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Applied Sciences
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Cryptography in Data Protection and Privacy-Enhancing Technologies
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Cryptography in Data Protection and Privacy-Enhancing Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2001

Special Issue Editors

Dr. Yi-Fan Tseng

E-Mail Website
Guest Editor
Department of Computer Science, National Chenchi University, Taipei 11605, Taiwan
Interests: public key cryptography; provable security; cryptographic protocol
Prof. Dr. Chun-I Fan

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
Interests: applied cryptology; information and communication security; AI security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the age of rapid technological advancement, protecting personal information is of paramount importance. Privacy-Enhancing Technologies (PETs) stand as the forefront guardians, embodying fundamental data protection principles. By minimizing personal data use, maximizing data security, and empowering individuals, PETs play a pivotal role in preserving privacy in our interconnected world. These technologies empower online users to safeguard the privacy of their personally identifiable information (PII) during interactions with various services and applications, utilizing innovative techniques to reduce the possession of personal data by information systems without compromising functionality.

This Special Issue delves into the multifaceted realm of Privacy-Enhancing Technologies, exploring their impact on the digital landscape. Topics of interest include, but are not limited to, the following:

  • Cryptographic protocols for secure data transmission;
  • Encryption algorithms and techniques in privacy protection;
  • Blockchain technology for enhancing data privacy;
  • Authentication and verification methods for secure access;
  • Digital signatures in ensuring data integrity and authenticity;
  • Multiparty computation.

Dr. Yi-Fan Tseng
Prof. Dr. Chun-I Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • privacy-enhancing technologies (PETs)
  • data protection
  • cryptographic protocols
  • multiparty computation
  • digital signatures
  • blockchain technology

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Published Papers (2 papers)

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Research

20 pages, 1301 KiB  
Article
A Reliable Aggregation Method Based on Threshold Additive Secret Sharing in Federated Learning with Quality of Service (QoS) Support
by Yu-Ting Ting, Po-Wen Chi and Chien-Ting Kuo
Appl. Sci. 2024, 14(19), 8959; https://doi.org/10.3390/app14198959 - 4 Oct 2024
Viewed by 485
Abstract
Federated learning is a decentralized privacy-preserving mechanism that allows multiple clients to collaborate without exchanging their datasets. Instead, they jointly train a model by uploading their own gradients. However, recent research has shown that attackers can use clients’ gradients to reconstruct the original [...] Read more.
Federated learning is a decentralized privacy-preserving mechanism that allows multiple clients to collaborate without exchanging their datasets. Instead, they jointly train a model by uploading their own gradients. However, recent research has shown that attackers can use clients’ gradients to reconstruct the original training data, compromising the security of federated learning. Thus, there has been an increasing number of studies aiming to protect gradients using different techniques. One common technique is secret sharing. However, it has been shown in previous research that when using secret sharing to protect gradient privacy, the original gradient cannot be reconstructed when one share is lost or a server is damaged, causing federated learning to be interrupted. In this paper, we propose an approach that involves using additive secret sharing for federated learning gradient aggregation, making it difficult for attackers to easily access clients’ original gradients. Additionally, our proposed method ensures that any server damage or loss of gradient shares are unlikely to impact the federated learning operation, within a certain probability. We also added a membership level system, allowing members of varying levels to ultimately obtain models with different accuracy levels. Full article
(This article belongs to the Special Issue Cryptography in Data Protection and Privacy-Enhancing Technologies)
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15 pages, 438 KiB  
Article
Protecting Instant Messaging Notifications against Physical Attacks: A Novel Instant Messaging Notification Protocol Based on Signal Protocol
by Raghad Almari, Abdullah Almosallam, Saleh Almousa and Saad Alahmadi
Appl. Sci. 2024, 14(14), 6348; https://doi.org/10.3390/app14146348 - 21 Jul 2024
Viewed by 1017
Abstract
Over the years, there has been a significant surge in the popularity of instant messaging applications (IMAs). However, the message notification functionality in IMAs exhibits certain limitations. Some IMAs fail to alert users about new messages after their phone restarts unless they unlock [...] Read more.
Over the years, there has been a significant surge in the popularity of instant messaging applications (IMAs). However, the message notification functionality in IMAs exhibits certain limitations. Some IMAs fail to alert users about new messages after their phone restarts unless they unlock the phone. This is a consequence of end-to-end encryption (E2EE) and the app not knowing the message is in the queue until the app decrypts it. This approach using E2EE is used to prevent offline attacks, as the key is unavailable to decrypt the notification messages. In this paper, we introduce a novel design and implementation of a message notification protocol for IMAs based on the Signal protocol. The proposed protocol aims to securely display notifications on a locked device and ensures that cryptographic keys are stored in a location that is isolated from the user’s device to prevent offline attacks. This approach enhances the security of private key storage, safeguarding private keys against various external threats. The innovative design strengthens the off-site key management system, rendering it resilient against offline attacks and mitigating the risk of key compromise. Additionally, the proposed protocol is highly efficient, requiring no specialized hardware for implementation. It offers confidentiality of cryptographic keys and protection against offline attacks, further enhancing the overall security of the system. We evaluate the protocol’s effectiveness by analyzing multiple independent implementations that pass a suite of formal tests via ProVerif. Full article
(This article belongs to the Special Issue Cryptography in Data Protection and Privacy-Enhancing Technologies)
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