Comparative Analysis of Privacy Preserving Techniques in Distributed Database

In recent years, isolation takes an imperative role to secure the data from various probable attackers. For public advantage data need to be shared as required for Health care and researches, individual privacy is major concern with respect to sensitive information. For that anonymization of data with K-anonymity and L-diversity are studied. Existing system is depends on providers and is used generalization technique for anonymization. This will increase in data loss to avoid this slicing techniques are used. Data publishing is done in such a way that privacy of data should be preserved. While publishing collaborative data to multiple data providers two types of problem occurs, first is outsider attack and second is insider attack. Outsider attack is by the people who are not data providers and insider attack is by colluding data provider who may use their own data records to understand the data records shared by other data providers. The paper focuses on insider attack, and makes some contributions. This problem can be overcome by combining slicing techniques with m-privacy techniques and addition of protocols as secure multiparty computation and trusted third party will increase the privacy of system effectively.