Privacy-preservation for gradient descent methods

By L. Wan, Wee Keong Ng, Shuguo Han, Vincent Cheng-Siong Lee. 2007

92 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Privacy & Security

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yang Liu
2. Bingsheng He
3. Qinbin Li
4. Qiang Yang
5. Tianjian Chen
6. Zeyi Wen
7. Zhaomin Wu
8. Yan Kang

In Privacy & Security

1. Yang Liu
2. Yan Kang
3. Qiang Yang
4. Bingsheng He
5. Qinbin Li
6. Zeyi Wen
7. Tianjian Chen
8. Shengping Zhou

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.”
• Federated Machine Learning (2019, background): “Privacy-preserving machine learning algorithms have been proposed for vertically partitioned data, including Cooperative Statistical Analysis [15], association rule mining [65], secure linear regression [22, 32, 55], classification [16] and gradient descent [68].”
• Vertical Federated Learning: Concepts, Advances, and Challenges (2022, background): “The most common training protocol is using gradient descent [37], which requires parties to transmit local model outputs and corresponding gradients, together termed intermediate results, instead of local data.”
• FDML: A Collaborative Machine Learning Framework for Distributed Features (2019, background): “, via homomorphic encryption [11, 28] or secret sharing [3, 22, 29].”
• Efficient privacy preserving video surveillance (2009, background): “Our main achievement is an approach that achieves information theoretic privacy, while being extremely efficient over SMC.”
• Context-Aware Verifiable Cloud Computing (2017, background): “presented a generic formulation of secure gradient descent methods with privacy preservation [17].”
• Privacy-Preserving Backpropagation Neural Network Learning (2009, background): “However, in terms of multi-layer neural networks, their protocol is limited as it is only for one simple neural network configuration with one node in the output layer and no hidden layers.”
• A survey on trust management for Internet of Things (2014, background): “Note: SMC refers to the references [37-66, 67, 68, 70, 71, 74, 77, 78, 79, 80, 81, 82, 83, 85, 88, 89, 90, 91, 94, 95, 96, 97, 104, 105, 106, 107, 108, 110-114, 116, 117-118, 125, 126, 127, 129, 137, 141]”
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “protection: cryptographic methods and differential privacy. Cryptographic methods such as homomorphic encryption[15, 77, 27, 33, 74, 145, 146, 204, 206, 112], and secure multi-party computation (SMC) [157, 36, 23, 51, 24, 101, 17, 62, 91, 181, 39, 65] are widely used in privacy-preserving machine le”