Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data 2015
DOI: 10.1145/2723372.2737785
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Private Release of Graph Statistics using Ladder Functions

Abstract: Protecting the privacy of individuals in graph structured data while making accurate versions of the data available is one of the most challenging problems in data privacy. Most efforts to date to perform this data release end up mired in complexity, overwhelm the signal with noise, and are not effective for use in practice. In this paper, we introduce a new method which guarantees differential privacy. It specifies a probability distribution over possible outputs that is carefully defined to maximize the util… Show more

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Cited by 93 publications
(95 citation statements)
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“…However, rather than using a transitive closure probability, we count the number of triangles in the input graph and then add transitive edges until the number of triangles in the resulting graph matches that of the input graph. The number of triangles in a graph can be accurately estimated under differential privacy using a recent technique [37] (Appendix C.3). Like TCL, for each transitive edge we add, we remove a seed edge to maintain the expected degree distribution; however, since the deleted edge may itself be part of one or more existing triangles, we reject proposed replacements that decrease the net triangle count.…”
Section: Structural Model: Tricyclementioning
confidence: 99%
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“…However, rather than using a transitive closure probability, we count the number of triangles in the input graph and then add transitive edges until the number of triangles in the resulting graph matches that of the input graph. The number of triangles in a graph can be accurately estimated under differential privacy using a recent technique [37] (Appendix C.3). Like TCL, for each transitive edge we add, we remove a seed edge to maintain the expected degree distribution; however, since the deleted edge may itself be part of one or more existing triangles, we reject proposed replacements that decrease the net triangle count.…”
Section: Structural Model: Tricyclementioning
confidence: 99%
“…Wang et al outlined a general, divide and conquer approach using smooth sensitivity for graph analysis tasks that have low local sensitivity, such as clustering coefficient [35]. Zhang et al [37] defined the Ladder framework for producing accurate DP estimates of subgraph counting queries, including triangles and k-stars. The Ladder framework combines the concept of "local sensitivity at distance t" [25] with the exponential mechanism [22].…”
Section: Related Workmentioning
confidence: 99%
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