Sheema Madhusudhanan

As privacy concerns intensify in data-driven systems, this article presents PriMa $\chi $ , a hybrid framework that combines a genetic algorithm (GA) and reinforcement learning (RL) to optimize the privacy–utility tradeoff in differential privacy (DP) through explicit adaptive privacy–utility control. PriMa $\chi $ adaptively selects perturbation configurations to minimize the privacy budget $(\varepsilon)$ while preserving data utility $({\mathcal {U}})$ . To support verifiable privacy-preserving analytics in decentralized environments, we further integrate PriMa $\chi $ with a privacy-aware smart-contract framework that enables on-chain DP enforcement and zero-knowledge proof (ZKP) verification. The framework supports structured, transactional, and spatiotemporal workloads, including decentralized finance, electronic health records, census analytics, and location services. An interleaved Petri net model is used to formally verify privacy-aware state transitions in the smart-contract workflow. Experimental results show that PriMa $\chi $ achieves utility of at least 80% under dataset-dependent privacy budgets in the range $0.003 \leq \varepsilon~\leq\unicode{0x0142}.43$ , while also effectively mitigating model-extraction, membership-inference, and privacy-budget-exhaustion attacks. These results demonstrate that PriMa $\chi $ provides adaptive, auditable, and practically deployable privacy protection for decentralized analytics.

The increasing adoption of Digital Twins (DT) driven by the Internet of Things (IoT) in critical domains such as healthcare, smart energy, and mobility introduces unprecedented privacy risks due to continuous data collection, contextual sensitivity, and user traceability. We propose PRivacy in DT with minimum privacy budget ( \(\varepsilon\) ), PRiD \(\varepsilon\) , a context- and pattern-aware, genetically optimized adaptive Differential Privacy (DP) model to secure DT through a modular four-layer architecture. PRiD \(\varepsilon\) combines contextual sensitivity estimation, domain-specific heuristics, and genetic noise injection to achieve adaptive per-pattern DP guarantees. It integrates Federated Learning (FL), dynamically tuning \(\varepsilon\) across local nodes based on sensitivity feedback and evolving model requirements. A privacy-sensitive access control mechanism regulates query responses by role, budget, and pattern-level risk. Evaluations across healthcare, smart energy, and mobility demonstrate high utility ( \(>\) 95%) at low \(\varepsilon\in[0.1,0.35]\) , and strong resilience against reconstruction, inference, and \(\varepsilon\) -variation exploitation attacks. PRiD \(\varepsilon\) supports scalable, privacy-preserving DT modeling, with theoretical analysis and empirical benchmarking indicating an overall worst-case complexity of \(\mathcal{O}(n\log n)\) under the proposed pipeline.

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).