Armored Twins: Flexible Privacy Protection for Digital Twins through Conditional Proxy Re-Encryption and Multi-Party Computation

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Digital twins, i.e., up-to-date digital copies of a physical object maintained in the cloud, make it possible to conveniently review a physical object’s state, indirectly interact with the physical object, or perform computations on the object’s state and history – also in combination with data from other digital twins. The concept of digital twins has seen wide uptake in Internet of Things use cases, e.g., in manufacturing to monitor a product’s lifecycle, or precision medicine to provide personalized treatment. Besides these benefits, challenges arise, especially if the involved data producers, clouds and data consumers are not in the same trusted domain: Who owns and controls the data? Are the parties (e.g., cloud) sufficiently trusted to handle privacy-sensitive data?

In this work, we propose ArmoredTwins, i.e., a system for digital twins that protects the confidentiality of digital twin data while providing flexible and fine-grained sharing by employing key-policy conditional proxy re-encryption to enable processing on subsets of the data. Alternatively, to support computation on very sensitive data, our system integrates secure multi-party computation, which does not reveal the data items to the individual nodes performing the computation. Benchmarks of our implementation highlight the system’s feasibility and practical performance.
Original languageEnglish
Title of host publication18th International Conference on Security and Cryptography (SECRYPT 2021)
Publication statusAccepted/In press - 2021
EventSECRYPT 2021: 18th International Conference on Security and Cryptography - Online, Virtuell
Duration: 6 Jul 20218 Jul 2021
Conference number: 18


ConferenceSECRYPT 2021
Internet address

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