Privacy by Design in Digital Twin Ecosystem
Abstract
Privacy-protecting digital twins are also crucial for fields like healthcare, finance, and smart cities, where sharing data in real time makes it harder to stay safe and follow the rules. This study discovered that digital twin ecosystems and innovative methodologies, including AI-driven adaptive privacy models, federated learning, homomorphic encryption, secure multiparty processing (SMPC), and blockchain-based identity management, are employing the Principles of Privacy by Design (PbD). Evaluations of privacy risks and mitigation strategies presently employ real-world datasets, including MIMIC-III for healthcare, Federal Reserve Bank records of transactions for finance, and Waymo’s Open Driving Dataset for autonomous systems. So, at least in some simulated experiments, the main trade-offs between privacy, extra processing power, and system performance have been looked at. The results showed that AI and decentralized security frameworks made information safer without making the systems run slower. But it was still very hard to follow the rules and make things bigger. This study demonstrated the establishment of secure, privacy-centric digital twin ecosystems, enhancing the safety and resilience of real-world deployments.