Web: http://arxiv.org/abs/2211.10708

Nov. 22, 2022, 2:20 a.m. | Samah Baraheem, Zhongmei Yao

cs.CR updates on arXiv.org arxiv.org

Nowadays, machine learning models and applications have become increasingly
pervasive. With this rapid increase in the development and employment of
machine learning models, a concern regarding privacy has risen. Thus, there is
a legitimate need to protect the data from leaking and from any attacks. One of
the strongest and most prevalent privacy models that can be used to protect
machine learning models from any attacks and vulnerabilities is differential
privacy (DP). DP is strict and rigid definition of privacy, …

differential privacy future machine machine learning outlook privacy survey

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