M-Privacy Preserving Synergetic for Data Publishing
Priya V. Mundafale, Prof. Gurudev Sawarkar · International Journal of Computer Trends and Technology · 2014
In this paper, our focus is on the study of Data mining is the extraction of interesting patterns or knowledge from huge amount of data. In recent years, with the explosivedevelopment in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. A number of methods and techniques have been developed for privacy preserving data mining. Privacy preserving data mining is an important issue in the areas of data mining and security on private data in the following scenario: Multiple parties, each having a private data set, want a group of people organized for a joint purpose rule mining without disclosing their private data to other parties. Because of the interactive nature among parties, developing a secure framework to achieve such a computation is both challenging and desirable. There is an increasing need for sharing data repositories containing personal information across multiple distributed, possibly untrusted, and private databases.Such data sharing is subject to constraints imposed by privacy of data subjects as well as data confidentiality of institutions or data providers. We developed a set of decentralized protocols that enable data sharing for horizontally partitioned databases given these constraints.