A project using digital twins to develop statistical models with disclosure control for private information, as well as and the design GDPR-compliant digital twins. <\/p>\n\n\n\n
In this project we study the efficient algorithmic design of trustworthy digital twins for smart buildings. The goal is to develop the design methodology that results in statistical models where the disclosure of private information is controlled with analytical guarantees and the model is representative for the application in smart buildings such as HVAC monitoring or advanced energy management. <\/p>\n\n\n\n
We will also explore how to design digital twins that are compliant with GDPRs data minimization principle. The research will build on previous research on privacy-preserving energy management design (see figures below, data collected in KTH Live-in Lab, [ACCESS, TIFS]), digital twins for HVAC systems, cross-disciplinary research on law and technology [FIU], and our recently proposed privacy measure called pointwise maximal leakage [PML]. <\/p>\n\n\n\n
[ACCESS] R. Reddy, T. J. Oechtering, and D. M\u00e5nsson, \u201cAdversarial Inference control in Cyber-Physical Systems: A Bayesian Approach with Application to Smart Meters,\u201d in IEEE ACCESS 2024.
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[TIFS] ] Y. You, Z. Li, and T. J. Oechtering, \u201cEnergy Management Strategy for Smart Meter Privacy and Cost Saving,\u201d in IEEE Transactions Information Forensics & Security, vol. 16, pp. 1522-1537, 2021.
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[FIU] T. J. Oechtering, S. Saeidian, and C. Magnusson-Sj\u00f6berg, \u201cCalculated Privacy: Tech Meets Law & Law Meets Tech,\u201d in FIU Law Review, 17(2), Jan 2024.
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[PML] S. Saeidian, G. Cervia, T. J. Oechtering, and M. Skoglund, \u201cPointwise Maximal Leakage,\u201d in IEEE Transactions Information Theory, vol. 69, no. 12, pp. 8054\u20138080, Dec. 2023. <\/p>\n\n\n\n
![\"Tobias](\"\/wp-content\/uploads\/sites\/21\/2024\/03\/T-oechtering_800x800.jpg\")
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