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  • 1.
    Madhusudhanan, Sheema
    et al.
    Department of Computer Science, Indian Institute of Information Technology Kottayam (IIITK), Kottayam, Kerala, India.
    Jose, Arun Cyril
    Department of Computer Science, Indian Institute of Information Technology Kottayam (IIITK), Kottayam, Kerala, India.
    Sahoo, Jayakrushna
    Department of Computer Science, Indian Institute of Information Technology Kottayam (IIITK), Kottayam, Kerala, India.
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    PRIMϵ: Novel Privacy-preservation Model with Pattern Mining and Genetic Algorithm2024In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 19, p. 571-585Article in journal (Refereed)
    Abstract [en]

    This paper proposes a novel agglomerated privacy-preservation model integrated with data mining and evolutionary Genetic Algorithm (GA). Privacy-pReservIng with Minimum Epsilon (PRIMϵ) delivers minimum privacy budget (ϵ) 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. ϵ-allocation by Differential Privacy (DP) is achieved in PRIMϵ with GA PRIMϵ , yielding a quantitative measure of privacy loss (ϵ) 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. PRIMϵ model is verified with Probabilistic Computational Tree Logic (PCTL) and proved to accept DP data only when ϵ ≤ 0.5. The work demonstrated resilience of model against background knowledge, membership inference, reconstruction, and privacy budget attack. PRIMϵ is compared with existing techniques on DP and is found to be linearly scalable with worst time complexity of O(n log n) .

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