PRIM: Novel Privacy-Preservation Model With Pattern Mining and Genetic Algorithm

This paper proposes a novel agglomerated privacy preservation model integrated with data mining and evolutionary Genetic Algorithm (GA). Privacy-pReservIng with Minimum Epsilon (PRIM is an element of) delivers minimum privacy budget (is an element of) value to protect personal or sensitive data during data mining and publication. In this work, the proposed Pattern identification in the Locale of Users with Mining (PLUM) algorithm, identifies frequent patterns from dataset containing users' sensitive data. is an element of-allocation by Differential Privacy (DP) is achieved in PRIM is an element of with GAPRIM is an element of, yielding a quantitative measure of privacy loss (is an element of) ranging from 0.0001 to 0.045. The proposed model maintains the trade-off between privacy and data utility with an average relative error of 0.109 on numerical data and an Earth Mover's Distance (EMD) metric in the range between [0.2,1.3] on textual data. PRIM is an element of model is verified with Probabilistic Computational Tree Logic (PCTL) and proved to accept DP data only when is an element of <= 0.5. The work demonstrated resilience of model against background knowledge, membership inference, reconstruction, and privacy budget attack. PRIM is an element of is compared with existing techniques on DP and is found to be linearly scalable with worst time complexity of O(n log n).