Privacy Preserving Horizontal Partitioning of Outsourced Database for Frequent Pattern Mining Using Paillier
Manasi Dhage, Seema V. Kedar · 2017
In recent years data sharing is an important task. This data publication is conducted by various organizations under some important rules and regulations. Such data is useful in various researchers. Also With high demand for cloud services there are serious concerns about the privacy of individuals and also the outsourced database. In such cases there is high demand to check the utility of data and integrity of data. Data integration is another form of data sharing, where data owners sends there data to server for aggregation and then performed preprocessing on aggregated data before storing on third party server or cloud. There are several challenges while designing secure system like hiding the original and sensitive information of the individual and whole database from attacker. So in this system to tackle these challenges, the existing system uses the technique of homomorphic encryption which works on encrypted data, which results in increased security of outsourced data and also increased system performance. In proposed system, we are adding the concept of horizontal partitioning to spilt the database in two different parts as well as apply the rule generation algorithm. By making use of horizontal partition we are able to save the time needed for rule generation.