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- W4387319330 abstract "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." @default.
- W4387319330 created "2023-10-04" @default.
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- W4387319330 date "2023-01-01" @default.
- W4387319330 modified "2023-10-14" @default.
- W4387319330 title "Cryptographic Primitives in Privacy-Preserving Machine Learning: A Survey" @default.
- W4387319330 doi "https://doi.org/10.1109/tkde.2023.3321803" @default.
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