Blockchain-Enabled Secure Data Sharing with Honey Encryption and DSNN-Based Key Generation
Abstract
:1. Introduction
- We design an algorithm called IABHE+DSNN for securing a multi-party data-sharing system. It was developed to securely share the data via MapReduce framework a multi-party data-sharing system.
- In this work, data encryption is executed using the IABHE model, and a secret key is developed for data protection using the DSNN model.
- We use blockchain technology for storing and accessing data. This approach integrates data protection and transparency of data transactions.
2. Related Work
Challenges
- The PRE + TEE technique used in [3] significantly reduced the reduplicative data as well as the ciphertexts, but it failed to consider computational complexity problems that occur while sharing data securely in data-driven systems. The complexity problems are evaluated by generating the key using DSNN.
- The Medi-Block record scheme utilized in [7] attained less storage overhead while providing a secure platform for medical data sharing. However, third-party storage services are required to share medical data securely. The hashing parameters and interpolation used in the proposed research ensure the security of data sharing.
- The MA-RABE method employed in [10] was highly effective and robust in providing security for multi-party data sharing, but it failed to decrypt local models even after obtaining the encryption and decryption keys of participants. The encryption and decryption algorithms enhanced in the proposed research ensure security in multi-party data sharing.
- The BSKM model used in [12] effectively stored the data for differential transactions and provided reliable data communication. Meanwhile, it recorded a high time for writing and updating transactions in objects while enhancing the privacy and confidentiality of data. The evaluation metrics that evaluated the encryption and decryption times show the confidentiality of the data.
3. The Proposed Methodology
3.1. System Model for Blockchain-Based Multi-Party Data Sharing
3.1.1. Data Owner
3.1.2. Data Requester
3.1.3. IPFS
3.1.4. Encryption Server
3.1.5. MPA
3.2. The Proposed Map-Based IABHE+DSNN
- Blockchain Network: In this system, the blockchain network acts as the central controller and orchestrates the interactions among the various components, including the subcomponents in both the blockchain-based multi-party network and the MapReduce framework. It is responsible for managing the overall workflow and system phases, such as initialization, registration, authentication, data sharing, validation, and data protection.
- IPFS: Connects with IMPEncryption Server to store encrypted data and interacts with the data requester to provide stored encrypted data.
- DataOwner: Uses the MPEncryptionServer to encrypt data. Stores encrypted data in IPFS and shares data through a blockchain network.
- DataRequester: Requests data through BlockchainNetwork. Retrieves encrypted data from IPFS and uses IMPEncryptionServer to decrypt data.
- IMPEncryption Server: Performs encryption and decryption using keys generated by DSNN and works with MapReduce Framework to handle large-scale data encryption tasks.
- MapReduce Framework: Utilizes IABHE for encryption operations and depends on DSNN for key generation.
- IABHE: It encrypts data within the MapReduce framework and generates the necessary encryption keys.
- DSNN: Generates secret keys for encryption and decryption.
- System Phases: Encapsulates the phases managed by the blockchain network and Coordinates activities across the entire system.
3.2.1. System Initialization
3.2.2. Registration
- (a)
- Registration between the data owner and MPA: The generated message is sent to the data owner, and this generated message is stored in the data owner as ∼. Finally, this stored message in the data owner ∼ is again forwarded to MPA and stored for registration. The data owner is registered in MPA if the created message is equal to the stored message in data owner ∼. Figure 4 shows the registration of the system among the data owner and MPA.
- (b)
- Registration between data requester and MPA: The registration among the data requester and MPA is performed by initially passing the data requester ID and password to MPA, where it is stored as and . The MPA forwards a copy of these data to the IPFS, and the details are stored as and in IPFS. Then, a message i is generated in MPA by XOR-ing the hashed value of stored data requester password concatenated with security parameter B along with the public key of the data owner , which is given by,Later, the created message is fed to the data requester and stored in the data requester, and this stored message is forwarded to MPA for registration. If the created message is equal to the stored message in the data requester, then the data requester is registered with the MPA. Figure 5 depicts the process executed during registration among the data requester and MPA.
