Name
Title | ||
|---|---|---|
Homopal: A Secure Collaborative Machine Learning Platform Based On Homomorphic Encryption |
Abstract | ||
|---|---|---|
Homomorphic Encryption (HE) allows encrypted data to be processed without decryption, which could maximize the protection of user privacy without affecting the data utility. Thanks to strides made by cryptographers in the past few years, the efficiency of HE has been drastically improved, and machine learning on homomorphically encrypted data has become possible. Several works have explored machine learning based on HE, but most of them are restricted to the outsourced scenario, where all the data comes from a single data owner. We propose HomoPAl, an HE -based secure collaborative machine learning system, enabling a more promising scenario, where data from multiple data owners could be securely processed. Moreover, we integrate our system with the popular MPI framework to achieve parallel HE computations. Experiments show that our system can train a logistic regression model on millions of homomorphically encrypted data in less than two minutes. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICDE48307.2020.00152 | 2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2020) |
Keywords | DocType | ISSN |
homomorphic encryption, machine learning | Conference | 1084-4627 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qifei Li | 1 | 0 | 0.34 |
| Zhicong Huang | 2 | 58 | 6.47 |
| Lu Wen-jie | 3 | 2 | 1.71 |
| Cheng Hong | 4 | 0 | 0.34 |
| Hunter Qu | 5 | 3 | 1.72 |
| Hui He | 6 | 80 | 16.45 |
| Weizhe Zhang | 7 | 287 | 53.07 |