2010 CollusionResistantPrivacyPreser

• (Yang et al., 2010) ⇒ Bin Yang, Hiroshi Nakagawa, Issei Sato, and Jun Sakuma. (2010). “Collusion-resistant Privacy-preserving Data Mining.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835867

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Cited By

• http://scholar.google.com/scholar?q=%22Collusion-resistant+privacy-preserving+data+mining%22+2010
• http://portal.acm.org/citation.cfm?id=1835867&preflayout=flat#citedby

Quotes

Author Keywords

Privacy, Distributed, Collusion, Data Mining, Secure Multiparty Computation.

Abstract

Recent research in privacy-preserving data mining (PPDM) has become increasingly popular due to the wide application of data mining and the increased concern regarding the protection of private and personal information. Lately, numerous methods of privacy-preserving data mining have been proposed. Most of these methods are based on an assumption that semi-honest is and collusion is not present. In other words, every party follows such protocol properly with the exception that it keeps a record of all its intermediate computations without sharing the record with others. In this paper, we focus our attention on the problem of collusions, in which some parties may collude and share their record to deduce the private information of other parties. In particular, we consider a general problem in PPDM - multiparty secure computation of some functions of secure summations of data spreading around multiple parties. To solve such a problem, we propose a new method that entails a high level of security - full-privacy. With this method, no sensitive information of a party will be revealed even when all other parties collude. In addition, this method is efficient with a running time of [math]\displaystyle{ O(m) }[/math]. We will also show that by applying this general method, a large number of problems in PPDM can be solved with enhanced security.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 CollusionResistantPrivacyPreser	Bin Yang Hiroshi Nakagawa Issei Sato Jun Sakuma			Collusion-resistant Privacy-preserving Data Mining		KDD-2010 Proceedings		10.1145/1835804.1835867		2010