3.2.3. Authentication
- (a)
- Authentication between the data owner and MPA: The authentication request message is generated by the data owner during authentication between the data owner and MPA. The message is created by XOR-ing the hashed value of random number concatenated with the recorded data owner public key concatenated with the modulus of the security parameter A along with the hashed value of data owner password . Thus, the authentication request message generated is given as,The generated authentication request in the data owner is forwarded to MPA, and MPA generates the message using the credentials available with it. The message is obtained by XOR-ing the hashed value of random number u and concatenating it with data owner public key concatenated with the modulus of the security parameter A and hashing the value of stored data owner password . Thus, the generated message is designated as,The data owner is verified in MPA if the authentication request message in the data owner is the same as the recorded message in MPA. Then, an OTT is generated in MPA to authenticate with the data owner. The OTT is created by XOR-ing the hashed value of recorded data owner ID and data owner public key , which is expressed asThen, the generated OTT in MPA is passed to the data owner and stored as , where is generated by XOR-ing the hashed value of data owner ID and security parameter along with the data owner public key and is given by,The generated by the data owner is forwarded to MPA for authentication. If the created OTT in MPA is equal to the generated in the data owner, then the data owner is authenticated in MPA. Moreover, the process performed during authentication among the data owner and MPA is displayed in Figure 6.
- (b)
- Authentication between the data requester and MPA: An authentication request message is generated by the data requester for authentication between the data requester and MPA. Here, it is created by XOR-ing the hashed value of the data requester ID and concatenated with a random number along with the hashed value of the data requester password concatenated with a security parameter. The generated authentication message is expressed as,Then, the message created by the data requester is sent to MPA for verification. On receiving the authentication message, the message is created by the MPA by XOR-ing the hashed value of the data requester ID, concatenating and concatenated with a random number hashing the value of the data requester password, and then concatenating with a security parameter. The created message is expressed asMoreover, the data requester is verified to see if the authentication message created by the data requester is equal to the generated message in MPA. Later, an is generated in MPA by hashing the XOR-ed value of the data requester password and random number, which is expressed asThe data generated in MPA is sent to the data requester and recorded, and the recorded data requester is again passed to MPA for verification. Here, the data requester is authenticated with MPA if the stored data are the same as the recorded data. Figure 7 displays the authentication performed among the data requester and MPA.
3.2.4. Key Generation Using DSNN
- CNN: CNN is a multi-layer supervised learning neural network that mainly comprises two core models, such as convolution and pooling layers, for feature extraction from the input data. The convolution and pooling layers are adjusted to reduce the dimension of the data and to extract abstract features from the data. Thus, the resultant data from CNN is fed into SNN for the generation of a secret key for the encryption task.
- Integrate-and-fire neuron model: The DSNN utilizes a simple integrate-and-fire neuron model for key-generation task.The integrate-and-fire neuron dynamics are expressed by,Here, the Dirac function is represented as , the synapse weights of input neuron are indicated as , the spike time series is denoted as , and Q resembles time.
- Convolution operation: The convolution operation carried out in DSNN is the same as of CNN, which is designated as,
- Max pooling operation: The max pooling operation in DSNN cannot be performed using a simple maximum value operation due to the transmission of discrete spike signals by DSNN. Thus, global average pooling is performed in DSNN to perform the max pooling operation.
- Weight normalization: In the DSNN model, the fire rate of spiking neurons is ensured by multiplying the spike neuron weights using weight normalization. The weight normalization coefficient is selected using a robust normalization method. Here, the spiking activation is performed to measure the weight normalization coefficient.
- Realization of biases: The data range is updated by adding biases to the network, where the constantly released spike signals by the neurons are utilized as biases. In general, the weights of connections among neurons are considered to change the bias values, and this process is repeated to stimulate biases.
3.2.5. Data Encryption Using IABHE
- Initialization: The process begins with initializing the system and setting up the necessary parameters for data encryption and key generation. This step prepares the algorithm for secure data-sharing operations.
- Authentication: Authentication involves verifying the identities of the parties involved in the data-sharing process. This step ensures that only authorized entities can access and share data securely.
- Initial Registration: During initial registration, entities are registered within the system and provided with the necessary credentials for data access and sharing. This step establishes the foundation for secure data sharing among multiple parties.
- Data Protection: Data protection is a crucial step where sensitive information is encrypted using the IABHE. This encryption process ensures that data remain confidential and secure during storage and transmission.
- Validation: Validation involves verifying the integrity and authenticity of the encrypted data to prevent unauthorized access or tampering. This step ensures that only authorized parties can decrypt and access the shared data.
