抽象的

Enhanced M-Privacy for Collaborative Data Publishing

Aseema Jana, Shubham Joshi

In recent years, privacy takes an important role to secure the data from various probable attackers. When for public advantage data need to be shared as required for Health care and researches, individual privacy is major concern regarding sensitive information. So while publishing such data, privacy should be conserved .While publishing collaborative data to multiple data provider’s 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 mprivacy techniques and addition of protocols as secure multiparty computation and trusted third party will increase the privacy of system effectively.