A sanitization approach for privacy preserving data mining on social distributed environment

Abstract

Data owners worry about their private data in the information that is being uncovered without authorization in the cloud computing environment. While applying privacy preserving methods to the data, the data owners attempt to retain the knowledge inside the data. One approach to solve this problem is the concept of distributed databases where different parties have horizontal or vertical partitions of the data. Cluster analysis is a frequently used data mining task which aims at decomposing or partitioning a usually multivariate data set into groups such that the data objects in one group are more similar to each other. While using encryption based kernel k-means algorithm, large data’s can’t be encrypted in the distributed environment. To extend the privacy concept, a novel method based Privacy Preserving Distributed Data Mining is planned. According to this, a sanitization approach will be developed to improve the privacy of the user data. In sanitization process, a privacy based objective function will be developed and an optimal key will be generated based on the proposed objective function. Here artificial bee colony algorithm will be utilized for optimal key generation and large amount of data can be encrypted. Once the sanitization process is done, the sanitized information will be updated to service provider by the helper user for each cluster. Finally, the experimentation will be carried out with existing database to prove the efficiency of the proposed algorithm. The implementation will be done in JAVA using cloud simulator. Extensive execution assessments and security analysis exhibit the legitimacy and efficiency of the proposed technique.