- Data Sharing: The final step in the algorithm is data sharing, where encrypted data are securely transmitted between authorized parties using the generated keys from the DSNN. This step facilitates the secure and efficient sharing of sensitive information among multiple entities.
Algorithm 1 Mapper-Based IABHE Algorithm |
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3.2.6. Validation and Data-Sharing Phase
3.3. Data Decryption
- Sample Data:Original Plaintext: “Sensitive Data Example”
- Encryption Process:Encryption Key: A1B2C3D4E5F6G7H8Generated Ciphertext: f8h29jkwe823jhds8k
- Decryption Process:Decryption Key: A1B2C3D4E5F6G7H8Recovered Plaintext: “Sensitive Data Example”
4. Results and Discussion
4.1. Experimental Setup
- Dataset description:The database considered for blockchain-based multi-party data sharing is taken from the Skin Segmentation database [42] and Localization Data for Person Activity database [43].
- (i)
- Skin Segmentation database: The Skin Segmentation database given in [42] comprises about 245,057 learning samples, where 50,859 samples are skin samples and 194,198 are non-skin samples. Moreover, the data are collected randomly from B, G, and R values from different genders, race groups, and age groups.
- (ii)
- Localization Data for Person Activity database: Data for Person Activity database: The Localization Data for Person Activity database [43] possesses the various activities of five people, and the data are recorded from four tags, such as chest, belt, ankle right, and ankle left. Moreover, the tags are determined by one of the attributes.
- Evaluation measures:The different parameters, like key complexity, decryption time, and encryption time, are utilized to identify the performance of the IABHE+DSNN approach used for secure data sharing, and the parameters are demonstrated below:
- (a)
- Key complexity The security level offered by the cryptographic algorithm is termed key complexity and is expressed as
- (b)
- Decryption TimeThe decryption time is the time utilized to convert the cipher text into normal plaintext.
- (c)
- Encryption TimeThe time utilized to convert normal plaintext into cipher text is termed encryption time.
- Comparative Model:The baseline data-sharing approaches, such as PRE + TEE [3], Medi-Block record [7], MA-RABE [10], and BSKM [12] are utilized to identify the performance of the IABHE+DSNN model designed for secure data-sharing. The comparative analysis was conducted using the Python platform, with the Skin Segmentation database and the Localization Data for Person Activity database. This comparative analysis helps to identify and highlight the improvements in key performance metrics such as key complexity, encryption, and decryption efficiency achieved by our proposed system.
4.2. Comparative Analysis/Validation
4.2.1. Validation Using Skin Segmentation Database
- For data size of 100 KBThe validation of the designed IABHE+DSNN for secure data sharing with varying key sizes using the Skin Segmentation database for 100 KB of data is illustrated in Figure 12. The comparative analysis of key complexity between the proposed IABHE+DSNN and other data-sharing approaches is shown in Figure 12a. Baseline approaches like PRE + TEE, Medi-Block record, MA-RABE, and BSKM measured key complexities of 0.708, 0.747, 0.808, and 0.837, respectively, for a key size of 256 bits, whereas IABHE+DSNN achieved a maximum key complexity of 0.887.The investigation of different data-sharing approaches using decryption time is presented in Figure 12b. The IABHE+DSNN recorded a minimum decryption time of 7.987 s, while other traditional approaches recorded decryption times of 20.867 s for PRE + TEE, 17.876 s for Medi-Block record, 12.978 s for MA-RABE, and 10.876 s for BSKM, all for a key size of 256 bits.Moreover, the validation of various data-sharing approaches in terms of encryption time is displayed in Figure 12c. The IABHE+DSNN achieved a minimum encryption time of 15.876 s for a key size of 256 bits. In comparison, the encryption times recorded by prevailing approaches were 23.978 s for PRE + TEE, 21.977 s for Medi-Block record, 20.765 s for MA-RABE, and 18.877 s for BSKM.
