DySan: Dynamically Sanitizing Motion Sensor Data Against Sensitive Inferences through Adversarial Networks-Reference-Cited by-同舟云学术

DySan: Dynamically Sanitizing Motion Sensor Data Against Sensitive Inferences through Adversarial Networks

Published:2021-05-24 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security
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Author:

Boutet Antoine¹,Frindel Carole²,Gambs Sébastien³,Jourdan Théo²,Ngueveu Rosin Claude³

Affiliation:

1. Univ Lyon, INSA Lyon, Inria, CITI, Lyon, France

2. Univ Lyon, INSA Lyon, CNRS, Inserm, CREATIS UMR 5220, Lyon, France

3. Université du Québec à Montréal, Montréal, PQ, Canada

Funder

Canada Research Chair program

FQRNT Program AUDACE

NSERC Discovery Grant

Office of the Privacy Commissioner of Canada

ANR (Agence Nationale de la Recherche)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3433210.3453095

Reference38 articles.

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2. SenseGen: A deep learning architecture for synthetic sensor data generation

3. C. BenAbdelkader R. Cutler and L. Davis. 2002. Stride and cadence as a biometric in automatic person identification and verification. In IEEE FGR. 372--377. C. BenAbdelkader R. Cutler and L. Davis. 2002. Stride and cadence as a biometric in automatic person identification and verification. In IEEE FGR. 372--377.

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