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Refinement Orders for Quantitative Information Flow and Differential Privacy.

, , and . J. Cybersecur. Priv., 1 (1): 40-77 (December 2020)

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Bisimulation for Demonic Schedulers., , and . FoSSaCS, volume 5504 of Lecture Notes in Computer Science, page 318-332. Springer, (2009)Refinement Orders for Quantitative Information Flow and Differential Privacy., , and . J. Cybersecur. Priv., 1 (1): 40-77 (December 2020)Information Leakage Games: Exploring Information as a Utility Function., , , and . CoRR, (2020)Exact Optimization of Conformal Predictors via Incremental and Decremental Learning., , und . ICML, Volume 139 von Proceedings of Machine Learning Research, Seite 1836-1845. PMLR, (2021)Maritime Reporting Systems., , und . Guide to Maritime Informatics, Springer, (2021)Comparing Systems: Max-Case Refinement Orders and Application to Differential Privacy., , und . CSF, Seite 442-457. IEEE, (2019)Refinement Metrics for Quantitative Information Flow., und . The Art of Modelling Computational Systems, Volume 11760 von Lecture Notes in Computer Science, Seite 397-416. Springer, (2019)Optimal Obfuscation Mechanisms via Machine Learning., , und . CSF, Seite 153-168. IEEE, (2020)Implementing clustering for vehicular ad-hoc networks in ns-3., , und . WNS3, Seite 25-31. ACM, (2015)F-BLEAU: Fast Black-Box Leakage Estimation., , und . IEEE Symposium on Security and Privacy, Seite 835-852. IEEE, (2019)