation: The responsibility of the HHS OCR is to implement privacy rules, along with programs of remedial action and civil financial penalties. Furthermore, noncompliance with privacy rules might lead to criminal consequences.

To protect the privacy of patients, determining the amount of data that need to be shared and the protection of health information are the responsibilities of the HIPAA. The HIPAA’s law guarantees the protection of health information, as well as guiding acceptable practices and implementing strict security measures. Legal compliance and maintaining the safety and security of medical records depend on adherence to these standards.

2.4. GDPR in Healthcare: Expanding Privacy Horizons

The GDPR, effective from May 2018, directs the use of citizens’ data both inside and outside of the European Union (EU), applies to international organizations that manage these data (Article 3, paragraph 2), and has significantly impacted the handling of health data alongside the HIPAA. The main rules that the GDPR addresses are listed as follows [12]:

• Personal Data Protection: The GDPR broadens the scope of protection for information collected and processed by IoT devices in the healthcare industry, including health-related data.
• Legal Basis for Processing: To process personal information, including health information, there must be a clear legal basis that complies with the GDPR, which may include consent, contractual requirements, legal duties, vital interests, public interest, or legitimate interests.
• Data Minimization: Under the data minimization rule, companies can manage and gather only required information that serves a certain purpose related to healthcare.
• Individual Rights: Individuals have the right to access, update, and delete their data, as well as making the data portable and limiting its processing. Also, the responsibility of institutions is to manage these data and healthcare services to support these individuals’ rights.
• Data Security: Frequent security evaluations, encryption, and many other security measures are strictly enforced by the GDPR to preserve the security and privacy of individuals’ data.
• Data Breach Notification: In the event that a data breach occurs, the responsible authorities and individuals involved in the case must be notified, and it must be announced immediately. Moreover, the GDPR enforces the presentation of the protocols used to identify and handle a data breach event.
• Data Protection Impact Assessment (DPIA): The GDPR enforces enterprises to undertake a DPIA to specify and reduce processing operations with high risks.
• Appointment of a Data Protection Officer (DPO): Designating a DPO is a must for enterprises and organizations handling individuals’ data, such as health data, which guarantees their adherence to and compliance with the GDPR.
• GDPR Enforcement and Penalties: The value of the penalties that the data protection authorities can apply upon organizations’ failure to adhere to the GDPR is up to EUR 20 million or 4% of an organization’s global annual revenue.

As interconnected systems are growing rapidly in a world full of digitization, individuals must be familiar with the GDPR, which effectively works to guarantee the safety of their data within the EU, alongside the HIPAA in the U.S. (HHS, 2023).

2.5. Involved Parties in Healthcare Systems

Smart healthcare systems are designed to provide patients with the best healthcare possible. Therefore, they are a combination of specific entities that perform specific tasks and responsibilities. The following are the major entities that form and create smart healthcare systems [6,13]:

2.5.1. Core Healthcare Players

• Healthcare Providers: This category consists of the persons (doctors, nurses, specialists) and locations (from general hospitals to private practices) which render necessary medical services to patients. Healthcare technology aids a provider’s capacity to provide appropriate treatment at the proper time and make diagnoses.
• Patients: The smart healthcare system revolves around the patients who interface with the system for care, questions regarding treatment, health status checks, and attempts at more personalized healthcare. When patients are involved regarding their information and communication, general health outcomes tend to be better.
• Paying Entities: They interface with healthcare systems by managing the responsibility for payment, reimbursement, and billing matters. Paying entities include insurance companies, employers, and other organizations or individuals responsible for ensuring proper billing for medical services.

2.5.2. Other Involved Parties

• Technology Providers: Their responsibility includes designing, developing, and deploying the required technological infrastructure to support smart healthcare systems which enable a seamless flow of data exchange and efficient healthcare services. They design and offer a range of tools and solutions, including electronic health record (EHR) platforms, telemedicine applications, wearable device manufacturing, and health applications.
• Regulators and Government Agencies: Entities like the Data Protection Officer (DPO) and OCR play a critical role in enforcing compliance with the GDPR in the EU and the HIPAA in the US and safeguarding patients’ data. These agencies are responsible for overseeing the application of standards and frameworks that control the healthcare sector.
• Researchers and Academia: Collaboration between researchers and academic institutions and smart healthcare systems opens the horizons for innovation in the healthcare sector through collecting and analyzing data to generate novel insights that enhance the quality of the healthcare services provided to patients.
• Caregivers and Family Members: Their responsibility is taking care of and assisting patients at home or in healthcare organizations. They must join the healthcare systems as they have to monitor the health state of the patient.

In the context of IoT-based healthcare systems, the roles of healthcare providers, patients, and technology providers become even more intertwined. IoT devices generate continuous patient data streams, making real-time data security a primary concern. Therefore, privacy-preserving techniques such as encrypted edge computing, federated learning, and blockchain-based identity management are crucial for ensuring patient confidentiality. These techniques will be explored in later sections, reinforcing the necessity of tailored privacy-preserving strategies for IoT healthcare ecosystems.

3. Stratifying Privacy-Preserving Solutions

Privacy and security in IoT-based healthcare systems are deeply interconnected, yet they address distinct concerns. Security focuses on protecting data from unauthorized access, tampering, and cyber threats, employing measures such as encryption, authentication, and intrusion detection. Privacy, on the other hand, ensures that individuals retain control over their personal health information, dictating how, when, and by whom data can be accessed. While security is a prerequisite for privacy, it is not sufficient on its own; privacy requires additional mechanisms, such as data minimization, anonymization, and user-controlled access. This review examines privacy-preserving solutions in IoT-based healthcare while acknowledging their dependence on robust security measures.

