DPGraph: A Benchmark Platform for Differentially Private Graph Analysis
Siyuan Xia, Beizhen Chang, Karl Knopf, Yihan He, Yuchao Tao, Xi Qin He · 2021
Differential privacy has become an appealing choice for analyzing sensitive data while offering strong privacy protection, even for complex data types like graphs. Despite a decade of academic efforts in designing differentially private algorithms for graph analysis, few works have been used in practice. This is due to their complexity in the choice of privacy guarantees and parameter/environmental configurations, or due to their scalability issues for large datasets.