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Oct 3, 2023 · Then, we review and systematize the cryptographic primitives used in PPML. We analyze these existing privacy-preserving schemes in their ...
Then, we review and systematize the cryptographic primitives used in PPML. We analyze these existing privacy-preserving schemes in their learning process, ...
In this paper, we first introduce some basic concepts such as machine learning tasks and processes. Then, we review and systematize the cryptographic primitives ...
surveys on machine learning, only few studies have investigated the cryptographic primitives used in privacy-preserving machine learning (PPML). No ...
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5.2.4 Application to Deep Learning. We now apply these primitives to a private deep learning setup in which a model owner interacts with a data owner. The ...
Privacy-preserving cryptographic protocols and primitives, like secure multi-party computation (MPC) and fully homomorphic encryption (FHE), may provide a ...
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A Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption ... cryptographic primitives constructed over a prime finite field and proves ...
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey ... Additionally, specialized SMPC primitives like Private ...
... primitives such as scalar product, matrix multiplication, comparison, and maxpool. The online communication of our scalar product is two ring elements ...
[118] utilize additive secret sharing as a cryptographic primitive to implement a secure multiparty computation protocol for privacy-preserving clustering.