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CODASPY
2016

Scalable and Secure Logistic Regression via Homomorphic Encryption

3 years 19 days ago
Scalable and Secure Logistic Regression via Homomorphic Encryption
Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instance. Our system is very scalable in both the dataset size and dimension, tolerating big size for example of hundreds of millions (108 s) records. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its accuracies and other related measures such as F-score and AUC.
Yoshinori Aono, Takuya Hayashi 0001, Le Trieu Phon
Added 31 Mar 2016
Updated 31 Mar 2016
Type Journal
Year 2016
Where CODASPY
Authors Yoshinori Aono, Takuya Hayashi 0001, Le Trieu Phong, Lihua Wang
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