Faster Secure Data Mining via Distributed Homomorphic Encryption

TL;DR

This paper introduces a distributed homomorphic encryption framework that reduces computational complexity and enables faster secure data mining, demonstrated by significantly quicker logistic regression training on benchmark datasets.

Contribution

It presents a novel distributed HE-based data mining framework that balances communication and computation to improve scalability of privacy-preserving data analysis.

Findings

Logistic regression training time reduced from 2 hours to 5 minutes.

Framework effectively handles various data mining algorithms.

Demonstrated scalability on benchmark datasets.

Abstract

Due to the rising privacy demand in data mining, Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field. By using the HE technique, it is possible to securely outsource model learning to the not fully trustful but powerful public cloud computing environments. However, HE-based training scales badly because of the high computation complexity. It is still an open problem whether it is possible to apply HE to large-scale problems. In this paper, we propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem. The main idea of our approach is to use the slightly more communication overhead in exchange of shallower computational circuit in HE, so as to reduce the overall complexity. We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets. For example, we successfully train a logistic regression model to recognize the digit 3 and 8 within around 5 minutes, while a centralized counterpart needs almost 2 hours.