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Privacy in Recommender Systems

Publications associées (94)

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Protecting Mobile Food Diaries from Getting too Personal

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

Smartphone applications that use passive sensing to support human health and well-being primarily rely on: (a) generating low-dimensional representations from high-dimensional data streams; (b) making inferences regarding user behavior; and (c) using those ...

Association for Computing Machinery2020

Privacy-preserving photo sharing based on blockchain

Touradj Ebrahimi, Pablo Pfister

Sharing photos online has become an extremely popular activity, raising a wide concern on privacy issues related to the shared content. ProShare, a photo-sharing solution developed by Multimedia Signal Processing Group of EPFL addresses some of these priva ...

SPIE-INT SOC OPTICAL ENGINEERING2020

Byzantine machine learning

Rachid Guerraoui, Mahsa Taziki, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault

The present invention concerns computer-implemented methods for training a machine learning model using Stochastic Gradient Descent, SGD. In one embodiment, the method is performed by a first computer in a distributed computing environment and comprises pe ...

2020

Interdependent and Multi-Subject Privacy: Threats, Analysis and Protection

Alexandra-Mihaela Olteanu

In Alan Westin's generally accepted definition of privacy, he describes it as an individual's right 'to control, edit, manage, and delete information about them[selves] and decide when, how, and to what extent information is communicated to others.' There ...

EPFL2019

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

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

Inpainting in Omnidirectional Images for Privacy Protection

Touradj Ebrahimi, Evgeniy Upenik, Pinar Akyazi

Privacy protection is drawing more attention with the advances in image processing, visual and social media. Photo sharing is a popular activity, which also brings the concern of regulating permissions associated with shared content. This paper presents a ...

2019

A Survey on Interdependent Privacy

Mathias Jacques Jean-Marc Humbert, Kévin Clément Huguenin, Benjamin Trubert

The privacy of individuals does not only depend on their own actions and data but may also be affected by the privacy decisions and by the data shared by others individuals. This interdependence is an essential aspect of privacy and ignoring it can lead to ...

2019

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

Forged Authenticity: Governing Deepfake Risks

The manipulation of digital content is not a new phenomenon, but the application of machine learning to the creation of deepfakes has (i) radically improved the quality of output, (ii) slashed the resources required to produce realistic fakes at previously ...

EPFL International Risk Governance Center (IRGC)2019