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.
|Title of host publication||Advances in Databases|
|Subtitle of host publication||Concepts, Systems and Applications - 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Proceedings|
|Number of pages||12|
|Publication status||Published - 2007|
|Event||12th International Conference on Database Systems for Advanced Applications, DASFAA 2007 - Bangkok, Thailand|
Duration: 2007 Apr 9 → 2007 Apr 12
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||12th International Conference on Database Systems for Advanced Applications, DASFAA 2007|
|Period||07/4/9 → 07/4/12|
Bibliographical noteFunding Information:
This work was partially supported by the Korea Research Foundation Grant funded by the Korean Government (KRF-2005-041-D00651) and the ITRC support program supervised by the IITA (IITA-2005-C1090-0502-0009).
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)