The integration of the IoT in healthcare solutions has highlighted the importance of complying with privacy regulations and preserving patients’ privacy, especially in relation to their sensitive data. This review presents a structured framework derived from the existing literature, summarizing the techniques that preserve the confidentiality, integrity, and access control of patient data. The framework helps guide the design and implementation of a privacy-preserving IoT-based healthcare system by presenting five elements of robust privacy-preserving solutions, as shown in Figure 2. These include Data Protection Mechanisms, access control and authorization, Privacy-Preserving Data Sharing, privacy policies and governance, and User Awareness and Empowerment. Furthermore, we present a classification of these core elements in Figure 3.

3.1. Data Protection Mechanisms

The basic mechanisms to preserve privacy in IoT-based healthcare systems are Data Protection Mechanisms. These techniques, including encryption and anonymization, are applied to patients’ data during several stages: data in transit, data at rest, and data in processing.

• Data in Transit: Refers to data actively moving between systems, devices, or networks (e.g., during communication between IoT devices and cloud servers).
• Data at Rest: Refers to data stored in a static state, such as on databases, servers, or local devices.
• Data in Processing: Refers to data actively being used or manipulated by applications or systems, such as during computations or analytics.

• Encryption Techniques: The process of using encryption techniques involves preserving the privacy of patient data through data encryption, such as symmetric, asymmetric, and homomorphic encryption, to convert the patient’s Personally Identifiable Information (PII) and PHI to a format that cannot be read or understood by unauthorized parties [14]. The encrypted PII/PHI, including texts or images, can then be securely sent, stored, and accessed by individuals with access privileges.
• Anonymization Techniques: Anonymization techniques are used for modifying or concealing healthcare data attributes to safeguard patient privacy while preserving the feasibility of data analysis. These techniques, such as k-anonymity, l-diversity, and differential privacy, are used to ensure that sensitive data remain protected while still being useful for research and analysis [13].

3.2. Access Control and Authorization

Access control and authorization mechanisms control data access and ensure that only authorized entities can retrieve and manipulate patient data. These mechanisms focus on controlling and managing permissions and privileges to protect sensitive information. These access control mechanisms include the following:

• Role-Based Access Control (RBAC): RBAC assigns access permissions based on predefined roles and responsibilities. It ensures that users access data and system functionalities based on their assigned roles, reducing the risk of unauthorized access [15].
• Attribute-Based Access Control (ABAC): Taking into consideration many factors, such as the user’s characteristics, external elements, and environmental conditions, ABAC permits the user access based on their attributes and policy rules.
• Consent Management Systems: These provide mechanisms for patients to control their data, as they assist in collecting, acquiring, managing, using, and sharing data, delegating the responsibility to patients to make knowledgeable decisions [16].

3.3. Privacy-Preserving Data Sharing

Privacy-preserving data-sharing techniques enable collaboration and analysis while protecting patient privacy. These techniques focus on methods that facilitate secure data sharing among multiple entities.

• Blockchain and Distributed Ledger Technologies: A blockchain provides a platform for managing and storing encrypted patient data in a decentralized and inviolable manner, ensuring data integrity, controlled access, and auditability [14].
• Secure Multi-Party Computation (SMC): SMC allows for computations on distributed data while protecting individual privacy. It leverages cryptographic techniques to perform calculations across multiple parties without revealing sensitive information.
• Federated Learning (FL): FL facilitates collaborative machine learning (ML) without the need to share raw data between entities. Instead, ML models are trained locally on individual datasets, and only the model updates are shared. This approach preserves confidentiality and enables collective intelligence while maintaining individual data sanctuaries, making it an effective privacy-preserving mechanism in healthcare [14].

3.4. Privacy Policies and Governance

Privacy policies and governance frameworks establish the foundational principles and mechanisms for ensuring accountability and privacy compliance in IoT-based healthcare systems. These frameworks guide organizations in implementing effective strategies to protect patient data and build trust among stakeholders. Two key approaches include the following [14]:

• Privacy by Design Principles: Considers privacy in the development process of IoT-based healthcare systems, encouraging in-advance privacy measures from the early stages and ensuring privacy is a basic component of the system’s architecture.
• Transparent Data Usage and Auditability Mechanisms: Mechanisms for transparent data usage and auditability provide visibility regarding how patient data are accessed, used, and processed. They enable individuals and regulatory bodies to monitor and enforce privacy compliance.

3.5. User Awareness and Empowerment

This category emphasizes the importance of user education, awareness, and control in preserving privacy in IoT-based healthcare systems. By educating individuals and providing them with tools to control their PHI, these measures enable users to actively participate in privacy protection. Key aspects include the following:

• User Education and Awareness Programs: These programs aim to establish knowledge and awareness among individuals regarding data processing practices, privacy risks, and their rights regarding PHI. Such programs can delegate the responsibility to the users to make knowledgeable decisions and apply proactive measures to assure privacy compliance [17].
• User-Controlled Privacy Settings: User-controlled privacy settings enable individuals to manage their privacy preferences and customize the level of data sharing and access permissions. Users can control how their personal health information is used and shared.
• Privacy-Enhancing Technologies: Such technologies comprise tools, techniques, and interfaces that enable users to protect their privacy while benefiting from IoT-based healthcare services. They allow individuals to preserve their privacy without compromising the functionality and utility of the system [17].

The above five core components are the central areas of focus in addressing privacy concerns in IoT-based healthcare systems. These components are widely investigated and challenging due to their pivotal role in ensuring that healthcare data are kept private. Because of their complexity, it is necessary to invest in focused research efforts to develop robust solutions that comply with regulations while effectively balancing privacy with the utility of healthcare data.

4. Methodology

In this review, we aimed to search for IoT-based healthcare systems that use privacy-preserving techniques, as shown in Figure 4. To gather related studies, we performed a structured search on several well-known academic databases, including IEEE Xplore, PubMed, Scopus, SpringerLink, and Elsevier ScienceDirect. Several keyword combinations were used during our search, such as the following:

• (“IoT” OR “Internet of Things”) AND (“Healthcare” OR “Medical”) AND (“Privacy” OR “Security” OR “Confidentiality”).
• (“Data Privacy” OR “Privacy-Preserving”) AND (“IoT Security” OR “Healthcare Data Security”).
• (“Federated Learning” OR “Homomorphic Encryption” OR “Blockchain”) AND (“Healthcare Applications”).

