计算机科学
密码学
机器学习
人工智能
密码原语
推论
过程(计算)
机器翻译
信息隐私
领域(数学)
密码协议
数据科学
计算机安全
数学
纯数学
操作系统
作者
Hong Qiu,Debiao He,Qi Feng,Muhammad Khurram Khan,Min Luo,Kim‐Kwang Raymond Choo
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-17
标识
DOI:10.1109/tkde.2023.3321803
摘要
Advances in machine learning have enabled a broad range of complex applications, such as image recognition, recommendation system and machine translation. Data plays an important role in our increasingly complex and diverse environments, and this also reinforces the importance of data privacy in machine learning-enabled applications. Although there are a number of literature survey articles on machine learning, only a few studies have investigated the cryptographic primitives used in privacy-preserving machine learning (PPML). In other words, there is no, or limited, systematization of knowledge (SoK) that provides a comprehensive introduction to cryptography that have been deployed in PPML. In this paper, we firstly introduce some basic concepts such as machine learning tasks and processes. Then, we review and systematize the cryptographic primitives used in PPML. We analyze these existing privacy-preserving schemes in their learning process, especially training and inference. Finally, we conclude our survey and provide an outlook on future trends and research directions in the field.
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