Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption

By Irene Giacomelli, Somesh Jha, Marc Joye, C. D. Page, Kyonghwan Yoon. 2018

103 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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. Qiang Yang
3. Li Fei-Fei
4. Tianjian Chen
5. Mohit Bansal
6. Philip S. Yu
7. Trevor Darrell
8. Kate Saenko

In Computational Theory and Mathematics

1. Tianjian Chen
2. Josh Beal
3. Xi He
4. Qiang Yang
5. Somesh Jha
6. Daniel Levy
7. Ashwin Machanavajjhala
8. Serena Yeung

Citations in the corpus

• Federated Machine Learning (2019, methodology): “vacy guarantees. Nikolaenko et al.[48] implemented a privacy-preserving protocol for linear regression on horizontally partitioned data using homomorphic encryption and Yao’s garbled circuits and Ref [22, 24] proposed a linear regression approach for vertically partitioned data. These systems solved”
• Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers (2019, background): “This is a very common assumption in the two-server model [46, 47, 49, 68, 81, 84, 85].”
• Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference (2018, methodology): “While HE has been explored for machine learning applications, many works focus on simpler models such as linear [34], logistic [18] and ridge regression [28].”