Scalable and Secure Logistic Regression via Homomorphic Encryption
| Title | Scalable and Secure Logistic Regression via Homomorphic Encryption |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Aono, Yoshinori, Hayashi, Takuya, Trieu Phong, Le, Wang, Lihua |
| Conference Name | Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy |
| Date Published | March 2016 |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-3935-3 |
| Keywords | additively homomorphic encryption, composability, logistic regression, Metrics, outsourced computation, pubcrawl, Resiliency, white box, white box cryptography |
| Abstract | 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 the training data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Our system is secure and scalable with the dataset size. |
| URL | http://doi.acm.org/10.1145/2857705.2857731 |
| DOI | 10.1145/2857705.2857731 |
| Citation Key | aono_scalable_2016 |