Allam Naga Sudha Madhuri, S. Mabjan
Data privacy, data security, data publishing, privacy leakages, slicing
In recent years, privacy preserving has seen rapid growth which leads to an increase in the capability to store and retrieve personal dataset without revealing the sensitive information about the individuals. Different techniques have been proposed to improve accuracy in a huge sourcing database. Anonimization techniques such as, generalization are designed for improving accuracy in privacy preserving method. But the malicious workers can hack the private information of the user and misuse it. Recent work has shown that anonymity for generalization loses significant amounts of information,especially for data of higher dimensionality. Collecting and publishing large amounts of individuals’ data to public for purposes such as medical research, market analysis and economical measures has increased major privacy concerns about individual’s sensitive information. Many Privacy-Preserving Data Publishing (PPDP) techniques have been proposed in literature to act. But the result is no proper privacy characterization and measurement. In this paper we introduce a novel technique called overlapped slicing, which partitions the data in both horizontal and vertical. Slicing preserves better data utility than generalization techniques. As an extension we proposed a technique called random attribute slicing, in which an attribute is divided into more than one column. Important advantage of this work is to handle high-dimensional data and also preserves better privacy than the previous techniques
Article Details
Unique Paper ID: 153860

Publication Volume & Issue: Volume 8, Issue 9

Page(s): 68 - 71
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