Cryptology ePrint Archive: Report 2019/935

Interpretable Encrypted Searchable Neural Networks

Kai Chen; Zhongrui Lin; Jian Wan; Chungen Xu.

Abstract: In cloud security, traditional searchable encryption (SE) requires high computation and communication overhead for dynamic search and update. The clever combination of machine learning (ML) and SE may be a new way to solve this problem. This paper proposes interpretable encrypted searchable neural networks (IESNN) to explore probabilistic query, balanced index tree construction and automatic weight update in an encrypted cloud environment. In IESNN, probabilistic learning is used to obtain search ranking for searchable index, and probabilistic query is performed based on ciphertext index, which reduces the computational complexity of query significantly. Compared to traditional SE, it is proposed that adversarial learning and automatic weight update in response to user's timely query of the latest data set without expensive communication overhead. The proposed IESNN performs better than the previous works, bringing the query complexity closer to $O(\log N)$ and introducing low overhead on computation and communication.

Category / Keywords: applications / Searchable Encryption; Searchable Neural Networks; Probabilistic Learning; Adversarial Learning; Automatic Weight Update.

Original Publication (in the same form): The 2nd International Conference on Machine Learning for Cyber Security (ML4CS 2019)

Date: received 16 Aug 2019, withdrawn 22 Aug 2019

Contact author: kaichen at njust edu cn

Available format(s): (-- withdrawn --)

Note: The creative combination of cryptology and artificial intelligence may bring new vitality to cryptology.

Version: 20190822:171813 (All versions of this report)

Short URL: ia.cr/2019/935


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