Multiparty privacy preserving data mining for vertically partitioned data
Rashmi Wandile, Neha Unavane, Yogesh Sangekar, Dhanraj Kachole · International journal of advance research and innovative ideas in education · 2018
The field of privacy pursues rapid advances in recent years because of the increases in the ability to store data. One of the most important topics in research community is Privacy preserving data mining (PPDM). Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. People today have become well aware of the privacy intrusions of their sensitive data and are very reluctant to share their information. The major area of concern is that non-sensitive data even may deliver sensitive information, including personal information, facts or patterns. In this paper we demonstrate how the different departments of same organization combine their data without harming the privacy of the client. Then we use this data for making effective decisions in efficient and accurate manner. Data is said to be vertically partitioned when several organizations own different attributes of information for the same set of entities.