• frequent itemsets mining;
  • distributed association rules mining;
  • multi-party computation;
  • privacy preserving;
  • collaborative framework


Knowledge-discovering or pattern-discovering process, such as data mining, is an important technique to discover hidden but useful information from a large volume of data. Under distributed environment, data mining task has become a challenging task due to data protection and privacy concerns. The secure multi-party computation (SMC) approach has been widely used to solve privacy-preserving data mining problems. However, generic SMC solutions are not practical from an efficiency point of view, especially when the number of parties and the size of the data are large. In view of these problems, we utilize a secure collaborative framework to facilitate the computation protocol for SMC. In this paper, we particularly consider the problem of privacy-preserving frequent itemsets mining under distributed environment. Our solution reduces the risk for central data mining and improves the efficiency of the current generic SMC solutions. Furthermore, our solution is more reliable and flexible regardless of the number of parties involved. Copyright © 2011 John Wiley & Sons, Ltd.