Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Sébastien Gambs"'
Publikováno v:
Sensors, Vol 22, Iss 11, p 4107 (2022)
A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. How
Externí odkaz:
https://doaj.org/article/f4f7db11c71a418793145dc0131856dc
Publikováno v:
Algorithms, Vol 14, Iss 3, p 87 (2021)
The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial t
Externí odkaz:
https://doaj.org/article/ae136bc2588847ec9f9fb691821f50b6
Autor:
Ghada Arfaoui, Guillaume Dabosville, Sébastien Gambs, Patrick Lacharme, Jean-François Lalande
Publikováno v:
EAI Endorsed Transactions on Mobile Communications and Applications, Vol 2, Iss 5, Pp 1-18 (2014)
The emergence of the NFC (Near Field Communication) technology brings new capacities to the next generation of smartphones, but also new security and privacy challenges. Indeed through its contactless interactions with external entities, the smartpho
Externí odkaz:
https://doaj.org/article/5630a01c546a440a91df7699fd91df10
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 7, Iss 2 (2017)
The massive collection of personal data by personalization systems has rendered the preservation of privacy of individuals more and more difficult. Most of the proposed approaches to preserve privacy in personalization systems usually address this is
Externí odkaz:
https://doaj.org/article/22a0ffcffe384c30b927f0f4fd7d6472
Publikováno v:
Machine Learning
Machine Learning, 2023, Special Issue on Safe and Fair Machine Learning, 112 (6), pp.2131-2192. ⟨10.1007/s10994-022-06191-y⟩
Machine Learning, 2023, Special Issue on Safe and Fair Machine Learning, 112 (6), pp.2131-2192. ⟨10.1007/s10994-022-06191-y⟩
International audience; Unwanted bias is a major concern in machine learning, raising in particular significant ethical issues when machine learning models are deployed within high-stakes decision systems. A common solution to mitigate it is to integ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::caf487966f9bb81f01e523c27d152c5f
https://hal.science/hal-03709547/document
https://hal.science/hal-03709547/document
Publikováno v:
Proceedings of the 10th Workshop on Encrypted Computing & Applied Homomorphic Cryptography.
Autor:
Héber H. Arcolezi, Jean-François Couchot, Sébastien Gambs, Catuscia Palamidessi, Majid Zolfaghari
Publikováno v:
ESORICS 2022-European Symposium on Research in Computer Security
ESORICS 2022-European Symposium on Research in Computer Security, Sep 2022, Copenhague, Denmark. pp.770-775, ⟨10.1007/978-3-031-17143-7_40⟩
Computer Security – ESORICS 2022 ISBN: 9783031171420
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Computer Security – ESORICS 2022
ESORICS 2022-European Symposium on Research in Computer Security, Sep 2022, Copenhague, Denmark. pp.770-775, ⟨10.1007/978-3-031-17143-7_40⟩
Computer Security – ESORICS 2022 ISBN: 9783031171420
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Computer Security – ESORICS 2022
This paper introduces the multi-freq-ldpy Python package for multiple frequency estimation under Local Differential Privacy (LDP) guarantees. LDP is a gold standard for achieving local privacy with several real-world implementations by big tech compa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4130d5d6fa9b4389f1c6f11336cc9e47
https://inria.hal.science/hal-03816212
https://inria.hal.science/hal-03816212
Publikováno v:
Proceedings on Privacy Enhancing Technologies, Vol 2021, Iss 3, Pp 122-141 (2021)
In this work, we propose a novel approach for the synthetization of data based on copulas, which are interpretable and robust models, extensively used in the actuarial domain. More precisely, our method COPULA-SHIRLEY is based on the differentially-p
Publikováno v:
HAL
EDBT 2023-26th International Conference on Extending Database Technology
EDBT 2023-26th International Conference on Extending Database Technology, May 2023, Ioánnina, Greece. pp.512-525, ⟨10.48786/edbt.2023.44⟩
EDBT 2023-26th International Conference on Extending Database Technology
EDBT 2023-26th International Conference on Extending Database Technology, May 2023, Ioánnina, Greece. pp.512-525, ⟨10.48786/edbt.2023.44⟩
Collecting and analyzing evolving longitudinal data has become a common practice. One possible approach to protect the users' privacy in this context is to use local differential privacy (LDP) protocols, which ensure the privacy protection of all use
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3d08d9998470561f20a580cd0816cb0
Publikováno v:
ACM International Conference on Information and Knowledge Management, Virtual Event
ACM International Conference on Information and Knowledge Management, Virtual Event, Nov 2021, Queensland, Australia. pp.4665-4669, ⟨10.1145/3459637.3481965⟩
CIKM
ACM International Conference on Information and Knowledge Management, Virtual Event, Nov 2021, Queensland, Australia. pp.4665-4669, ⟨10.1145/3459637.3481965⟩
CIKM
International audience; FairCORELS is an open-source Python module for building fair rule lists. It is a multi-objective variant of CORELS, a branch-and-bound algorithm to learn certifiably optimal rule lists. FairCORELS supports six statistical fair
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de26886acc217b610b9ec7cce3b679c0
https://hal.laas.fr/hal-03427276/file/2021_CIKM_DemoTrack_FairCORELS.pdf
https://hal.laas.fr/hal-03427276/file/2021_CIKM_DemoTrack_FairCORELS.pdf