Privacy-Preserving Distributed Optimization Under Time Constraints Using Secure Multi-Party Computation and Evolutionary Algorithms

TL;DR

This paper introduces a method combining evolutionary algorithms and secure multi-party computation to enable privacy-preserving distributed optimization within strict time limits, balancing solution quality and privacy.

Contribution

It proposes a novel approach that reduces runtime overhead of privacy-preserving computations in distributed optimization using evolutionary algorithms, suitable for time-critical applications.

Findings

The approach successfully returns solutions within deadlines in experiments.

Obfuscation enhances privacy but affects solution quality.

Genetic algorithms and NSGA-II are effective in this setting.

Abstract

In distributed optimization, multiple parties collaborate to find an optimal solution to a problem. Privacy-preserving distributed optimization uses techniques, such as secure multi-party computation (MPC), to protect the private inputs of each party. In time-critical settings, the runtime overhead introduced by privacy-preserving computations may prevent the optimization from finishing within the deadline. This paper presents an approach for privacy-preserving distributed optimization in time-critical settings that combines evolutionary algorithms for solution search and MPC for the evaluation of solutions. The approach reduces the impact of privacy-preserving computations on runtime and allows to return solution within the deadline. Obfuscation of evaluation results provides additional protection for private inputs from an honest-but-curious platform provider, but introduces a potential trade-off between protection and solution quality. This trade-off is investigated in experiments using a genetic algorithm for both the single-objective assignment problem and the traveling salesperson problem, as well as NSGA-II for the multi-objective assignment problem.