Mining frequent graph patterns with differential privacy

Entong Shen, Ting Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Citations (Scopus)

Abstract

Discovering frequent graph patterns in a graph database offers valuable information in a variety of applications. However, if the graph dataset contains sensitive data of individuals such as mobile phonecall graphs and web-click graphs, releasing discovered frequent patterns may present a threat to the privacy of individuals. Differential privacy has recently emerged as the de facto standard for private data analysis due to its provable privacy guarantee. In this paper we propose the first differentially private algorithm for mining frequent graph patterns. We first show that previous techniques on differentially private discovery of frequent itemsets cannot apply in mining frequent graph patterns due to the inherent complexity of handling structural information in graphs. We then address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling based algorithm. Unlike previous work on frequent itemset mining, our techniques do not rely on the output of a non-private mining algorithm. Instead, we observe that both frequent graph pattern mining and the guarantee of differential privacy can be unified into anMCMCsampling framework. In addition, we establish the privacy and utility guarantee of our algorithm and propose an efficient neighboring pattern counting technique as well. Experimental results show that the proposed algorithm is able to output frequent patterns with good precision.

Original languageEnglish
Title of host publicationKDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages545-553
Number of pages9
VolumePart F128815
ISBN (Electronic)9781450321747
DOIs
Publication statusPublished - 11 Aug 2013
Event19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, United States
Duration: 11 Aug 201314 Aug 2013

Other

Other19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
CountryUnited States
CityChicago
Period11/8/1314/8/13

Fingerprint

Markov processes
Sampling

Keywords

  • Differential privacy
  • Graph pattern mining

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Shen, E., & Yu, T. (2013). Mining frequent graph patterns with differential privacy. In KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. Part F128815, pp. 545-553). [2487601] Association for Computing Machinery. https://doi.org/10.1145/2487575.2487601

Mining frequent graph patterns with differential privacy. / Shen, Entong; Yu, Ting.

KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. Part F128815 Association for Computing Machinery, 2013. p. 545-553 2487601.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shen, E & Yu, T 2013, Mining frequent graph patterns with differential privacy. in KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. vol. Part F128815, 2487601, Association for Computing Machinery, pp. 545-553, 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, United States, 11/8/13. https://doi.org/10.1145/2487575.2487601
Shen E, Yu T. Mining frequent graph patterns with differential privacy. In KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. Part F128815. Association for Computing Machinery. 2013. p. 545-553. 2487601 https://doi.org/10.1145/2487575.2487601
Shen, Entong ; Yu, Ting. / Mining frequent graph patterns with differential privacy. KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. Part F128815 Association for Computing Machinery, 2013. pp. 545-553
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