SOCIAL NETWORKING WITH PROTECTING SENSITIVE LABELS USING ANONYMIZATION METHODOLOGY
Keywords:
Anonymization, Noise node, KDLDAbstract
The use of social network sites goes on increasing day by day e.g. wiki vote, live journal social network, twitter, LinkedIn network. By victimization these social networks, users get a lot of helpful data of alternative user’s like the user performance, non-public growth, spread of sickness, salaries etc. it's additionally vital that user’s non-public data shouldn't get reveal. Thus, nowadays it's essential to safeguard user’s privacy and utilization of social network information. the bulk of developer developed privacy models like K-anonymity for protecting node or vertex reidentification in structure data. User’s privacy models get forced by alternative user, if a bunch of node for the most part shares similar sensitive labels then new users simply establish recent user’s information, so solely structure anonymization technique isn't entirely protected or helpful.
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