Private Graph Property Testing

2026-09-13

AI Summary

研究通过开发新的隐私放大技术，设计了具有正式隐私保证的高效图属性测试器，解决了大规模图属性测试中的隐私保护问题。

Abstract

Graph property testing asks whether a massive graph satisfies a given property, or is far from doing so, using only a sublinear number of queries to the graph. Since property testers typically inspect only a small, randomly sampled portion of the input, they appear naturally compatible with differential privacy and privacy amplification by subsampling. Despite this, few results link these two fields. We initiate a systematic study of differentially private graph property testing with the goal of designing efficient testers with formal privacy guarantees in the dense and bounded-degree graph models. We develop new privacy amplification theorems for several widely used graph-sampling procedures such as induced subgraph sampling, random walks and k-disc sampling. We then leverage these privacy amplification techniques to design a private canonical tester in the dense graph model, as well as private bipartiteness testers and subgraph freeness testers in the dense and bounded-degree graph models. Finally, using the new privacy amplification theorem for k-disc sampling, we prove that every property of hyperfinite graphs is privately testable. The resulting query complexities of our private testers are comparable to those of their non-private counterparts.

Problem

• Differential Privacy
• Graph Property Testing
• Sublinear Queries

Innovation

• differentially private graph property testing
• privacy amplification theorems
• induced subgraph sampling
• random walks
• k-disc sampling