Privacy Preserving Mining Maximal Frequent Patterns in Transactional Databases

  • Md. Rezaul Karim
  • Md. Mamunur Rashid
  • Byeong-Soo Jeong
  • Ho-Jin Choi
Conference paper

DOI: 10.1007/978-3-642-29038-1_23

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7238)
Cite this paper as:
Karim M.R., Rashid M.M., Jeong BS., Choi HJ. (2012) Privacy Preserving Mining Maximal Frequent Patterns in Transactional Databases. In: Lee S., Peng Z., Zhou X., Moon YS., Unland R., Yoo J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg

Abstract

Problem of finding frequent patterns has long been studied because it is very essential to data mining tasks such as association rule analysis, clustering, and classification analysis. Privacy preserving data mining is another important issue for this domain since most users do not want their private information to leak out. In this paper, we proposed an efficient approach for mining maximal frequent patterns from a large transactional database with privacy preserving capability. As for privacy preserving, we utilized prime number based data transformation method. We also developed a noble algorithm for mining maximal frequent patterns based on lattice structure. Extensive performance analysis shows the effectiveness of our approach.

Keywords

privacy preserving data mining maximal frequent pattern prime number theory 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Md. Rezaul Karim
    • 1
  • Md. Mamunur Rashid
    • 1
  • Byeong-Soo Jeong
    • 1
  • Ho-Jin Choi
    • 2
  1. 1.Dept. of Computer EngineeringKyung Hee UniversityKyunggi-doRepublic of Korea
  2. 2.Computer Science Dept.Korea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea

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