- For data size of 200 KBFigure 13 shows the validation of IABHE+DSNN designed in this research for secure data sharing while utilizing a Segmentation database for 200 KB and by varying key sizes. The comparative analysis of the proposed IABHE+DSNN with other secured data-sharing approaches in terms of key complexity is given in Figure 13a. The IABHE+DSNN obtained a key complexity of 0.575, whereas the baseline secure data-sharing models measured key complexity is 0.398 by PRE + TEE, 0.437 by Medi-Block record, 0.497 by MA-RABE, and 0.536 by BSKM for a key size of 256 Bits. The analysis of different secure data-sharing models using decryption time is given in Figure 13b. The IABHE+DSNN measured minimum decryption time of 17.868 s for a key size of 256 bits, and the decryption time obtained by other prevailing approaches like PRE + TEE is 32.866 s, Medi-Block is 29.867 s, MA-RABE is 25.976 s, and BSKM is 21.978 s. The validation of various data-sharing models utilizing encryption time is given in Figure 13c. The prevailing data-sharing models, such as PRE + TEE, Medi-Block record, MA-RABE, and BSKM, obtained encryption time of 41.786 s, 37.786 s, 33.978 s, and 31.876 s for a key size of 256 Bits, whereas IABHE+DSNN recorded minimum encryption time of 28.876 s.
4.2.2. Validation Using Localization Data for Person Activity Database
- For data size of 100 KBThe evaluation of IABHE+DSNN used to securely share data while utilizing Localization Data for Person Activity database by differing key sizes for 100 KB data is given in Figure 14. The analysis of various secure data-sharing techniques by employing key complexity is given in Figure 14a. The IABHE+DSNN recorded a maximum key complexity of 0.887, and the key complexity measured for key size of 256 Bits by baseline approaches, such as PRE + TEE is 0.708, Medi-Block is 0.747, MA-RABE is 0.808, and BSKM is 0.837. The comparative validation of data-sharing techniques by means of decryption time is depicted in Figure 14b. The IABHE+DSNN recorded a decryption time of 10.786 s, whereas the decryption time obtained by traditional models for a key size of 256 Bits is 19.876 s by PRE + TEE, 17.876 s by Medi-Block record, 15.876 s by MA-RABE, and 13.735 s by BSKM. In addition, Figure 14c displays the analysis of different data-sharing models by utilizing encryption time. Here, the encryption time recorded by prevailing data-sharing schemes, like PRE + TEE, Medi-Block record, MA-RABE, and BSKM, is 31.876 s, 28.866 s, 22.867 s, and 18.765 s. Similarly, the designed IABHE+DSNN approach obtained an encryption time of 15.765 s, which is less than other existing data-sharing techniques for a key size of 256 bits.
- For data size of 200 KBFigure 15 displays the validation of the developed IABHE+DSNN approach with the Localization Data for Person Activity dataset for secure data sharing by varying key sizes. Figure 15a depicts the evaluation of different data-sharing models utilized in the MPA system by means of key complexity. The key complexity obtained by designed IABHE+DSNN is 0.876 for a key size of 256 bits, and the key complexity of 0.708, 0.747, 0.808, and 0.837 is recorded by prevailing secure data-sharing approaches, such as PRE + TEE, Medi-Block record, MA-RABE, and BSKM. Moreover, the validation of data-sharing approaches utilizing decryption time is given in Figure 15b. Here, the decryption time obtained by traditional models for key size of 256 Bits is 27.976 s by PRE + TEE, 22.987 s by Medi-Block record, 19.767 s by MA-RABE, and 17.987 s by BSKM, whereas the designed IABHE+DSNN approach measured minimum decryption time of 13.866 s. The validation of various secure data-sharing schemes using encryption time is displayed in Figure 15c. The encryption time recorded by existing data-sharing techniques, such as PRE + TEE, Medi-Block record, MA-RABE, and BSKM, for key sizes of 256 Bits is 55.987 s, 22.987 s, 19.767 s, and 17.987 s. On the other hand, the IABHE+DSNN technique recorded minimum encryption time of 36.765 s.
