Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Aurélien Bellet"'
Autor:
Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet, Nathalie Vauquier, Emmanuel Vincent, Mohamed Maouche, Marc Tommasi, Nicolas Papernot
Publikováno v:
Proceedings on Privacy Enhancing Technologies
Proceedings on Privacy Enhancing Technologies, 2023, 2023 (1)
Proceedings on Privacy Enhancing Technologies, 2023, 2023 (1), ⟨10.48550/arXiv.2202.11823⟩
Proceedings on Privacy Enhancing Technologies, 2023, 2023 (1)
Proceedings on Privacy Enhancing Technologies, 2023, 2023 (1), ⟨10.48550/arXiv.2202.11823⟩
International audience; Sharing real-world speech utterances is key to the training and deployment of voice-based services. However, it also raises privacy risks as speech contains a wealth of personal data. Speaker anonymization aims to remove speak
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c43fc58d17be944b82095e3f6520e3b5
https://inria.hal.science/hal-03588932
https://inria.hal.science/hal-03588932
Publikováno v:
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2022, 6 (3), pp.1-27. ⟨10.1145/3550302⟩
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2022, 6 (3), pp.1-27. ⟨10.1145/3550302⟩
Recommender systems are proving to be an invaluable tool for extracting user-relevant content helping users in their daily activities (e.g., finding relevant places to visit, content to consume, items to purchase). However, to be effective, these sys
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac44ddf5ff1aee0232b32e989a65da4e
http://hdl.handle.net/20.500.12210/80075
http://hdl.handle.net/20.500.12210/80075
Autor:
Kuan Liu, Aurélien Bellet
Publikováno v:
Neurocomputing
Neurocomputing, 2019, 333, pp.185-199. ⟨10.1016/j.neucom.2018.12.060⟩
Neurocomputing, Elsevier, 2019, 333, pp.185-199. ⟨10.1016/j.neucom.2018.12.060⟩
Neurocomputing, 2019, 333, pp.185-199. ⟨10.1016/j.neucom.2018.12.060⟩
Neurocomputing, Elsevier, 2019, 333, pp.185-199. ⟨10.1016/j.neucom.2018.12.060⟩
Similarity and metric learning provides a principled approach to construct a task-specific similarity from weakly supervised data. However, these methods are subject to the curse of dimensionality: as the number of features grows large, poor generali
Publikováno v:
Journal of Computational Biology
Journal of Computational Biology, 2021, 28 (5), pp.435-451. ⟨10.1089/cmb.2020.0445⟩
24th International Conference On Research In Computational Molecular Biology (RECOMB 2020)
24th International Conference On Research In Computational Molecular Biology (RECOMB 2020), 2020, Virtual, Italy. ⟨10.1101/2020.01.15.907808⟩
Journal of Computational Biology, Mary Ann Liebert, 2021, 28 (5), pp.435-451. ⟨10.1089/cmb.2020.0445⟩
RECOMB
Journal of Computational Biology, 2021, 28 (5), pp.435-451. ⟨10.1089/cmb.2020.0445⟩
24th International Conference On Research In Computational Molecular Biology (RECOMB 2020)
24th International Conference On Research In Computational Molecular Biology (RECOMB 2020), 2020, Virtual, Italy. ⟨10.1101/2020.01.15.907808⟩
Journal of Computational Biology, Mary Ann Liebert, 2021, 28 (5), pp.435-451. ⟨10.1089/cmb.2020.0445⟩
RECOMB
Some organisations like 23andMe and the UK Biobank have large genomic databases that they re-use for multiple different genome-wide association studies (GWAS). Even research studies that compile smaller genomic databases often utilise these databases
Autor:
Lie He, Sebastian U. Stich, Mariana Raykova, Phillip B. Gibbons, Mehryar Mohri, David Evans, Badih Ghazi, Felix X. Yu, Sen Zhao, Jianyu Wang, Zheng Xu, Weikang Song, Prateek Mittal, Ramesh Raskar, Zachary Garrett, Farinaz Koushanfar, H. Brendan McMahan, Ayfer Ozgur, Mikhail Khodak, Rafael G. L. D'Oliveira, Jakub Konecní, Aurélien Bellet, Arjun Nitin Bhagoji, Hubert Eichner, Han Yu, Adrià Gascón, Ananda Theertha Suresh, Sanmi Koyejo, Praneeth Vepakomma, Josh Gardner, Chaoyang He, Florian Tramèr, Tancrède Lepoint, Salim El Rouayheb, Peter Kairouz, Li Xiong, Kallista Bonawitz, Rasmus Pagh, Tara Javidi, Mehdi Bennis, Dawn Song, Martin Jaggi, Zhouyuan Huo, Hang Qi, Gauri Joshi, Qiang Yang, Richard Nock, Yang Liu, Brendan Avent, Justin Hsu, Rachel Cummings, Graham Cormode, Marco Gruteser, Aleksandra Korolova, Ziteng Sun, Zaid Harchaoui, Ben Hutchinson, Zachary Charles, Daniel Ramage