After the search process, we initially gathered 80 research papers, focusing on studies published between 2020 and 2024. However, some highly cited articles from 2018 and 2019 were included, as they provided essential background knowledge on privacy-preserving techniques. Moreover, we followed a three-step process to improve the selection: removing duplicate papers, reviewing titles and abstracts for relevance, and conducting an in-depth full-text analysis. After this process, we reduced our focus to 58 studies that aligned closely with our research objectives and classified the selected studies according to their proposed privacy-preserving techniques, such as blockchains, federated learning, and homomorphic encryption. We also reviewed their key contributions, including novel frameworks and approaches to Secure Multi-Party Computation. In addition, we considered the methodologies used in these studies, including whether they involved simulations, real-world implementations, or theoretical analysis. A significant part of our evaluation also looked at the challenges related to using these privacy-preserving techniques, such as scalability concerns, computational demands, and risks related to data sharing.

To present our findings clearly, we have used a table to summarize different privacy-preserving techniques and their applications. A framework was used to group the studies based on the privacy-preserving techniques they investigated, allowing us to identify key trends, highlight research gaps, and outline potential future directions in this field.

5. Literature Review

As we investigated the current literature concerning privacy in healthcare, it appeared to encompass a complex interplay of academic insights, regulatory structures, and technological developments. The following Literature Review Section presents relevant papers exploring security and privacy aspects in IoT-based healthcare systems. These papers shed light on the healthcare industry’s challenges and propose innovative solutions to ensure the confidentiality and privacy of sensitive healthcare data Figure 5. The selected literature covers various topics, including security requirements, encryption techniques, blockchain-based solutions, privacy preservation, and data integrity. By examining these works, researchers in the field can gain valuable insights and inspiration for their studies.

To provide a better understanding, we classified each presented paper based on the five core components in Figure 2, providing a specific perspective on their contribution to preserving privacy in the proposed IoT-based healthcare systems, as shown in Figure 3.

Each paper will be accompanied by its respective classification, providing a nuanced perspective on its contribution to preserving privacy in the IoT-based healthcare system landscape. The classification of the reviewed works is summarized in Table 1.

5.1. Comparison with Existing Review Papers

Several papers have explored privacy-preserving techniques in IoT-based healthcare, each differing in their focus, scope, and methodology. Below, we compare our study with key existing reviews and highlight how it expands on their findings.

Dhinakaran et al. [18] examined AI-driven privacy-preserving techniques, particularly machine learning-based anonymization and AI-enhanced access control. While insightful, their study lacked a broader classification of privacy-preserving mechanisms applicable to IoT healthcare. Our review goes beyond AI-based solutions, systematically categorizing various privacy techniques.

Nasiri et al. [19] focused on cybersecurity risks and resilience in IoT-based healthcare, using the CIA Triad (Confidentiality, Integrity, Availability) framework. However, their work did not classify privacy-preserving techniques or evaluate their effectiveness. In contrast, our review systematically organizes privacy-preserving solutions, assessing their real-world applicability and limitations.

Hathaliya et al. [20] analyzed security and privacy in Healthcare 4.0, covering biometric security, blockchain, and cryptographic solutions. However, their focus was primarily on security rather than a structured classification of privacy solutions. Our review fills this gap by systematically categorizing privacy-preserving methods specific to IoT-based healthcare.

Bhuiyan et al. [21] provide a comprehensive review of the IoT in healthcare, covering technological advancements, security challenges, and market opportunities. Their study discusses various healthcare technologies and services but focuses primarily on general security models rather than privacy-preserving techniques. While they highlight potential risks and necessary security mechanisms, they do not offer a structured classification of privacy-preserving methods specific to IoT-based healthcare. Our study addresses this gap by systematically categorizing privacy-preserving techniques and evaluating their effectiveness in real-world applications, providing a more focused approach to privacy concerns in IoT healthcare systems.

Kumar et al. [22] proposed Secure Multi-Party Computation (MPC) and homomorphic encryption (HE) as privacy-preserving mechanisms for secure data sharing and disease prediction in online healthcare systems. Their work demonstrated how encrypted data sharing can improve privacy, utilizing the Random Forest Algorithm for symptom-based disease prediction. However, their study primarily focused on encryption-based techniques and lacked a comparative evaluation of other privacy-preserving solutions, such as federated learning, blockchains, and differential privacy. In contrast, our study extends beyond encryption-based methods, systematically classifying a wide range of privacy-preserving approaches and assessing their security guarantees, implementation challenges, and scalability.

Unlike previous work, our study systematically classifies privacy-preserving techniques in IoT-based healthcare, analyzing their functionality, security guarantees, and practical applications. This comparative approach provides a structured understanding of the existing solutions and their limitations. Additionally, we identify key research gaps and emerging challenges, offering insights for future advancements in privacy preservation.

While previous papers have provided valuable insights into specific privacy-preserving techniques, they often focus on particular aspects, such as AI-driven solutions, blockchain security, or cybersecurity risks. In contrast, our review offers a broader classification and comparative analysis of privacy-preserving methods across different IoT healthcare applications. In the following sections, we further explore the integration of the IoT in healthcare, analyzing key challenges, privacy concerns, and potential solutions.

5.2. IoT and Healthcare Integration

This section reviews the integration of the IoT in the healthcare sector by analyzing existing studies that discuss its challenges, opportunities, and applications.

Agbo et al. [23] explored the use of blockchain technology in healthcare, specifically in medical records, research, and patient monitoring. The authors discussed challenges, including interoperability, security, and scalability, proposing solutions involving encrypted data pointers and robust software development. The paper points to the blockchain’s opportunities and challenges for use in healthcare.

