Secure Machine Learning over Relational Data

TL;DR

This paper develops secure protocols for federated machine learning over relational data, enabling privacy-preserving join operations and model training with enhanced privacy protections including zero information leakage and differential privacy.

Contribution

It introduces secure protocols for join operations over general foreign-key schemas and integrates privacy-preserving techniques into federated machine learning workflows.

Findings

Protocols support secure joins over complex relational schemas

Achieves zero information leakage beyond the trained model

Incorporates differential privacy to protect individual data and model privacy

Abstract

A closer integration of machine learning and relational databases has gained steam in recent years due to the fact that the training data to many ML tasks is the results of a relational query (most often, a join-select query). In a federated setting, this poses an additional challenge, that the tables are held by different parties as their private data, and the parties would like to train the model without having to use a trusted third party. Existing work has only considered the case where the training data is stored in a flat table that has been vertically partitioned, which corresponds to a simple PK-PK join. In this paper, we describe secure protocols to compute the join results of multiple tables conforming to a general foreign-key acyclic schema, and how to feed the results in secret-shared form to a secure ML toolbox. Furthermore, existing secure ML systems reveal the PKs in the join results. We strengthen the privacy protection to higher levels and achieve zero information leakage beyond the trained model. If the model itself is considered sensitive, we show how differential privacy can be incorporated into our framework to also prevent the model from breaching individuals' privacy.