Authenticable data analytics over encrypted data in the cloud
Publication Type
Journal Article
Publication Date
1-2023
Abstract
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.
Keywords
Encrypted data, authenticable encryption, data privacy, homomorphic encryption
Discipline
Information Security | Numerical Analysis and Scientific Computing
Research Areas
Cybersecurity
Publication
IEEE Transactions on Information Forensics and Security
ISSN
1556-6013
Identifier
10.1109/TIFS.2023.3256132
Publisher
Institute of Electrical and Electronics Engineers
Citation
CHEN, Lanxing; MU, Yi; ZENG, Lingfang; REZAEIBAGHA, Fatemah; and DENG, Robert H..
Authenticable data analytics over encrypted data in the cloud. (2023). IEEE Transactions on Information Forensics and Security.
Available at: https://ink.library.smu.edu.sg/sis_research/7815
Additional URL
https://doi.org/10.1109/TIFS.2023.3256132