Kumar et al. [22], using Secure Multi-Party Computation (MPC) and homomorphic encryption (HE), proposed a novel approach for secure online healthcare systems. The system enables encrypted data sharing and disease prediction based on symptom similarity using the Random Forest Algorithm. The paper also discusses other data security models and techniques in healthcare.

A solution based on blockchains to preserve privacy and data integrity in IoT-based healthcare was presented by Bhalaji et al. [24]. The paper presents the risks associated with sensor data collection and proposes a privacy-preserving algorithm with efficient time complexity. The solution ensures data security and patient privacy.

Jeong et al. [25] proposed a blockchain-based monitoring system for the reliable and secure management of personal medical information. The system included a sensor unit, control unit, and smartphone interface, providing real-time data and risk analysis based on biosignals. The paper highlighted the system’s improved reliability and security, achieved through blockchain integration.

Bhuiyan et al. [21] provide a comprehensive review of the IoT in healthcare, covering technologies, security, and market opportunities. They propose a new security model, address challenges in transitioning to IoT-based systems, and review various healthcare technologies and services. The paper concludes with open issues and future research directions in IoT healthcare.

Ratta et al. [26] explore the potential of blockchains and the IoT for use in healthcare. The paper discusses their application in drug traceability, remote patient monitoring, and medical record management. Through innovative solutions, they focus on refining healthcare systems and addressing challenges such as interoperability and privacy concerns.

The issues with integrating blockchains in the IoT were investigated by Alzoubi et al. [27], and they provided recommendations to address these challenges, including authentication, data protection, stability, resistance to attacks, ease of deployment, and self-maintenance. Anonymity, permissionless blockchains, and pseudonymization were highlighted as significant concerns. The paper concludes by addressing unresolved issues and future trends in blockchain–IoT integration.

5.3. Blockchain-Based Solutions

In this section, we delve into various papers that have explored blockchain-based solutions for enhancing privacy and security in healthcare.

Husnain et al. [28] introduce HealthChain, a blockchain-based approach to enhance the security and interoperability of electronic health records (EHRs). By incorporating AES-256 encryption and Elliptic Curve Cryptography (ECC), it safeguards sensitive medical data while enabling efficient and seamless data exchange between healthcare providers. The framework leverages smart contracts to automate access control and maintain data integrity. Designed for privacy, resilience, and improved security, HealthChain surpasses conventional centralized EHR systems in both data protection and interoperability.

In their paper, Patel [29] suggests using a blockchain-based approach to ensure privacy and identity allocation in IoT devices. The paper covers the basics of the IoT and blockchains and various consensus models. The authors propose a secure identity management system that leverages public key cryptography. The paper presents an approach to preserving privacy in IoT devices utilizing blockchain technology.

Namasudra et al. [30] proposed a blockchain-based technique for ensuring the security of medical certificates in IoT healthcare. Their architecture utilizes Ethereum with a Proof of Stake algorithm and IPFS for secure storage. The system ensures privacy, performance, and security through smart contract rules, outperforming existing schemes. Future improvements will aim to address scalability and certificate updates.

Saini et al. [31] proposed a blockchain-based access control framework for the secure sharing of electronic medical records (EMRs) in smart healthcare systems. The framework employs smart contracts for user verification, access authorization, misbehavior detection, and revocation. EMRs are encrypted and stored in the cloud, and their hashes are recorded in the blockchain. An evaluation with a private Ethereum system demonstrated its efficiency in real-time healthcare scenarios.

5.4. Security and Privacy Challenges in IoT-Based Healthcare

In this section, we present papers that address challenges and concerns regarding security and privacy in IoT-based healthcare.

The implementation of security and privacy measures in IoT-based healthcare was presented by Karunarathne et al. [32]. Furthermore, they proposed solutions that could potentially mitigate such challenges. They also highlighted the importance of hybrid robust, lightweight security mechanisms, strict access control policies, and context-based confidentiality policies.

Sadek et al. [33] examine the privacy and security concerns regarding IoT sleep trackers in healthcare. The study highlights risks related to third-party apps and data migration to the cloud. Existing trackers, potential solutions, and recommendations for users and service providers are discussed. The paper contributes insights into preventing and mitigating privacy and security risks in IoT sleep trackers.

The literature review conducted on security and privacy in Healthcare 4.0 by Hathaliya et al. [20] addressed the challenges of various technologies, including tele-healthcare technology. They studied the techniques for mitigating security and privacy issues, along with their advantages and limitations. Their literature review provides valuable insights for researchers and practitioners in the healthcare industry.

The paper [34] by Edeagu et al. discusses security and privacy challenges in IoT healthcare. It emphasizes the risks posed by numerous connected smart devices and emphasizes public awareness. The paper proposes practical approaches for secure IoT design and development. Countermeasures such as Public Key Encryption, traffic filtering, and robust wireless network security are recommended to mitigate attacks and protect data.

5.5. Privacy-Preserving Schemes

In this section, we explore a variety of papers that put forth privacy-preserving schemes employing diverse techniques.

Salim et al. [35] proposed a privacy-preserving scheme using homomorphic encryption for securing medical data in the IoMT. The system ensures data integrity, privacy preservation, and secure computation on untrusted cloud servers. Key contributions include virtual node clusters, data encryption, and data confidentiality. A comparative analysis demonstrated the effectiveness of the proposed scheme in maintaining IoMT data security.

Guo et al. proposed M-PPKS [36], a privacy-preserving k-means strategy for healthcare systems using physical sensors. The strategy ensures participant confidentiality during data handling by dividing iterations into two stages: finding the nearest cluster centers and computing new centers. The approach achieved efficient clustering results while preserving privacy. The paper utilized the Paillier cryptosystem for data encryption and introduced a cloud platform to reduce the communication complexity.

Li et al. [37] proposed ADDETECTOR, a privacy-preserving system for early-stage Alzheimer’s disease detection using IoT devices. It collects user audio and employs novel linguistic features for accuracy. ADDETECTOR ensures privacy through a three-layer architecture, federated learning (FL), and differential privacy (DP) mechanisms. Experimental results showed high accuracy and easy deployment.

