Paper 2019/935

Interpretable Encrypted Searchable Neural Networks

Kai Chen, Zhongrui Lin, Jian Wan, and 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.

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

Metadata
Available format(s)
-- withdrawn --
Category
Applications
Publication info
Published elsewhere. The 2nd International Conference on Machine Learning for Cyber Security (ML4CS 2019)
Keywords
Searchable EncryptionSearchable Neural NetworksProbabilistic LearningAdversarial LearningAutomatic Weight Update.
Contact author(s)
kaichen @ njust edu cn
History
2019-08-22: withdrawn
2019-08-18: received
See all versions
Short URL
https://ia.cr/2019/935
License
Creative Commons Attribution
CC BY
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