PRIMϵ: 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 $\epsilon $ ) delivers minimum privacy budget ( $\epsilon $ ) 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. $\epsilon $ -allocation by Differential Privacy (DP) is achieved in PRIM $\epsilon $ with GA $_{\textbf {PRIM$\epsilon $}}$ , yielding a quantitative measure of privacy loss ( $\epsilon $ ) 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 $\epsilon $ model is verified with Probabilistic Computational Tree Logic (PCTL) and proved to accept DP data only when $\epsilon \le 0.5$ . The work demonstrated resilience of model against background knowledge, membership inference, reconstruction, and privacy budget attack. PRIM $\epsilon $ is compared with existing techniques on DP and is found to be linearly scalable with worst time complexity of $\mathcal {O}$ (n log n).