Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Treiber, Amos"'
Autor:
Cammarota, Rosario, Schunter, Matthias, Rajan, Anand, Boemer, Fabian, Kiss, Ágnes, Treiber, Amos, Weinert, Christian, Schneider, Thomas, Stapf, Emmanuel, Sadeghi, Ahmad-Reza, Demmler, Daniel, Stock, Joshua, Chen, Huili, Hussain, Siam Umar, Riazi, Sadegh, Koushanfar, Farinaz, Gupta, Saransh, Rosing, Tajan Simunic, Chaudhuri, Kamalika, Nejatollahi, Hamid, Dutt, Nikil, Imani, Mohsen, Laine, Kim, Dubey, Anuj, Aysu, Aydin, Hosseini, Fateme Sadat, Yang, Chengmo, Wallace, Eric, Norton, Pamela
In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized inferences to
Externí odkaz:
http://arxiv.org/abs/2008.04449
AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e.g., the European GDPR. Using cryptographic techniques, it is possible to
Externí odkaz:
http://arxiv.org/abs/2002.00801
Autor:
Nautsch, Andreas, Patino, Jose, Treiber, Amos, Stafylakis, Themos, Mizera, Petr, Todisco, Massimiliano, Schneider, Thomas, Evans, Nicholas
Publikováno v:
Proc. Interspeech 2019
In many voice biometrics applications there is a requirement to preserve privacy, not least because of the recently enforced General Data Protection Regulation (GDPR). Though progress in bringing privacy preservation to voice biometrics is lagging be
Externí odkaz:
http://arxiv.org/abs/1907.03454
Autor:
Schneider, Thomas, Treiber, Amos
Publikováno v:
IEEE TPDS 2019
Privacy-preserving scalar product (PPSP) protocols are an important building block for secure computation tasks in various applications. Lu et al. (TPDS'13) introduced a PPSP protocol that does not rely on cryptographic assumptions and that is used i
Externí odkaz:
http://arxiv.org/abs/1906.04862
Autor:
Nautsch, Andreas, Jiménez, Abelino, Treiber, Amos, Kolberg, Jascha, Jasserand, Catherine, Kindt, Els, Delgado, Héctor, Todisco, Massimiliano, Hmani, Mohamed Amine, Mtibaa, Aymen, Abdelraheem, Mohammed Ahmed, Abad, Alberto, Teixeira, Francisco, Matrouf, Driss, Gomez-Barrero, Marta, Petrovska-Delacrétaz, Dijana, Chollet, Gérard, Evans, Nicholas, Schneider, Thomas, Bonastre, Jean-François, Raj, Bhiksha, Trancoso, Isabel, Busch, Christoph
Publikováno v:
In Computer Speech & Language November 2019 58:441-480
Publikováno v:
In Speech Communication November 2019 114:60-71
Autor:
Treiber, Amos
Privacy-Enhancing Technologies (PETs) emerged as a technology-based response to the increased collection and storage of data as well as the associated threats to individuals' privacy in modern applications. They rely on a variety of cryptographic mec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::898e2306de601c905460551fd1ab85d6
http://tuprints.ulb.tu-darmstadt.de/22922/
http://tuprints.ulb.tu-darmstadt.de/22922/
Autor:
Bayerl, Sebastian P, Brasser, Ferdinand, Busch, Christoph, Frassetto, Tommaso, Jauernig, Patrick, Kolberg, Jascha, Nautsch, Andreas, Riedhammer, Korbinian, Sadeghi, Ahmad-Reza, Schneider, Thomas, Stapf, Emmanuel, Treiber, Amos, Weinert, Christian
Publikováno v:
PPML 2019, Privacy Preserving Machine Learning Workshop, CCS 2019 Workshop, November 15, 2019, London, UK
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1093::324db5817c909354f7de8a6ef34105b9
http://www.eurecom.fr/publication/6098
http://www.eurecom.fr/publication/6098
Akademický článek
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