Privacy Preserving Access of Outsourced Data in Heterogeneous Databases

- Privacy is main concern in the world, among present technological phase. Information security has become a dangerous issue since the information sharing has a common need. Recently, privacy issues have been increased enormously when internet is flourishing with forums, social media, blogs and e-commerce, etc. Hence research area is retaining privacy in data mining. The sensitive data of the data owners should not be known to the third parties and other data owners. To make it efficient, the horizontal partitioning is done on the heterogeneous databases is introduced to improve privacy and efficiency. we address the major issues of privacy preservation in information mining. In particular, we consider to provide protection between different data owners and to give privacy between them by partitioning the databases horizontally and the data2019;s are available in the heterogeneous databases. Our proposed work is to center around the study of security saving on unknown databases and conceiving private refresh methods to database frameworks that backings thoughts of obscurity assorted than k-secrecy. Symmetric homomorphic encryption scheme, which is significantly more efficient than the asymmetric schemes. Our proposed work helps the valid user can extract with key issue in partition data in automated approach and the data2019;s are partitioned horizontally

A Privacy-Preserving Framework for Collaborative Association Rule Mining in Cloud

Collaborative Data Mining facilitates multiple organizations to integrate their datasets and extract useful knowledge from their joint datasets for mutual benefits. The knowledge extracted in this manner is found to be superior to the knowledge extracted locally from a single organization’s dataset. With the rapid development of outsourcing, there is a growing interest for organizations to outsource their data mining tasks to a cloud environment to effectively address their economic and performance demands. However, due to privacy concerns and stringent compliance regulations, organizations do not want to share their private datasets neither with the cloud nor with other participating organizations. In this paper, we address the problem of outsourcing association rule mining task to a federated cloud environment in a privacy-preserving manner. Specifically, we propose a privacy-preserving framework that allows a set of users, each with a private dataset, to outsource their encrypted databases and the cloud returns the association rules extracted from the aggregated encrypted databases to the participating users. Our proposed solution ensures the confidentiality of the outsourced data and also minimizes the users’ participation during the association rule mining process. Additionally, we show that the proposed solution is secure under the standard semi-honest model and demonstrate its practicality

Association rule hiding using integer linear programming

Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database