PRiD \(\boldsymbol{\varepsilon}\) : A Contextual and Pattern-Aware Model for Digital Twins Using Genetically Optimized Differential Privacy

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.