Sahinbas et al. [38] proposed a privacy-preserving model for IoT healthcare using federated learning and Secure Multi-Party Computation. The model enables cooperative learning while keeping training data on the device, ensuring privacy. Algorithms like FedAvg, FedBCD, and FedProx were discussed for high-performance data analysis. The proposed model enhanced privacy and data sharing in the network.

Onesimu et al. [39] proposed a privacy-preserving data collection scheme for IoT-based healthcare. Their approach utilizes a clustering-based anonymity model to prevent privacy attacks and ensures privacy on both the client and server sides. The scheme effectively addresses various disclosure and attack scenarios. The paper introduced the Discernibility Metric (DM) for evaluating the data quality, and the proposed scheme provides a comprehensive solution to privacy concerns in IoT-based healthcare systems.

Esfahani et al. [40] proposed a privacy-preserving scheme for IoT-based healthcare systems, ensuring patient location and data privacy. The scheme provides mutual authentication, patient anonymity, and authorized access to patient information. It is secure against attacks and offers additional security services with a minimal sensor overhead. Performance analysis showed improved efficiency compared to existing methods. The proposed scheme presents a comprehensive approach to preserving patient privacy in IoT healthcare systems.

Ren et al. [41] addressed privacy challenges in the IoT and proposed techniques for preserving privacy with different types of IoT data. Their methods included data stream anonymization using k-anonymity and privacy-enhancing techniques for continuous and media data. Experimental results demonstrated privacy preservation without compromising data utility. This paper offers a comprehensive overview of privacy-enhancing techniques for IoT data.

Thummisetti et al. [42] discuss how federated learning enhances healthcare by enabling privacy-preserving collaborative data analysis for disease prediction and monitoring. They emphasize the capability of the decentralized nature of federated learning to protect sensitive patient information while addressing challenges and advocating for its integration into healthcare systems.

Dhinakaran et al. [18] explore privacy-preserving techniques in IoT-based cloud systems, focusing on strategies like encryption, anonymization, and access control. They demonstrate the growing importance of AI integration in privacy preservation, highlighting the use of ML for anonymization and homomorphic encryption for secure computation. The paper advocates for using federated learning and AI-driven solutions to enhance data privacy in IoT environments.

5.6. Encryption and Security Frameworks

In this section, we delve into various papers that introduce a security framework for the healthcare industry leveraging encryption and blockchain technology.

Husnain et al. [28] propose a blockchain-based framework for securing and improving the interoperability of electronic health records (EHRs). The framework integrates AES-256 encryption and Elliptic Curve Cryptography (ECC) to protect sensitive medical data while enabling seamless data sharing across healthcare institutions. Utilizing smart contracts, HealthChain ensures automated access control and data integrity. The proposed system enhances security, privacy, and resilience, outperforming traditional centralized EHR systems in terms of data protection and interoperability.

5.7. Security Requirements in IoT-Based Healthcare Systems

The fundamental security requirements for IoT-based healthcare systems are proposed in this section.

Nasiri et al. [19] examined IoT-based healthcare systems’ security requirements. Cybersecurity and cyber resiliency were two main groups identified as requirements. Furthermore, the paper emphasizes the importance of considering both conventional and novel requirements to ensure the trustworthiness of healthcare systems based on the IoT. Also, it provides valuable insights for researchers in the IoT-based healthcare system security field.

Table 1. Distribution of papers in the literature classified according to the privacy preservation framework. This table organizes research papers according to the five core components of the privacy preservation framework for IoT-based healthcare systems and their respective subcategories: A1 (encryption techniques), A2 (anonymization), B1 (Role-Based Access Control), B2 (Attribute-Based Access Control), B3 (Consent Management Systems), C1 (Blockchain and Distributed Ledger Technologies), C2 (Secure Multi-Party Computation), C3 (federated learning), D1 (Privacy by Design Principles), D2 (transparent data usage and auditability mechanisms), E1 (User Awareness and Education Programs), E2 (user-controlled privacy settings), and E3 (Privacy-Enhancing Technologies).

Paper	A	B	C	D	E
[19]	-	-	-	D1-D2	-
[28]	A1	-	C1	D1	-
[32]	A1	B1-B2-B3	-	D1-D2	-
[29]	A1	-	C1	-	-
[33]	A1	-	-	D1-D2	E1-E2
[30]	-	-	C1	-	-
[35]	A1	-	-	D1-D2	-
[36]	A1	-	-	-	-
[20]	A1	-	-	D1-D2	-
[31]	A1	B1-B2	C1	-	-
[23]	A1	-	C1	D1-D2	-
[34]	A1	-	-	-	E1-E3
[22]	A1	-	C2	-	-
[24]	A1	-	C2	-	-
[25]	A1	-	-	D1-D2	E1-E2
[37]	-	-	C1	-	-
[21]	-	-	C1	D1	-
[26]	A1	B1-B2	-	D1-D2	-
[39]	-	-	C2-C3	-	-
[40]	A2	-	-	-	-
[41]	A2	-	-	-	-
[27]	A1-A2	-	-	D1-D2	-
[38]	A1	B2	C1	-	-
[42]	A1-A2	B2	C3	D1	E1
[18]	-	-	C3	-	E3

Table 1. Distribution of papers in the literature classified according to the privacy preservation framework. This table organizes research papers according to the five core components of the privacy preservation framework for IoT-based healthcare systems and their respective subcategories: A1 (encryption techniques), A2 (anonymization), B1 (Role-Based Access Control), B2 (Attribute-Based Access Control), B3 (Consent Management Systems), C1 (Blockchain and Distributed Ledger Technologies), C2 (Secure Multi-Party Computation), C3 (federated learning), D1 (Privacy by Design Principles), D2 (transparent data usage and auditability mechanisms), E1 (User Awareness and Education Programs), E2 (user-controlled privacy settings), and E3 (Privacy-Enhancing Technologies).

