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Authenticable data analytics over encrypted data in the cloud
Journal article   Peer reviewed

Authenticable data analytics over encrypted data in the cloud

Lanxiang Chen, Yi Mu, Lingfang Zeng, Fatemeh Rezaeibagha and Robert H. Deng
IEEE transactions on information forensics and security, Vol.18, pp.1800-1813
2023

Abstract

Computer Science Computer Science, Theory & Methods Engineering Engineering, Electrical & Electronic Science & Technology Technology
Statistical analytics on encrypted data requires a fully-homomorphic encryption (FHE) scheme. However, heavy computation overheads make FHE impractical. In this paper we propose a novel approach to achieve privacy-preserving statistical analysis on an encrypted database. The main idea of this work is to construct a privacy-preserving calculator to calculate attributes' count values for later statistical analysis. To authenticate these encrypted count values, we adopt an authenticable additive homomorphic encryption scheme to construct the calculator. We formalize the notion of an authenticable privacy-preserving calculator that has properties of broadcasting and additive homomorphism. Further, we propose a cryptosystem based on binary vectors to achieve complex logic expressions for statistical analysis on encrypted data. With the aid of the proposed cryptographic calculator, we design several protocols for statistical analysis including conjunctive, disjunctive and complex logic expressions to achieve more complicated statistical functionalities. Experimental results show that the proposed scheme is feasible and practical.

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.187 Security Systems
4.187.160 Cryptographic Protocols
Web Of Science research areas
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
ESI research areas
Computer Science
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