Proposing a Novel Synergized K-Degree L-Diversity T-Closeness Model for Graph Based Data Anonymization

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Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Big Data
Format: PDF
Privacy becoming a major concern in publishing sensitive information of the individuals in the social network data. A lot of anonymization techniques are evolved to protect sensitive information of individuals. K-anonymity is one of the data anonymization framework for protecting privacy that emphasizes the lemma, every tuple should be different from at least k-1 other tuples in accordance with their Quasi-IDentifiers (QIDs). Researchers have developed privacy models similar to k-anonymity but still label-node relationship is not well protected.
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