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Publications associées (30)

Data Privacy Concerns as a Source of Resistance to Complete Mobile Data Collection Tasks Via a Smartphone App

Daniel Gatica-Perez

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 INC2022

Differentially Private Multi-Agent Constraint Optimization

Boi Faltings, Sujit Prakash Gujar, Sankarshan Damle, Aleksei Triastcyn

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 ...

ACM2022

A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

Jean-Marc Odobez, Anshul Gupta, Samy Tafasca

Predicting where a person is looking is a complex task, requiring to understand not only the person's gaze and scene content, but also the 3D scene structure and the person's situation (are they manipulating? interacting or observing others? attentive?) to ...

IEEE2022

Synthetic Data - Anonymisation Groundhog Day

Carmela González Troncoso, Theresa Stadler

Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the stat ...

USENIX ASSOC2022

Utility/privacy trade-off as regularized optimal transport

Etienne Patrice Boursier

Strategic information is valuable either by remaining private (for instance if it is sensitive) or, on the other hand, by being used publicly to increase some utility. These two objectives are antagonistic and leaking this information by taking full advant ...

SPRINGER HEIDELBERG2022

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

Boi Faltings, Aleksei Triastcyn

We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employing ...

MDPI2022

TEE-based decentralized recommender systems: The raw data sharing redemption

Anne-Marie Kermarrec, Rafael Pereira Pires, Akash Balasaheb Dhasade, Nevena Dresevic

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Fe ...

2022

Scalable Multi-agent Coordination and Resource Sharing

Panayiotis Danassis

A plethora of real world problems consist of a number of agents that interact, learn, cooperate, coordinate, and compete with others in ever more complex environments. Examples include autonomous vehicles, robotic agents, intelligent infrastructure, IoT de ...

EPFL2022

Flying in Private Mode: Understanding and Improving the Privacy ICAO Address Program

Guillaume Michel Le Bars

Location privacy of aircraft has recently gained attention as air traffic management was modernized using novel surveillance technologies. Business aviation circles and various military and government entities voiced serious concerns about automated and ub ...

AMER INST AERONAUTICS ASTRONAUTICS2021

Integrity and Metadata Protection in Data Retrieval

Kirill Nikitin

Secure retrieval of data requires integrity, confidentially, transparency, and metadata-privacy of the process. Existing protection mechanisms, however, provide only partially these properties: encryption schemes still expose cleartext metadata, protocols ...

EPFL2021

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