Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption
| Autor: | C. David Page, Kyonghwan Yoon, Marc Joye, Irene Giacomelli, Somesh Jha |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
020203 distributed computing
Theoretical computer science business.industry Computer science Computation Homomorphic encryption Cryptography 02 engineering and technology Encryption Regularization (mathematics) Bottleneck Regression Linear regression 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business |
| Zdroj: | Applied Cryptography and Network Security ISBN: 9783319933863 ACNS |
| Popis: | Linear regression with 2-norm regularization (i.e., ridge regression) is an important statistical technique that models the relationship between some explanatory values and an outcome value using a linear function. In many applications (e.g., predictive modeling in personalized health-care), these values represent sensitive data owned by several different parties who are unwilling to share them. In this setting, training a linear regression model becomes challenging and needs specific cryptographic solutions. This problem was elegantly addressed by Nikolaenko et al. in S&P (Oakland) 2013. They suggested a two-server system that uses linearly-homomorphic encryption (LHE) and Yao’s two-party protocol (garbled circuits). In this work, we propose a novel system that can train a ridge linear regression model using only LHE (i.e., without using Yao’s protocol). This greatly improves the overall performance (both in computation and communication) as Yao’s protocol was the main bottleneck in the previous solution. The efficiency of the proposed system is validated both on synthetically-generated and real-world datasets. |
| Databáze: | OpenAIRE |
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