Paper	A	B	C	D	E
[19]	-	-	-	D1-D2	-
[28]	A1	-	C1	D1	-
[32]	A1	B1-B2-B3	-	D1-D2	-
[29]	A1	-	C1	-	-
[33]	A1	-	-	D1-D2	E1-E2
[30]	-	-	C1	-	-
[35]	A1	-	-	D1-D2	-
[36]	A1	-	-	-	-
[20]	A1	-	-	D1-D2	-
[31]	A1	B1-B2	C1	-	-
[23]	A1	-	C1	D1-D2	-
[34]	A1	-	-	-	E1-E3
[22]	A1	-	C2	-	-
[24]	A1	-	C2	-	-
[25]	A1	-	-	D1-D2	E1-E2
[37]	-	-	C1	-	-
[21]	-	-	C1	D1	-
[26]	A1	B1-B2	-	D1-D2	-
[39]	-	-	C2-C3	-	-
[40]	A2	-	-	-	-
[41]	A2	-	-	-	-
[27]	A1-A2	-	-	D1-D2	-
[38]	A1	B2	C1	-	-
[42]	A1-A2	B2	C3	D1	E1
[18]	-	-	C3	-	E3

6. Privacy-Preserving Architectures for Scalable IoT-Based Healthcare Systems

In this section, we aim to explore different architectures employed in IoT-based healthcare systems, identifying their role in addressing the challenges presented by the intersection of healthcare data privacy and the integration of the IoT. This dynamic environment highlights the need for privacy-preserving architectures that align with the scalability demands of these complex ecosystems.

This section outlines various architectural approaches presented in Figure 6 that demonstrate how the privacy and scalability of IoT-based healthcare systems can intersect. These approaches utilize centralized and decentralized processing, edge computing, hybrid cloud models, and zero-trust principles to address the intricate privacy challenges in connected healthcare environments.

6.1. Centralized Privacy Management

Centralized privacy-preserving architectures safeguard sensitive healthcare data through concentrated data processing and storage [43].

• Efficient Data Management: Making the handling of data more streamlined by having consistent privacy policies and access controls in place.
• Enhanced Security Measures: Implementing robust security, including advanced encryption standards and access controls.
• Streamlined Compliance: The centralized approach makes it easier to adhere to regulatory frameworks, simplifying the process of meeting compliance requirements with centralized data storage.

6.2. Decentralized Data Aggregation and Processing

Aggregating and processing data in a decentralized manner is adopted by privacy-preserving architectures to support the protection of patients’ confidentiality during data analysis. The distribution of data processing to edge devices reduces the need for centralized data while minimizing large data breaches. Furthermore, it supports the resolution of latency issues during data transmission to a central server for processing [13]. Such an approach requires a robust solution to preserve privacy at the edge. Blockchain technology plays a crucial role, as it provides a tamper-resistant and transparent platform for storing health records within decentralized networks by creating a decentralized ledger of transactions. Blockchain technology provides patients with considerable control over their medical data, prevents unauthorized data modifications, and ensures data integrity.

The decentralized nature of blockchains aligns seamlessly with the goals of decentralized data aggregation and processing, offering an additional layer of security and trust in healthcare data management. However, challenges include scalability, energy consumption, and ensuring data privacy within a public blockchain.

6.3. Edge Computing for Real-Time Privacy Preservation

The need to transmit raw data to remote servers is eliminated, as sensitive data computations can be conducted immediately on IoT devices or local gateways, benefiting from edge computing. Such computations can reduce privacy risks during data transmission and minimize data exposure. However, challenges such as optimizing computation-intensive tasks on resource-constrained devices and ensuring data integrity [17] require addressing. The following section explains how edge computing can be leveraged in maintaining the confidentiality and efficiency of data handling while protecting patient privacy.

• Advancing Privacy Preservation Through Edge Computing: Edge computing preserves patient data confidentiality by processing data closer to their source, aligning with the need for Data Protection Mechanisms [44].
• Mitigating Latency and Real-Time Analysis: By addressing real-time requirements, edge computing reduces the latency in data analysis crucial for healthcare decision-making [45].
• Addressing Resource Constraints in Edge Devices: Innovative approaches have optimized algorithms on resource-constrained edge devices, maintaining privacy without compromising performance.
• Ensuring Data Consistency and Security in Edge Environments: Strategies for ensuring data integrity and security in edge computing are connected with challenges discussed regarding Privacy-Preserving IoT Data Analytics [45].
• Decentralized Data Ownership and Control: Edge computing empowers individuals and healthcare institutions by processing data locally, aligning with the need for user-controlled privacy settings.
• Collaborative Edge Learning for Privacy-Preserving Analysis: Delving into collaborative edge learning, this approach bridges collaborative techniques and the localized processing capabilities of edge devices [46].

6.4. Hybrid Cloud Architectures for Flexibility

Hybrid cloud architectures offer a balance between utilizing cloud resources for computational power and keeping sensitive healthcare data on-premises for enhanced privacy. Healthcare institutions can maintain control over their data while benefiting from cloud scalability. Ensuring secure data transmission between on-premises infrastructure and the cloud and managing data synchronization pose challenges in this approach [13].

6.5. Zero-Trust Architectures for Enhanced Security

Zero-trust architectures require continuous verification before granting access as they treat all devices and users as potential threats. Such an approach limits unauthorized access to sensitive healthcare data, which reduces the attack surface. Implementing adaptive access controls, robust authentication mechanisms, and continuous monitoring are pivotal to the success of zero-trust architectures [47].

The privacy-preserving architectures discussed in this section align closely with the privacy-preserving framework outlined in Section 3. The centralized, decentralized, and hybrid models contribute to Data Protection Mechanisms by enforcing encryption and secure access control measures. Edge computing solutions also promote User Awareness and Empowerment by enabling localized processing while reducing exposure to external security threats. Furthermore, the use of blockchains and federated learning in decentralized architectures directly supports Privacy-Preserving Data Sharing, ensuring that patient data remain protected while facilitating collaborative analytics.

