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MAS-Encryption and Its Applications in Privacy-Preserving Classifiers

Lookup NU author(s): Chongzhi Gao, Dr Changyu Dong

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE Computer Society, 2020.

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Abstract

IEEEHomomorphic encryption (HE) schemes, such as fully homomorphic encryption (FHE), support a number of useful computations on ciphertext in a broad range of applications, such as e-voting, private information retrieval, cloud security, and privacy protection. While FHE schemes do not require any interaction during computation, the key limitations are large ciphertext expansion and inefficiency. Thus, to overcome these limitations, we develop a novel cryptographic tool, MAS-Encryption (MASE), to support real-value input and secure computation on the multiply-add structure. The multiply-add structures exist in many important protocols, such as classifiers and outsourced protocols, and we will explain how MASE can be used to protect the privacy of these protocols, using two case study examples. Specifically, the first case study example is the privacy-preserving Naive Bayes classifier that can achieve minimal Bayes risk, and the other example is the privacy-preserving support vector machine. We prove that the constructed classifiers are secure and evaluate their performance using real-world datasets. Experiments show that our proposed MASE scheme and MASE based classifiers are efficient, in the sense that we achieve an optimal tradeoff between computation efficiency and communication interactions. Thus, we avoid the inefficiency of FHE based paradigm.


Publication metadata

Author(s): Gao C, Li J, Xia S, Choo KR, Lou W, Dong C

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Knowledge and Data Engineering

Year: 2020

Pages: epub ahead of print

Online publication date: 31/07/2020

Acceptance date: 02/04/2016

Date deposited: 10/11/2020

ISSN (print): 1041-4347

ISSN (electronic): 1558-2191

Publisher: IEEE Computer Society

URL: https://doi.org/10.1109/TKDE.2020.3009221

DOI: 10.1109/TKDE.2020.3009221


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