4.3. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Authors & Reference | Methodology | Advantages | Limitations/Disadvantages |
---|---|---|---|
Wang et al. [3] | Security enhancements: Blockchain, PRE, TEEs | Robust security measures, safeguarding sensitive data | Lack of data visual validation techniques; High computational and storage overhead |
Singh et al. [7] | Medi-Block record: Secure data sharing using blockchain technology. Utilizing blockchain-based authentication, bilinear mapping | Tamper-proof and anonymous identity management | Storage burden due to redundant data copies; Scalability issues in large networks |
Yin et al. [9] | A blockchain-based collaborative training method for multi-party data sharing. Employing blockchain-based collaborative training, distributed ledger | Prototype system to analyze and evaluate time consumption | Limited success in enhancing data security; High latency in data processing |
Chen et al. [10] | Industrial chain data sharing: Blockchain, Big Data Technology | Enhanced data sharing and circulation efficiency | Integration complexity, need for standardization; Difficult implementation |
Bakir et al. [12] | New Blockchain-Based Special Keys Security Model With Path Compression Algorithm for Big Data. Utilizing a special key security model, path compression algorithm | Ensures data confidentiality, integrity, and consistency | Need for efficient public auditing techniques; High implementation cost |
Alhazmi et al. [14] | Towards big data security framework by leveraging fragmentation and blockchain technology. Employing fragmentation techniques, lightweight metadata structure | Eliminates the need for third-party auditing | Complexity of implementation; Fragmentation may impact performance |
Qin et al. [15] | Big data security architecture: Blockchain, Trusted Data Cloud Center | Enhanced security, integrity, and transparency of data | Potential scalability issues, complexity; High initial setup and maintenance costs |
Battah et al. [17] | MPA: Blockchain-based authorization, IPFS Encryption | Secure multi-party data access, decentralized storage | Latency in data retrieval; Limited network performance |
Rath et al. [38] | Secure Outsourcing: Parallel processing, secure cloud outsourcing | Efficient computation for IoT, reduced processing load | Security risks in outsourcing; Dependence on cloud services |
Caldarola et al. [39] | Neural fairness protocol: Neural fairness blockchain protocol, elliptic curves lottery | Ensures fairness in transactions, enhanced security | Computational overhead; Complex implementation |
Proposed System IABHE+DSNN | Data protection within the MapReduce framework, data encryption using IABHE, key generation managed by DSNN. | Enhanced data security through hybrid key-generation approach | Research will focus on addressing additional security concerns and analyzing systems within large network behavior |
Size of Data | Evaluation Parameters | PRE + TEE | Medi-Block Record | MA-RABE | BSKM | Proposed IABHE+DSNN |
---|---|---|---|---|---|---|
Using skin Segmentation database | ||||||
100 KB | Key complexity | 0.408 | 0.476 | 0.545 | 0.597 | 0.657 |
Decryption time (s) | 20.867 | 17.876 | 12.978 | 10.876 | 7.987 | |
Encryption time (s) | 23.978 | 21.977 | 20.765 | 18.877 | 15.876 | |
200 KB | Key complexity | 0.398 | 0.437 | 0.497 | 0.536 | 0.575 |
Decryption time (s) | 32.866 | 29.867 | 25.976 | 21.978 | 17.867 | |
Encryption time (s) | 41.786 | 37.786 | 33.978 | 31.876 | 28.876 | |
Using Localization Data for Person Activity database | ||||||
100 KB | Key complexity | 0.708 | 0.747 | 0.808 | 0.837 | 0.887 |
Decryption time (s) | 19.876 | 17.876 | 15.876 | 13.765 | 10.786 | |
Encryption time (s) | 31.876 | 28.866 | 22.867 | 18.765 | 15.765 | |
200 KB | Key complexity | 0.754 | 0.797 | 0.826 | 0.857 | 0.876 |
Decryption time (s) | 27.976 | 22.987 | 19.767 | 17.987 | 13.866 | |
Encryption time (s) | 55.987 | 49.865 | 45.987 | 39.867 | 36.765 |
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Siyal, R.; Long, J.; Asim, M.; Ahmad, N.; Fathi, H.; Alshinwan, M. Blockchain-Enabled Secure Data Sharing with Honey Encryption and DSNN-Based Key Generation. Mathematics 2024, 12, 1956. https://doi.org/10.3390/math12131956
Siyal R, Long J, Asim M, Ahmad N, Fathi H, Alshinwan M. Blockchain-Enabled Secure Data Sharing with Honey Encryption and DSNN-Based Key Generation. Mathematics. 2024; 12(13):1956. https://doi.org/10.3390/math12131956
Chicago/Turabian StyleSiyal, Reshma, Jun Long, Muhammad Asim, Naveed Ahmad, Hanaa Fathi, and Mohammad Alshinwan. 2024. "Blockchain-Enabled Secure Data Sharing with Honey Encryption and DSNN-Based Key Generation" Mathematics 12, no. 13: 1956. https://doi.org/10.3390/math12131956