Privacy Preserving Mining Maximal Frequent Patterns in Transactional Databases
- 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
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.
Keywordsprivacy preserving data mining maximal frequent pattern prime number theory
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