Publication
A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings
Publications associées (37)
Differentially private multi-agent constraint optimization
Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle
Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...
Dordrecht2024Arbitrary Decisions are a Hidden Cost of Differentially Private Training
Carmela González Troncoso, Bogdan Kulynych
Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...
New York2023Data Privacy Concerns as a Source of Resistance to Complete Mobile Data Collection Tasks Via a Smartphone App
Smartphones present many interesting opportunities for survey research, particularly through the use of mobile data collection applications (apps). There is still much to learn, however, about how to integrate apps in general population surveys. Recent stu ...
OXFORD UNIV PRESS INC2022Differentially Private Multi-Agent Constraint Optimization
Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle
Several optimization scenarios involve multiple agents that desire to protect the privacy of their preferences. There are distributed algorithms for constraint optimization that provide improved privacy protection through secure multiparty computation. How ...
ACM2022Privacy-Aware Digital Mediation Tools for Improving Adolescent Mental Well-being: Application to School Bullying
Denis Gillet, Isabelle Barbara Marie-Hélène Cardia, Maria Gaci
In human-computer interaction, self-disclosure of sensitive information regarding distressing experiences requires the establishment of a trust channel between the user and the digital tool. As privacy and security have been identified as factors that cont ...
2020Data-Aware Privacy-Preserving Machine Learning
In this thesis, we focus on the problem of achieving practical privacy guarantees in machine learning (ML), where the classic differential privacy (DP) fails to maintain a good trade-off between user privacy and data utility. Differential privacy guarantee ...
EPFL2020Privacy-Enhancing Technologies for Mobile Applications and Services
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 ...
EPFL2019Invited Paper: The Applications of Machine Learning in Privacy Notice and Choice
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 ...
IEEE2019Interdependent and Multi-Subject Privacy: Threats, Analysis and Protection
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 ...
EPFL2019Inpainting 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