Articulation Point Based Quasi Identifier Detection for Privacy Preserving in Distributed Environment

These days, huge data size requires high-end resources to be stored in IT organizations premises. They depend on cloud for additional resource necessities. Since cloud is a third-party, we cannot guarantee high security for our information as it might be misused. This necessitates the need of privacy in data before sharing to the cloud. Numerous specialists proposed several methods, wherein they attempt to discover explicit identifiers and sensitive data before distributing it. But, quasi-identifiers are attributes which can spill data of explicit identifiers utilizing background knowledge. Analysts proposed strategies to find quasi- identifiers with the goal that these properties can likewise be considered for implementing privacy. But, these techniques suffer from many drawbacks like higher time consumption and extract more quasi identifiers which decreases data utility. The proposed work overcomes this drawback by extracting minimum required quasi attributes with minimum time complexity

Improved Technique for Preserving Privacy while Mining Real Time Big Data

With the evolution of Big data, data owners require the assistance of a third party (e.g.,cloud) to store, analyse the data and obtain information at a lower cost. However, maintaining privacy is a challenge in such scenarios. It may reveal sensitive information. The existing research discusses different techniques to implement privacy in original data using anonymization, randomization, and suppression techniques. But those techniques are not scalable, suffers from information loss, does not support real time data and hence not suitable for privacy preserving big data mining. In this research, a novel approach of two level privacy is proposed using pseudonymization and homomorphic encryption in spark framework. Several simulations are carried out on the collected dataset. Through the results obtained, we observed that execution time is reduced by 50%, privacy is enhanced by 10%. This scheme is suitable for both privacy preserving Big Data publishing and mining