7. Secure Data-Sharing Protocols

In the dynamic world of healthcare systems based on the IoT, it is essential to guarantee that sensitive patient information is shared securely among different parties. Achieving security requires secure mechanisms that must be established based on the wealth of knowledge available in the literature focusing on privacy in healthcare, discussed in Section 5. As we dig into the technical complexities of designing robust and privacy-preserving protocols for secure data sharing, we find that these protocols address the challenges identified in the literature and integrate seamlessly with the comprehensive classification framework for privacy preservation solutions discussed in Section 3. Various techniques are explored, including secure data-sharing gateways, tokenization, and secure APIs, each contributing to the overarching goal of safeguarding patient privacy while enabling valuable collaborations.

7.1. Secure Data-Sharing Gateways

The secure data-sharing gateways discussed herein serve as pivotal intermediaries, implementing advanced encryption protocols that directly address the security requirements outlined in the literature. The emphasis on end-to-end encryption, utilizing cryptographic algorithms such as the RSA or ECC, guarantees the the confidentiality of patient data even when being transmitted across diverse stakeholders, which resonates with the need for conventional and novel security requirements to ensure the trustworthiness of IoT-based healthcare systems [48].

7.2. Tokenization for Controlled Access

Tokenization, a technique explored in our secure data-sharing protocols, by replacing sensitive data with unique tokens, aligns with the anonymization techniques discussed earlier, ensuring the protection of sensitive information during sharing. This strategic integration bolsters privacy preservation and also contributes to the overarching goal of secure and controlled data sharing [49].

7.3. Secure APIs for Controlled Interaction

Secure data-sharing gateways are crucial in the broader context of access control and authorization. These gateways act as guardians, ensuring only authorized entities traverse the healthcare data landscape. The use of secure APIs for controlled interaction aligns seamlessly with the tenets of Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), underscoring the importance of managing permissions and privileges in the evolving healthcare ecosystem [17].

Mitigating Security and Privacy Risks in Data Sharing:

• Data Integrity and Authenticity: Applying cryptographic hash functions and digital signatures ensures the authentication and guarantees the integrity of healthcare data during transmission, as it prevents unauthorized tampering or modifications by verifying that the received data are derived from a legitimate source [48].
• Access Revocation: Enabling timely and effective access revocation is a challenge in secure data sharing. If a stakeholder’s access privileges change or if a breach is detected, revoking access should be immediate and irreversible. Implementing mechanisms for instant access revocation while preserving data consistency and minimizing disruptions requires careful planning [17].
• Minimizing Data Exposure: While sharing data is essential for collaborative healthcare efforts, minimizing the exposure of sensitive information is crucial. Techniques such as data anonymization and minimal data disclosure help strike a balance between sharing insights and preserving patient privacy. Ensuring that shared data are stripped of unnecessary details without losing their analytical value poses a technical challenge [17].

Our investigation of secure data-sharing protocols shows that they fit within the classification framework for privacy preservation solutions. These protocols contribute to a considerable number of the framework’s categories, ranging from Data Protection Mechanisms, where encryption techniques play a pivotal role, to Privacy-Preserving Data Sharing, with tokenization as a cornerstone. The integration of these protocols is vital, providing a structured approach to addressing privacy concerns while guiding the design and implementation of secure IoT-based healthcare systems. In conclusion, the technical landscape of secure data-sharing protocols is intricate and essential for privacy preservation in IoT-based healthcare systems. By leveraging secure data-sharing gateways, tokenization, and secure APIs, healthcare stakeholders can effectively collaborate while ensuring patient privacy. Overcoming the data integrity, access revocation, and data exposure challenges requires innovative approaches and a holistic understanding of privacy and data utility considerations.

8. Privacy-Preserving Patient Monitoring

Regarding the healthcare revolution driven by the IoT, which includes wearable devices for continuous patient monitoring, this section focuses on the technical aspects of preserving patient privacy during monitoring. It discusses the benefits of health tracking and monitoring while exploring the safeguarding of patient confidentiality through the use of various technologies, such as encryption, secure data aggregation, and anomaly detection.

8.1. Encrypted Health Data Streams

Regarding the pursuit of privacy preservation, this section underscores the pivotal significance of encrypting health data streams originating from wearable IoT devices. Robust encryption protocols, exemplified by homomorphic encryption, ensure the confidentiality of health information. This approach aligns seamlessly with Secure Data Encryption and Transmission, extending the discourse to accommodate the challenges of implementing encryption in resource-constrained wearable devices [16].

8.2. Secure Data Aggregation Techniques

Secure data aggregation techniques come into focus when navigating the intricacies of privacy and insight extraction. Aggregating encrypted data at the device level or edge nodes aligns with the principles of Privacy-Preserving Data Sharing, emphasizing the importance of minimizing the exposure of raw patient information. The challenges of optimizing real-time processing and preventing information leakage during aggregation are addressed in tandem [16].

8.3. Anomaly Detection with Privacy Focus

As the narrative unfolds, the significance of anomaly detection in ensuring the prompt identification of potential health issues takes center stage. Leveraging differential privacy as a tool for anomaly detection introduces a layer of sophistication, emphasizing the delicate balance between controlled noise and accurate anomaly detection. The challenges with maintaining accuracy and utility while ensuring privacy are explored, connecting seamlessly with Differential Privacy-Aware ML [50].

8.4. Addressing Challenges: The Balancing Act

Privacy-preserving patient monitoring plays a crucial role in ensuring both data security and patient well-being. As discussed in this section, encrypted health data streams strengthen Data Protection Mechanisms, while secure aggregation techniques enhance Privacy-Preserving Data Sharing. By integrating anomaly detection methods with differential privacy, these systems also support User Awareness and Empowerment, allowing patients and healthcare providers to receive critical alerts without unnecessary data exposure.

