Techniques for privacy preserving data sharing: A survey

Abstract

Data mining is a technique where massive amounts of both sensitive and non-sensitive data are collected and examined. While distributing such private data, privacy preserving becomes an important issue. Various methods and techniques have been introduced in privacy preserving data mining to undertake this problem. The main intention of privacy preserving is to extract the knowledge without disclosing private data and it also concerns about the sequential release of data. Sequential data helps in predicting the next occurrence which leads to violating the privacy of individual data. In this paper, we briefly surveyed sequential pattern hiding, k-anonymity, data perturbation and secure sum computation techniques to address the issues of privacy preserving data sharing.