Data Attribute Security For High Dimensional Data Sets.

Authors: Harsha A. Chaudhari, Prof. Chhaya Nayak

Certificate: View Certificate

Abstract

In the recent year, the privacy takes major role to secure the data from various potential attackers. While publishing collaborative data to multiple data provider’s two types of problem arises, 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. In the proposed approach problem can be resolved by using different approaches as m-privacy, which is a technique which guarantees that the anonymized data satisfies a given privacy constraint against any group of up to m-colluding dataproviders. Second, Heuristic algorithms is also exploiting the equivalence group monotonicity of privacy constraints and adaptive ordering techniques for efficiently checking m-privacy given a set of records. Data provider aware anonymization algorithm with adaptive m- privacy checking strategy is to ensure high utility and m-privacy of anonymized data with efficiency. Privacy for collaborative data publishing can further enhanced by combining techniques of m-privacy with Slicing techniques. And by using secure protocols as trusted-third party(TTP), Secure multiparty computation(SMC) or enhancement in the protocol security can be done effectively. Keywords:- M–privacy, L-diversity, Data Anonymization, Slicing, Bucketization..

Introduction

Privacy-preserving publishing of micro data has been studied extensively in recent years. Micro data contain records each of which contains information about an individual entity, such as a person, a household, or an organization. Several micro data anonymization techniques have been proposed. Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving micro data publishing

Conclusion

We consider a potential attack on collaborative data publishing. We used slicing algorithm for anonymization and L diversity and verify it for security and privacy by using binary algorithm of data privacy. This proposed system help to improve the data privacy and security when data is athered from different sources and output should be in collaborative fashion. Slicing algorithm is very useful when we are using high dimensional data. It divides data in both vertical and horizontal fashion. Due to encryption we can increase security. But the limitation is there could be loss of data utility. Above system can used in many applications like hospital management system, many industrial areas where we like to protect a sensitive data like salary of employee. This proposed system help to improve the data privacy and security when data is gathered from different sources and output should be in collaborative fashion.