A General Framework for Privacy-Preserving Distributed Greedy Algorithm

TL;DR

This paper introduces a general framework that transforms distributed greedy algorithms into privacy-preserving versions, ensuring data privacy without sacrificing the algorithm's effectiveness in multi-party data analysis.

Contribution

The paper presents a novel framework enabling most distributed greedy algorithms to be converted into privacy-preserving algorithms while maintaining their original performance.

Findings

Most distributed greedy algorithms can be adapted to preserve privacy.

The framework achieves the same results as non-privacy-preserving algorithms.

It effectively protects sensitive data during distributed computation.

Abstract

Increasingly more attention is paid to the privacy in online applications due to the widespread data collection for various analysis purposes. Sensitive information might be mined from the raw data during the analysis, and this led to a great privacy concern among people (data providers) these days. To deal with this privacy concerns, multitudes of privacy-preserving computation schemes are proposed to address various computation problems, and we have found many of them fall into a class of problems which can be solved by greedy algorithms. In this paper, we propose a framework for distributed greedy algorithms in which instances in the feasible set come from different parties. By our framework, most generic distributed greedy algorithms can be converted to a privacy preserving one which achieves the same result as the original greedy algorithm while the private information associated with the instances is still protected.