论文信息 - Privacy Preserving of Shared Data in Deep Learning

Privacy Preserving of Shared Data in Deep Learning

In the age of Big Data the need of developing machine learning algorithms has increased. Such algorithms are used to extract valuable information needed for different types of sectors; health, education, financial …etc. In many cases data has to be shared among several parties to guarantee better accuracy of the results of such algorithms. In these cases privacy of data would be questionable. In this paper a survey has been conducted on research that focused on Privacy Preserving techniques when applying deep learning algorithms on distributed or shared data.

Ahmad Al-Qerem | Eman Al Nagi | Eman Al Nagi | Ahmad Al-qerem

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[2] Jian Shen,et al. Privacy preserving multi-party computation delegation for deep learning in cloud computing , 2018, Inf. Sci..

[3] Shiho Moriai,et al. Privacy-Preserving Deep Learning via Additively Homomorphic Encryption , 2018, IEEE Transactions on Information Forensics and Security.

[4] Michael Naehrig,et al. CryptoNets: applying neural networks to encrypted data with high throughput and accuracy , 2016, ICML 2016.

[5] Helen Nissenbaum,et al. Respecting Context to Protect Privacy: Why Meaning Matters , 2018, Sci. Eng. Ethics.

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[7] Taher El Gamal. A public key cryptosystem and a signature scheme based on discrete logarithms , 1984, IEEE Trans. Inf. Theory.

[8] Nauman Aslam,et al. An efficient reinforcement learning-based Botnet detection approach , 2020, J. Netw. Comput. Appl..

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