Zobrazeno 1 - 10
of 192
pro vyhledávání: '"Chatzikokolakis, Konstantinos"'
Quantitative Information Flow (QIF) provides a robust information-theoretical framework for designing secure systems with minimal information leakage. While previous research has addressed the design of such systems under hard constraints (e.g. appli
Externí odkaz:
http://arxiv.org/abs/2411.10059
Conformal Predictors (CP) are wrappers around ML models, providing error guarantees under weak assumptions on the data distribution. They are suitable for a wide range of problems, from classification and regression to anomaly detection. Unfortunatel
Externí odkaz:
http://arxiv.org/abs/2102.03236
Publikováno v:
ACM Trans. Priv. Secur. 25, 3, Article 20 (August 2022), pp. 1-36
A common goal in the areas of secure information flow and privacy is to build effective defenses against unwanted leakage of information. To this end, one must be able to reason about potential attacks and their interplay with possible defenses. In t
Externí odkaz:
http://arxiv.org/abs/2012.12060
Security system designers favor worst-case security metrics, such as those derived from differential privacy (DP), due to the strong guarantees they provide. On the downside, these guarantees result in a high penalty on the system's performance. In t
Externí odkaz:
http://arxiv.org/abs/2011.03396
This paper considers the problem of estimating the information leakage of a system in the black-box scenario. It is assumed that the system's internals are unknown to the learner, or anyway too complicated to analyze, and the only available informati
Externí odkaz:
http://arxiv.org/abs/2005.04399
We consider the problem of obfuscating sensitive information while preserving utility, and we propose a machine learning approach inspired by the generative adversarial networks paradigm. The idea is to set up two nets: the generator, that tries to p
Externí odkaz:
http://arxiv.org/abs/1904.01059
Publikováno v:
In 2019 IEEE Symposium on Security and Privacy (SP), pp. 835-852. IEEE, 2019
We consider the problem of measuring how much a system reveals about its secret inputs. We work under the black-box setting: we assume no prior knowledge of the system's internals, and we run the system for choices of secrets and measure its leakage
Externí odkaz:
http://arxiv.org/abs/1902.01350
Local differential privacy (LPD) is a distributed variant of differential privacy (DP) in which the obfuscation of the sensitive information is done at the level of the individual records, and in general it is used to sanitize data that are collected
Externí odkaz:
http://arxiv.org/abs/1805.01456
Publikováno v:
Entropy 2018, 20(5), 382
In the inference attacks studied in Quantitative Information Flow (QIF), the attacker typically tries to interfere with the system in the attempt to increase its leakage of secret information. The defender, on the other hand, typically tries to decre
Externí odkaz:
http://arxiv.org/abs/1803.10042
Publikováno v:
Proceedings of the 7th International Conference on Principles of Security and Trust (POST 2018), Lecture Notes in Computer Science, Vol.10804, pp. 134-159, 2018
In the inference attacks studied in Quantitative Information Flow (QIF), the adversary typically tries to interfere with the system in the attempt to increase its leakage of secret information. The defender, on the other hand, typically tries to decr
Externí odkaz:
http://arxiv.org/abs/1802.10465