Preserving Multi-party Machine Learning with Homomorphic Encryption

By Hassan Takabi, Ehsan Hesamifard. 2016

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Citations in the corpus

Learning Privately over Distributed Features: An ADMM Sharing Approach (2019, background): “Another approach to privacy-preserving machine learning is through encryption Gilad-Bachrach et al. (2016); Takabi et al. (2016); Kikuchi et al. (2018) or secret sharing Mohassel and Zhang (2017); Wan et al. (2007); Bonte and Vercauteren (2018), so that models are trained on encrypted data.”
Privacy-preserving Machine Learning as a Service (2018, positive): “Based on our extensive experiments, this approach always lead to the best polynomial approximation [45].”
FDML: A Collaborative Machine Learning Framework for Distributed Features (2019, background): “, via homomorphic encryption [11, 28] or secret sharing [3, 22, 29].”
Data Security and Privacy Protection for Cloud Storage: A Survey (2020, background): “To solve this problem, the degree of approximate polynomials is set in an appropriate interval, and the resulting noise is controlled within a threshold value in [77].”
Practical Federated Gradient Boosting Decision Trees (2019, background): “Much research efforts have been devoted to developing new learning algorithms in the setting of vertical or horizontal federated learning (Smith et al. 2017; Takabi, Hesamifard, and Ghasemi 2016; Liu, Chen, and Yang 2018; Yurochkin et al. 2019).”
Minimizing latency for secure distributed computing (2017, background): “On the other hand, privacy concerns have been studied in the machine learning literature, see e.g., [16]–[18].”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “In this regard, different techniques can be use for preserving privacy, e.g., using cryptographic approaches (like homomorphic encryption [74], and multi-party computation [75], etc.), differential privacy [76], and federated learning [77].”