Abstract
Social Networks have seen an exponential increase in their user activity. The corresponding user data can be extremely valuable, for improving user experience, identifying trends to make business decisions, and analyzing local and global user behavior. There are concerns over how this data is handled, largely because there are no stringent measures to regulate its handling. Anonymizing user data is vital to ensure the re-identification risk is minimum while retaining as much possible useful information from it. This work proposes a novel method to anonymize user data and ensure it satisfies the three privacy constraints of K-Anonymity (KA), L-Diversity (LD), and T-Closeness (TC), while also keeping information loss from anonymization techniques to the minimum. This dual objective problem is solved through a minimization approach.
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