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Nov 11, 2020 · In this paper, we investigate the privacy-preserving technology in machine learning application. We first introduce the models in privacy-preserving machine ...
In this paper, we investigate the privacy-preserving technology in machine learning application. We first introduce the models in privacy-preserving machine ...
An analysis of more than 45 papers related to privacy attacks against machine learning that have been published during the past seven years.
Missing: Protocols. | Show results with:Protocols.
This paper presents a comprehensive survey of recent Machine Learning (ML)- and Deep Learning (DL)-based solutions for privacy in IoT.
In this paper, we aim to review these studies and examine opportunities and concerns related to utilizing data in ML-based solutions for privacy in IoT.
On this basis, the paper comparatively analyzes the main advantages and disadvantages of different mechanisms of privacy preserving for machine learning.
Sep 5, 2022 · In this paper, we provide a detailed analysis of state of the art for collaborative ML approaches from a privacy perspective.
Oct 7, 2020 · TL;DR: This paper reviews and systematizes the cryptographic primitives used in privacy-preserving machine learning, and analyzes these existing ...
The state-of-the-art methods for Privacy-Preserving Machine Learning (PPML), such as safe multi-party computation, homomorphic encryption, and differential ...
An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data.