Абстрактный

Data Preserving By Anonymization Techniques for Collaborative Data Publishing

R. Indhumathi, S. Mohana Priya

This paper mainly deals with the issue of privacy preserving in data mining while collaborating n number of parties and trying to maintain confidentiality of all data providers details while collaborating their database. Here two type of attacks are addressed “insider attack” and “outsider attack”. In insider attack, the data providers use their own records and try to retrieve other data provider details. Formal protection model k-Anonymity, l-diversity, t-closeness are used to protect privacy. Here notion of m-privacy algorithm is used to maintain privacy and secure multiparty computation protocol can also be used for privacy preserving.