Privacy-Preserving Frequent Pattern Mining across Private Databases Ada Wai-Chee Fu, Raymond Chi-Wing Wong, and Ke Wang   View Paper (PDF)   Return to Session 12: Security and Privacy Abstract Privacy consideration has much significance in the application of data mining. It is very important that the privacy of individual parties will not be exposed when data mining techniques are applied to a large collection of data about the parties. In many scenarios such as data warehousing or data integration, data from the different parties form a many-to-many schema. This paper addresses the problem of privacy-preserving frequent pattern mining in such a schema across two dimension sites. We assume that sites are not trusted and they are semi-honest. Our method is based on the concept of semi-join and does not involve data encryption which is used in most previous work. Experiments are conducted to study the efficiency of the proposed models. ©2006 Association for Computing Machinery