Privacy preserving data mining of sequential patterns for network traffic data

Seung Woo Kim, Sanghyun Park, Jung Im Won, Sang Wook Kim

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

2 Citations (Scopus)

Abstract

As a total amount of traffic data in networks has been growing at an alarming rate, many researches to mine traffic data with the purpose of getting useful information are currently being performed. However, since network traffic data contain the information about Internet usage patterns of users, network users' privacy can be compromised during the mining process. In this paper, we propose an efficient and practical method for privacy preserving sequential pattern mining on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model that operates as a single mining server and the retention replacement technique that changes the answer to a query probabilistically. In addition, our method accelerates the overall mining process by maintaining the meta tables in each site. Extensive experiments with real-world network traffic data revealed the correctness and the efficiency of the proposed method.

Original languageEnglish
Title of host publicationAdvances in Databases
Subtitle of host publicationConcepts, Systems and Applications - 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Proceedings
Pages201-212
Number of pages12
Publication statusPublished - 2007 Dec 1
Event12th International Conference on Database Systems for Advanced Applications, DASFAA 2007 - Bangkok, Thailand
Duration: 2007 Apr 92007 Apr 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4443 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on Database Systems for Advanced Applications, DASFAA 2007
CountryThailand
CityBangkok
Period07/4/907/4/12

Fingerprint

Privacy Preserving Data Mining
Sequential Patterns
Network Traffic
Data mining
Servers
Process Mining
Privacy
Internet
Mining
Server
Traffic
Addition method
Frequent Pattern
Privacy Preserving
Experiments
Repository
Accelerate
Replacement
Tables
Correctness

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kim, S. W., Park, S., Won, J. I., & Kim, S. W. (2007). Privacy preserving data mining of sequential patterns for network traffic data. In Advances in Databases: Concepts, Systems and Applications - 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Proceedings (pp. 201-212). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4443 LNCS).
Kim, Seung Woo ; Park, Sanghyun ; Won, Jung Im ; Kim, Sang Wook. / Privacy preserving data mining of sequential patterns for network traffic data. Advances in Databases: Concepts, Systems and Applications - 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Proceedings. 2007. pp. 201-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "As a total amount of traffic data in networks has been growing at an alarming rate, many researches to mine traffic data with the purpose of getting useful information are currently being performed. However, since network traffic data contain the information about Internet usage patterns of users, network users' privacy can be compromised during the mining process. In this paper, we propose an efficient and practical method for privacy preserving sequential pattern mining on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model that operates as a single mining server and the retention replacement technique that changes the answer to a query probabilistically. In addition, our method accelerates the overall mining process by maintaining the meta tables in each site. Extensive experiments with real-world network traffic data revealed the correctness and the efficiency of the proposed method.",
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Kim, SW, Park, S, Won, JI & Kim, SW 2007, Privacy preserving data mining of sequential patterns for network traffic data. in Advances in Databases: Concepts, Systems and Applications - 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4443 LNCS, pp. 201-212, 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Bangkok, Thailand, 07/4/9.

Privacy preserving data mining of sequential patterns for network traffic data. / Kim, Seung Woo; Park, Sanghyun; Won, Jung Im; Kim, Sang Wook.

Advances in Databases: Concepts, Systems and Applications - 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Proceedings. 2007. p. 201-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4443 LNCS).

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

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N2 - As a total amount of traffic data in networks has been growing at an alarming rate, many researches to mine traffic data with the purpose of getting useful information are currently being performed. However, since network traffic data contain the information about Internet usage patterns of users, network users' privacy can be compromised during the mining process. In this paper, we propose an efficient and practical method for privacy preserving sequential pattern mining on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model that operates as a single mining server and the retention replacement technique that changes the answer to a query probabilistically. In addition, our method accelerates the overall mining process by maintaining the meta tables in each site. Extensive experiments with real-world network traffic data revealed the correctness and the efficiency of the proposed method.

AB - As a total amount of traffic data in networks has been growing at an alarming rate, many researches to mine traffic data with the purpose of getting useful information are currently being performed. However, since network traffic data contain the information about Internet usage patterns of users, network users' privacy can be compromised during the mining process. In this paper, we propose an efficient and practical method for privacy preserving sequential pattern mining on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model that operates as a single mining server and the retention replacement technique that changes the answer to a query probabilistically. In addition, our method accelerates the overall mining process by maintaining the meta tables in each site. Extensive experiments with real-world network traffic data revealed the correctness and the efficiency of the proposed method.

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Kim SW, Park S, Won JI, Kim SW. Privacy preserving data mining of sequential patterns for network traffic data. In Advances in Databases: Concepts, Systems and Applications - 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Proceedings. 2007. p. 201-212. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).