High-Performance Privacy-Preserving Matrix Completion for Trajectory Recovery

TL;DR

This paper introduces a high-performance privacy-preserving matrix completion method that encrypts data, performs matrix completion efficiently using ADMM, and achieves faster speed with higher accuracy for trajectory recovery.

Contribution

It proposes a novel lightweight encryption combined with ADMM for privacy-preserving matrix completion, improving speed and accuracy over existing methods.

Findings

Faster than existing algorithms in numerical experiments.

Higher accuracy in matrix completion tasks.

Effective privacy preservation during trajectory recovery.

Abstract

Matrix completion has important applications in trajectory recovery and mobile social networks. However, sending raw data containing personal, sensitive information to cloud computing nodes may lead to privacy exposure issue.The privacy-preserving matrix completion is a useful approach to perform matrix completion while preserving privacy. In this paper, we propose a high-performance method for privacy-preserving matrix completion. First,we use a lightweight encryption scheme to encrypt the raw data and then perform matrix completion using alternating direction method of multipliers (ADMM). Then,the complemented matrix is decrypted and compared with the original matrix to calculate the error. This method has faster speed with higher accuracy. The results of numerical experiments reveal that the proposed method is faster than other algorithms.