Applying Neural Network Approach to Homomorphic Encrypted Data

Ghimeş, Ana-Maria; Vladuta, Valentin-Alexandru; Patriciu, Victor-Valeriu; Ionita, Adrian

doi:10.1109/ecai.2018.8679085

Cited by 8 publications

(1 citation statement)

References 9 publications

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“…As this field has been growing rapidly we have seen many changes, as recently as 2018 it was shown that current homomorphic systems were too slow for large amounts of data and NNs were not yet secure enough to work with encrypted data [76]. The same year it was thought a possible solution would be to perform the homomorphic encryptions as a part of a cloud environment using multiple parties [60].…”

Section: Cryptographic Technology Adapted To Neural Computingmentioning

confidence: 99%

The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

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In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.

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Section: Cryptographic Technology Adapted To Neural Computingmentioning

confidence: 99%