PSA: Private Set Alignment for Secure and Collaborative Analytics on Large-Scale Data

TL;DR

This paper introduces PSA, a privacy-preserving protocol for secure dataset alignment between companies, enabling collaborative analytics without revealing sensitive customer information, and demonstrates its efficiency on large datasets.

Contribution

The paper presents novel PSA protocols with two privacy levels, based on modified oblivious switching networks, and provides implementation benchmarks showing significant performance improvements.

Findings

Successfully joined datasets of one million records in 35.5 seconds

Achieved 100x speedup over homomorphic encryption protocols

Proved a new quasi-linear complexity algorithm for oblivious switching networks

Abstract

Enforcement of privacy regulation is essential for collaborative data analytics. In this work, we address a scenario in which two companies expect to securely join their datasets with respect to their common customers to maximize data insights. Apart from the necessary protection of raw data, it becomes more challenging to protect the identities and attributes of common customers, as it requires participants to align their records associated with common customers without knowing who they are. We proposed a solution, dubbed PSA, for this scenario, which is effectively applicable to real-world use cases, such as evaluating advertising conversion using data from both publishers and merchants. The contributions of this work are threefold: 1. We defined the notion of PSA with two levels of privacy protection and proposed novel PSA protocols based on the modified oblivious switching network, which leverages efficient symmetric key operations and offline precomputation to save online run time. 2. We implemented and benchmarked the proposed protocols in different network conditions by joining two datasets, each at the scale of one million records, in 35.5 sec on a single thread with a network bandwidth of 500 Mbps, resulting in an X100 improvement over the existing Homomorphic based protocols. 3. We give new proof for an algorithm of quasi-linear complexity that constructs an oblivious switching network to achieve a target permutation distinct from the existing one in the literature.