Identity Disclosure Protection in Dynamic Networks Using K

The data mining figures out accurate information for requesting user after the raw data is analyzed. Among lots of developments, data mining face hot issues on security, privacy and integrity. Data mining use one of the latest technique called privacy preserving data publishing (PPDP), which enforces security for the digital information provided by governments, corporations, companies and individuals in social networks. People become embarrassed when adversary tries to know the sensitive information shared. Sensitive information is gathered through the vertex and multi community identities of the user. Vertex identity denotes the self-information of user like name, address, mobile number, etc. Multi community identity denotes the community group in which the user participates. To prevent such identity disclosures, this paper proposes K W -structural diversity anonymity technique, for the protection of vertex and multi community identity disclosure. In K W -structural diversity anonymity technique, k is privacy level applied for users and W is an adversary monitoring time.