Differentially private network data release via stochastic kronecker graph

Abstract

Excessive sensitivity problem due to complication of data has been a non-negligible challenge to data privacy protection under differential privacy recently. We design a private data release framework called DPDR-SKG (Differentially Private Data Release via Stochastic Kronecker Graph), which focuses on releasing social network data under differential privacy and uses a two-phase privacy budget allocation. Firstly, we cluster the similar communities of network according to Stochastic Kronecker graph parameter. Secondly, we implement optimized privacy budget allocation in terms of cluster distribution. Experimental results show that the DPDR-SKG outperforms in preserving the privacy of network structure and effectively retaining the data utility.