Publikováno v:
Foundations and Trends in Machine Learning
Foundations and Trends in Machine Learning, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, Now Publishers, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, Now Publishers, 2021, 14 (1-2), pp.1-210
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b1ccc10027ba1ce68ce0210510e8bdc
https://inria.hal.science/hal-02406503v2/document
https://inria.hal.science/hal-02406503v2/document
Autor:
Aurélien Bellet, Emmanuel Vincent, Brij Mohan Lal Srivastava, Nathalie Vauquier, Marc Tommasi, Mohamed Maouche
Publikováno v:
INTERSPEECH 2020
INTERSPEECH 2020, Oct 2020, Shanghai, China
INTERSPEECH
INTERSPEECH 2020, Oct 2020, Shanghai, China
INTERSPEECH
International audience; Speech anonymization techniques have recently been proposed for preserving speakers' privacy. They aim at concealing speak-ers' identities while preserving the spoken content. In this study, we compare three metrics proposed i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a667654df73ddb00ec8d68544c048d31
https://inria.hal.science/hal-02907918/file/anonymization_metrics_IS2020.pdf
https://inria.hal.science/hal-02907918/file/anonymization_metrics_IS2020.pdf
Autor:
Xin Wang, Junichi Yamagishi, Brij Mohan Lal Srivastava, Marc Tommasi, Emmanuel Vincent, Aurélien Bellet, Natalia A. Tomashenko, Mohamed Maouche
Publikováno v:
INTERSPEECH 2020
INTERSPEECH 2020, International Speech Communication Association (ISCA), Oct 2020, Shanghai, China
INTERSPEECH
INTERSPEECH 2020, International Speech Communication Association (ISCA), Oct 2020, Shanghai, China
INTERSPEECH
International audience; The recently proposed x-vector based anonymization scheme converts any input voice into that of a random pseudo-speaker. In this paper, we present a flexible pseudo-speaker selection technique as a baseline for the first Voice
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4a430637e6c012d0314c9b2fd9a46d1
https://hal.archives-ouvertes.fr/hal-02610447v2/document
https://hal.archives-ouvertes.fr/hal-02610447v2/document
Publikováno v:
Machine Learning
Machine Learning, 2022, ⟨10.1007/s10994-022-06267-9⟩
Machine Learning, 2022, ⟨10.1007/s10994-022-06267-9⟩
Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the computation in the presence of malicious parties. We tackle these challenges
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79726b40f4624477aa6c3342d94dbec5
http://arxiv.org/abs/2006.07218
http://arxiv.org/abs/2006.07218
Autor:
Emmanuel Vincent, Brij Mohan Lal Srivastava, Sahidullah, Marc Tommasi, Aurélien Bellet, Nathalie Vauquier
Publikováno v:
ICASSP 2020-45th International Conference on Acoustics, Speech, and Signal Processing
ICASSP 2020-45th International Conference on Acoustics, Speech, and Signal Processing, IEEE Signal Processing Society, May 2020, Barcelona, Spain
ICASSP 2020-45th International Conference on Acoustics, Speech, and Signal Processing, IEEE Signal Processing Society, May 2020, Barcelona, Spain. pp.2802-2806
ICASSP
ICASSP 2020-45th International Conference on Acoustics, Speech, and Signal Processing, IEEE Signal Processing Society, May 2020, Barcelona, Spain
ICASSP 2020-45th International Conference on Acoustics, Speech, and Signal Processing, IEEE Signal Processing Society, May 2020, Barcelona, Spain. pp.2802-2806
ICASSP
International audience; Speech data conveys sensitive speaker attributes like identity or accent. With a small amount of found data, such attributes can be inferred and exploited for malicious purposes: voice cloning, spoofing, etc. Anonymization aim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00b68393fa49055f11efcaef345b1514
https://hal.inria.fr/hal-02355115v2/document
https://hal.inria.fr/hal-02355115v2/document
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030452568
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b3020c333a50e10fa9365e2ba21c400
https://doi.org/10.1007/978-3-030-45257-5_32
https://doi.org/10.1007/978-3-030-45257-5_32