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IJET - Vol3 Issue 5 Abstract According to the survey Maintaining and preserving privacy has become more significant problem. We consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. IJESRT A SURVEY ON TO PRESERVE PRIVACY FOR COLLABORATIVE DATA PUBLISHING Payal Patel, Sheetal Mehta Master of computer engineering, Parul Instituteof Engineering and Technology, India ABSTRACT The collaborative data publishing issue for anonymizing horizontally partitioned data at various data providers is considered. Genetic grey wolf optimization and C-mixture for The concept of data publishing faces a lot of security issues, indicating that when any trusted organization provides data to a third party, personal information need not be disclosed. Therefore, to maintain the privacy of the data, this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf
IJESRT
Dec 16, 2019 m-PRIVACY FOR COLLABORATIVE DATA PUBLISHING
Comparative Analysis of Privacy Preserving Techniques in
m-PRIVACY FOR COLLABORATIVE DATA PUBLISHING m-PRIVACY FOR COLLABORATIVE DATA PUBLISHING Akanksha Jain, Dr. Dinesh Shrimali JRN Rajasthan Vidhyapeeth University Abstract The collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers is considered. A new type of “insider attack” by colluding data providers who may use M-Privacy For Collaborative Data Publishing - YouTube Apr 05, 2013 Publishing set valued data via m-privacy