Technologies améliorant la confidentialité

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Publications associées (30)

VOTEAGAIN: A scalable coercion-resistant voting system

Carmela González Troncoso, Wouter Lueks

The strongest threat model for voting systems considers coercion resistance: protection against coercers that force voters to modify their votes, or to abstain. Existing remote voting systems either do not provide this property; require expensive operation ...

USENIX ASSOC2020

Private and Secure Distributed Learning

Georgios Damaskinos

The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...

EPFL2020

A Truthful, Privacy-Preserving, Approximately Efficient Combinatorial Auction For Single-minded Bidders

Boi Faltings, Sujit Prakash Gujar, Sankarshan Damle

Combinatorial auctions are widely used to sell resources/items. The challenges in such auctions are multi-fold. We need to ensure that bidders, the strategic agents, bid their valuations truthfully to the auction mechanism. Besides, the agents may desire p ...

ASSOC COMPUTING MACHINERY2019

Generating Artificial Data for Private Deep Learning

Boi Faltings, Aleksei Triastcyn

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy for the original dataset. We use generative adversarial networks to draw privacy-preserving artificial data samples and d ...

2019

Privacy-Enhancing Technologies for Mobile Applications and Services

Thi Van Anh Pham

Over a third of the world 's population owns a smartphone. As generic computing devices that support a large and heterogeneous collection of mobile applications (apps), smartphones provide a plethora of functionalities and services to billions of users. B ...

EPFL2019

MedCo: Enabling Secure and Privacy-Preserving Exploration of Distributed Clinical and Genomic Data

Jean-Pierre Hubaux, Bryan Alexander Ford, Olivier Michielin, Juan Ramón Troncoso-Pastoriza, Jean Louis Raisaro, Joao André Gomes de Sá e Sousa, Mickaël Misbach

The increasing number of health-data breaches is creating a complicated environment for medical-data sharing and, consequently, for medical progress. Therefore, the development of new solutions that can reassure clinical sites by enabling privacy-preservin ...

2019

Invited Paper: The Applications of Machine Learning in Privacy Notice and Choice

Hamza Harkous, Kassem Fawaz

For more than two decades since the rise of the World Wide Web, the "Notice and Choice" framework has been the governing practice for the disclosure of online privacy practices. The emergence of new forms of user interactions, such as voice, and the enforc ...

IEEE2019

Rethinking Location Privacy for Unknown Mobility Behaviors

Carmela González Troncoso, Simón Oya Diez

Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their designs and evaluate their privacy propert ...

IEEE COMPUTER SOC2019

Consensual and Privacy-Preserving Sharing of Multi-Subject and Interdependent Data

Jean-Pierre Hubaux, Kévin Clément Huguenin, Italo Ivan Dacosta Petrocelli, Alexandra-Mihaela Olteanu

Individuals share increasing amounts of personal data online. This data often involves–or at least has privacy implications for–data subjects other than the individual who shares it (e.g., photos, genomic data) and the data is shared without their consent. ...

2018

Privacy-Enhancing Technologies for Medical and Genomic Data: From Theory to Practice

Jean Louis Raisaro

The impressive technological advances in genomic analysis and the significant drop in the cost of genome sequencing are paving the way to a variety of revolutionary applications in modern healthcare. In particular, the increasing understanding of the human ...

EPFL2018

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