Privacy Preserving Data Mining of Sequential Patterns for Network Traffic Data

“…The mining of sequential patterns has become an active research topic [1,4,5,8,9,11,12,14,15,24,26,27]. Its wide applications include market basket analysis, traversal pattern finding, medical treatment diagnosis, natural disaster prediction, DNA sequence analysis, and so on.…”

Section: Introductionmentioning

confidence: 99%

Fast discovery of sequential patterns in large databases using effective time-indexing

Lin

Hsueh

Chang

2008

Information Sciences

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“…(26) and (27), the speedup factor is jDj/jCj. In addition, according to our experiments, the modified EM algorithm generally converges much faster than the conventional one.…”

Section: Computational Gain and Communication Overheadmentioning

confidence: 99%

Learning latent variable models from distributed and abstracted data

Zhang

Cheung

2011

Information Sciences

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“…Recent developments in privacy preserving data mining have proposed many efficient and practical techniques for hiding sensitive information that could have been discovered by data mining algorithms [2,4,10,11,17,18,22,24,27,33]. There are four broad areas of research in the field of privacy preserving data mining: privacy preserving data publishing, privacy preserving applications, utility issues, and distributed privacy with adversarial collaboration [1].…”

Section: Related Workmentioning

confidence: 99%

Hiding collaborative recommendation association rules on horizontally partitioned data

Wang

Lai

Hong

et al. 2010

IDA

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The study of privacy preserving data mining has become more important in recent years due to the increasing amount of personal data in public, the increasing sophistication of data mining algorithms to leverage this information, and the increasing concern of privacy breaches. Association rule hiding in which some of the association rules are suppressed in order to preserve privacy has been identified as a practical privacy preserving application [5,9,12,16,[19][20][21]23,25,[28][29][30][31]. Most current association rule hiding techniques assume that the data to be sanitized are in one single data set. However, in the real world, data may exist in distributed environment and owned by non-trusting parties that might be willing to collaborate. In this work, we propose a framework to hide collaborative recommendation association rules where the data sets are horizontally partitioned and owned by non-trusting parties. Algorithms to hide the collaborative recommendation association rules and to merge the sanitized data sets are introduced. Performance and various side effects of the proposed approach are analyzed numerically. Comparisons with trusting-third-party approach are reported. The proposed non-trusting-third-party approach shows better processing time, with similar side effects.

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Privacy Preserving Data Mining of Sequential Patterns for Network Traffic Data

Cited by 10 publications

References 11 publications

Fast discovery of sequential patterns in large databases using effective time-indexing

Fast discovery of sequential patterns in large databases using effective time-indexing

Learning latent variable models from distributed and abstracted data

Hiding collaborative recommendation association rules on horizontally partitioned data

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