This approach maintains a delicate balance, ensuring that healthcare systems provide accurate and meaningful insights while respecting patient privacy. Wearable IoT devices, though powerful, face challenges like limited resources and the need for real-time responsiveness, making edge computing for privacy preservation a vital component.

Ultimately, the success of privacy-preserving patient monitoring depends on seamlessly blending encryption, secure data aggregation, and intelligent anomaly detection. This combination not only enables continuous, real-time monitoring but also ensures that privacy is never compromised. Addressing challenges like data accuracy, system efficiency, and processing constraints requires a thoughtful, patient-centered approach that aligns with the evolving landscape of digital healthcare.

9. Integration of Privacy-Preserving IoT Devices

In healthcare, the propagation of IoT devices that preserve patients’ privacy makes it crucial to integrate such devices into the existing infrastructure. This section covers the complex technical considerations involved in the integration process, addressing challenges related to device interoperability, secure provisioning, and the equilibrium between maintaining patient privacy and ensuring robust data security.

9.1. Secure Device Provisioning

This section explores the crucial process of secure device provisioning, stressing the significance of thwarting unauthorized access and potential security breaches. Techniques like device attestation and secure bootstrapping emerge as stalwarts in establishing the authenticity and integrity of IoT devices. The narrative delicately weaves through the challenges of secure provisioning, resonating with the themes explored regarding Secure Interoperability and interconnectivity [51].

9.2. Navigating Privacy-Preserving Updates

The focus turns to the importance of regular software updates in maintaining the security of devices and addressing vulnerabilities. Updating IoT devices while preserving privacy presents challenges addressed through techniques like secure over-the-air (OTA) updates. The balance between the need for updates and the commitment to privacy protection highlights the challenges described in the consideration of scalability and performance [52].

9.3. Addressing Challenges: The Symphony of Integration

A heterogeneous ecosystem, characterized by diverse devices and technologies, necessitates a thoughtful consideration of compatibility, data formats, and communication protocols. The delicate dance between security and privacy unfolds as a central theme, requiring a holistic approach to ensure that security mechanisms fortify rather than compromise patient data privacy. Problems with scalability also need to be resolved, emphasizing the paramount importance of designing solutions that can handle a growing number of devices while preserving privacy [51].

The integration of privacy-preserving IoT devices into existing healthcare infrastructure emerges as a pivotal point in the redefinition of patient care. Collaborative efforts among device manufacturers, healthcare institutions, and regulatory bodies are essential for success. The technical prowess demanded in this integration journey serves as a testament to the dynamic intersection of technology and healthcare ethics, where patient privacy stands as a cornerstone.

10. Open Issues and Future Directions

The promise of IoT-based healthcare systems comes with significant privacy concerns. While this study has explored various approaches, several critical challenges remain, presenting exciting opportunities for further development. This section outlines key areas that need further exploration to strengthen patient privacy as the foundation of these systems.

10.1. Bridging the Gap Between Privacy and Utility

• Differential Privacy and Federated Learning:
  While these techniques offer promising avenues for secure data analysis, further research is crucial on practical challenges in deployment and exploring strategies for adapting differential privacy to smaller datasets. Addressing federated learning scalability issues with multiple healthcare providers and exploring advanced aggregation and encryption methods specifically tailored to sensitive health data [53] can unlock their full potential without compromising patient anonymity.
• Homomorphic Encryption and Secure Multi-Party Computation: Further, real-world use cases should be elaborated to illustrate the potential of these techniques. Future work should investigate how homomorphic encryption can be optimized for low-power healthcare IoT devices and consider hybrid approaches that balance computation between trusted and untrusted nodes to reduce bottlenecks [54].

10.2. Building a Robust and Interoperable Ecosystem

Interoperability Standards: There is a need to provide more specific examples of challenges in implementing interoperability standards, like difficulty in unifying data formats across different healthcare systems. Practical steps should be suggested to promote the adoption of standards, such as incentives or regulations, and examples of successful cross-border interoperability initiatives should be highlighted [55].

10.3. Blockchain-Based Solutions

Future research should expand on the potential of blockchain technology by exploring its use in decentralized healthcare applications, detailing how it can improve patient consent management or clinical trial data sharing. It should also discuss the challenges in balancing transparency with patient privacy in a public ledger [56].

10.4. Empowering Patients with Control and Transparency

• Context-Aware and Adaptive Privacy Mechanisms: Future research should highlight the need for continuous user education about privacy risks and protections. It should explore how AI could dynamically adjust privacy settings based on patient preferences, locations, and real-time conditions. This should be illustrated with real-world scenarios where such adjustments would be beneficial [57].
• User-Friendly Privacy Controls and Educational Tools: Complex interfaces hinder informed consent and effective data management. Research should prioritize developing intuitive privacy controls and engaging educational tools that clearly explain data usage, protection mechanisms, and user options. This will foster trust and cultivate a culture of privacy-conscious engagement [58].

10.5. Fostering a Culture of Privacy Awareness

Privacy Education and Awareness: Educating both healthcare professionals and patients about the nuances of privacy in IoT-based systems is vital. Research should inform the development of effective educational programs and awareness campaigns that address specific concerns and promote responsible data practices within the healthcare community [59].

The ability to uphold patient privacy as a non-negotiable right while benefiting from the enormous potential of IoT-based healthcare systems can be preserved and conserved by addressing these open issues and actively working toward these future directions.

11. Conclusions

Ensuring patient privacy in IoT-based healthcare systems is a critical challenge that requires balancing security, usability, and compliance with regulatory standards. This review offers a structured framework for privacy preservation, focusing on data protection, secure access control, privacy-aware data sharing, governance policies, and user empowerment. We explored various privacy-preserving techniques, including encryption, federated learning, and secure data-sharing protocols, to mitigate risks associated with IoT-based healthcare data.

As healthcare increasingly adopts the IoT, implementing strong privacy mechanisms is essential for maintaining trust and security. Future research should focus on optimizing privacy-preserving solutions for real-world deployment, addressing computational constraints, and enhancing user-centric privacy controls.