SCOPChain: A Blockchain Based Privacy Preserving Framework for Data Service Composition

Author:

khemaissia Rofaida1,Derdour Makhlouf2,Ferrag Mohamed Amine3,Bouhamed Mohammed Mounir4

Affiliation:

1. Laboratory of Mathematics, Informatics and Systems (LAMIS), Echahid Cheikh Larbi Tebessi University, Tebessa

2. Larbi Ben M’Hidi University Oum El Bouaghi, Algeria

3. 8 Mai 1945 University Guelma, Algeria

4. Echahid Hamma Lakhdar University, El-Oued, Algeria

Abstract

Abstract Service composition is combining multiple services to provide for user query a new service which uses data from multiple service providers that are incorporated in the composition. In this situation, the data privacy and especially of the service providers can be breached. Therefore, keeping the data privacy during the composition process is crucial by every work in the context of the service composition. Recent approaches rely on a central mediator that can be trusted or not to ensuring the privacy of the service providers during the query execution. The most recent approaches found problems in case of untrusted mediator where they enforce restrictions that can affect the efficiency of their works. Therefore, we propose SCOPChain which preserves the privacy of data service providers during service composition using BlockChain technology. We used a permissioned BlockChain that acts as trusted mediator where it enables users to access to the BC if they get administrator permission. We use a BC framework called Hyperledger Fabric to implement our solution where it stores sensitive data about the composition whereat intermediate query results are saved in IPFS that acts as offchain storage. As a proof of concept, we have tested SCOPChain on a real-world medical dataset to show its feasibility and efficiency for maintaining privacy in a secure and trusted manner.

Publisher

Research Square Platform LLC

Reference30 articles.

1. K. R. Boeckl, N. B. Lefkovitz, et al., NIST Privacy Framework: A Tool for Improving Privacy through Enterprise Risk Management, Version (2020).

2. ``Privacy-preserving machine learning with fully homomorphic encryption for deep neural network;Lee J-W;''IEEE Access,2022

3. Efficient privacy-preserving user authentication scheme with forward secrecy for industry 4.0;Wang C;Sci. China Inf. Sci.,2022

4. C. Zhang, M. Zhao, L. Zhu, W. Zhang, T. Wu and J. Ni, "FRUIT: A Blockchain-Based Efficient and Privacy-Preserving Quality-Aware Incentive Scheme," in IEEE Journal on Selected Areas in Communications, vol. 40, no. 12, pp. 3343–3357, Dec. 2022, doi: 10.1109/JSAC.2022.3213341.

5. J. Song, W. Wang, T. R. Gadekallu, J. Cao and Y. Liu, "EPPDA: An Efficient Privacy-Preserving Data Aggregation Federated Learning Scheme," in IEEE Transactions on Network Science and Engineering, doi: 10.1109/TNSE.2022.3153519.

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