Poster

Faster approximate subgraph counts with privacy

Dung Nguyen · Mahantesh Halappanavar · Venkatesh Srinivasan · Anil Vullikanti

Great Hall & Hall B1+B2 (level 1) #1611
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Wed 13 Dec 8:45 a.m. PST — 10:45 a.m. PST

Abstract: One of the most common problems studied in the context of differential privacy for graph data is counting the number of non-induced embeddings of a subgraph in a given graph. These counts have very high global sensitivity. Therefore, adding noise based on powerful alternative techniques, such as smooth sensitivity and higher-order local sensitivity have been shown to give significantly better accuracy. However, all these alternatives to global sensitivity become computationally very expensive, and to date efficient polynomial time algorithms are known only for few selected subgraphs, such as triangles, $k$-triangles, and $k$-stars.In this paper, we show that good approximations to these sensitivity metrics can be still used to get private algorithms.Using this approach, we show the first quasilinear time and parallel algorithms for privately counting the number of triangles.We also give a private polynomial time algorithm for counting any constant size subgraph using less noise than the global sensitivity; we show this can be improved significantly for counting paths in special classes of graphs.

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