The Homomorphic Encryption in Pipelines Accident Prediction by Using Cloud-based Neural Network

Yelina, T. N.; Bezzateev, Sergey; Mylnikov, V. A.

doi:10.1109/weconf.2019.8840658

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…Particularly, NN have been used with Fast Handover Protocol in place of MIPv6 to replace its short comings, allow the encryption of large scale satellite images for secure transmission and decryption efficiently and lightweight implementation for key systems in an IoT environment [117,120,121]. Other applications of Neural Cryptography has allowed for homomorphic encrpytion to be applied to cloud services for secure communication and noise compression, as well as intelligent transportation systems to allow confidentiality of personal information [122,123]. Finally, chaotic NNs has seem many applications as well [124][125][126].…”

Section: Applications In Industrymentioning

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: Applications In Industrymentioning

confidence: 99%

The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

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“…On the other hand, any datadriving mechanism heavily relies on the quantity and quality of information, which brings about the conflict between data usability and data confidentiality. Fortunately, secure multiparty computation (SMC) [5][6][7] and homomorphic encryption (HE) [8,9] provide us powerful tools to process data in a concealed manner.…”

Section: Introductionmentioning

confidence: 99%

Multiparty Homomorphic Machine Learning with Data Security and Model Preservation

Kuang

et al. 2021

Mathematical Problems in Engineering

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With the widespread application of machine learning (ML), data security has been a serious issue. To eliminate the conflict between data privacy and computability, homomorphism is extensively researched due to its capacity of performing operations over ciphertexts. Considering that the data provided by a single party are not always adequate to derive a competent model via machine learning, we proposed a privacy-preserving training method for the neural network over multiple data providers. Moreover, taking the trainer’s intellectual property into account, our scheme also achieved the goal of model parameter protection. Thanks to the hardness of the conjugate search problem (CSP) and discrete logarithm problem (DLP), the confidentiality of training data and system model can be reduced to well-studied security assumptions. In terms of efficiency, since all messages are coded as low-dimensional matrices, the expansion rates with regard to storage and computation overheads are linear compared to plaintext implementation without accuracy loss. In reality, our method can be transplanted to any machine learning system involving multiple parties due to its capacity of fully homomorphic computation.

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