PU_Bpub: High-Dimensional Data Release Mechanism Based on Spectral Clustering with Local Differential Privacy

Abstract

Although the release and analysis of high-dimensional data bring tremendous value to people, it causes great hidden danger to participants’ privacy in the meantime. Various privacy protection methods based on differential privacy have been proposed at present. However, most of them cannot simultaneously solve the problems of high computational overhead and privacy threats from untrusted servers caused by the curse of high dimensionality. Therefore, we propose a safer and more effective high-dimensional data release algorithm based on local differential privacy, which is referred to as PU_Bpub. It effectively preserves the dimensional correlation of the original high-dimensional data and reduces the communication overhead of synthetic data. Extensive experiments on real-world datasets demonstrate that our solution substantially outperforms the state-of-the-art techniques in terms of computational overhead, and the synthetic dataset has high utility.