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Locally differentially private (LDP) graph analysis allows private analysison a graph that is distributed across multiple users. However, suchcomputations are vulnerable to data poisoning attacks where an adversary canskew the results by submitting malformed data. In this paper, we formally studythe impact of poisoning attacks for graph degree estimation protocols underLDP. We make two key technical contributions. First, we observe LDP makes aprotocol more vulnerable to poisoning -- the impact of poisoning is worse whenthe adversary can directly poison their (noisy) responses, rather than theirinput data. Second, we observe that graph data is naturally redundant -- everyedge is shared between two users. Leveraging this data redundancy, we designrobust degree estimation protocols under LDP that can significantly reduce theimpact of data poisoning and compute degree estimates with high accuracy. Weevaluate our proposed robust degree estimation protocols under poisoningattacks on real-world datasets to demonstrate their efficacy in practice.

Robustness of Locally Differentially Private Graph Analysis Against Poisoning