Privacy Preserving Mining System of Association Rules in OpenStack-Based Cloud

Abstract

As an efficient data analysis tool, data mining can discover the potential association and regularity of massive data, and it has been widely used and played an important role in business decision, medical research and so on. However, the data mining technology is also a double-edged sword, in bringing convenience at the same time, will also cause the user’s privacy leak problem. In order to solve the problem, the symmetric searchable encryption technology is introduced into the association rule mining system to protect the privacy, and privacy preserving mining system of s (PP-MSAR) in OpenStack-based Cloud environment is designed. In order to solve the problem that the existing data mining algorithms can’t deal with large-scale data, this paper uses the computational power of Hadoop platform and add the global pruning technique to the existing algorithm based on MapReduce association rules, so that the counting of frequent item sets get reduced. At the same time, this paper add frequent matrix storage method into the distributed association rules algorithm and realize he algorithm of mining association rules for frequent matrix storage based on MapReduce. In addition, the introduction of symmetric searchable encryption technology to support the cloud server-side ciphertext retrieval, on the one hand to ensure that users stored in the database information will not be leaked to the outside for others, on the other hand also to ensure that the user data for the system staff confidential. Finally, we test the system, and the results show that the system can carry out association rules mining under the premise of protecting user privacy, and provide the correlation degree between data, which has certain practical significance and application value.