Efficient and Private Approximations of Distributed Databases Calculations

TL;DR

This paper introduces a sampling-based method for efficient, privacy-preserving distributed calculations on separated databases, balancing performance and accuracy, with experimental validation on set intersection approximation.

Contribution

It proposes a novel sampling technique tailored for non-collaborative, vertically partitioned datasets to improve distributed computation efficiency while maintaining privacy.

Findings

Sampling reduces computational complexity significantly.

The method achieves acceptable accuracy with smaller sample sizes.

Experimental results validate the effectiveness of the approach.

Abstract

In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separated databases and, a relative to it, issue of private data release were intensively investigated. However, despite a considerable progress, computational complexity, due to an increasing size of data, remains a limiting factor in real-world deployments, especially in case of privacy-preserving computations. In this paper, we present a general method for trade off between performance and accuracy of distributed calculations by performing data sampling. Sampling was a topic of extensive research that recently received a boost of interest. We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets. The method is exemplified and tested on approximation of intersection set both without and with privacy-preserving mechanism. An analysis of the bound on error as a function of the sample size is discussed and heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach.