A Review on Privacy Threats & Privacy Preserving Techniques: KAnonymity and Perturbation

Abstract

Privacy is one of the biggest concerns nowadays. A lot of information is daily shared by the people
with an explosive development of technology in the field of internet, storage and processing.
Dynamicity of networking areas like social network and web data, being unaware of the prior
knowledge of attacker are under a continuous threat and this is an open challenge. The information
shared by people when published gives rise to a serious privacy threat, as this data contains sensitive
information (e.g. individual’s interests, financial data, medical data, demographics and so on) which
could be used by an adversary or by any other third party personals. Many Privacy Preserving Data
Mining (PPDM) techniques are being used for data privacy and security but the success and
improvement in one parameter should not compromise the privacy and utility of others. In this paper,
we have analyzed the major threats towards the kind of data to the protected and then gave a general
review on privacy preserving techniques like 'k-anonymity' and 'Perturbation'. We have given a
comparison of the privacy preserving techniques based on their characteristics and challenges. We
hope to develop efficient methods of privacy preservation ahead in terms of stronger privacy and
better data utility.