Privacy-preserving distributed collaborative filtering

Publications associées (30)

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Anonymous Tokens with Stronger Metadata Bit Hiding from Algebraic MACs

Serge Vaudenay, Fatma Betül Durak

On the one hand, the web needs to be secured from malicious activities such as bots or DoS attacks; on the other hand, such needs ideally should not justify services tracking people's activities on the web. Anonymous tokens provide a nice tradeoff between ...

2023

Efficient and Accurate Peer-to-Peer Training of Machine Learning Based Home Thermal Models

Rachid Guerraoui, Alexandre David Olivier Maurer, Omid Ardakanian

The integration of smart thermostats in home automation systems has created an opportunity to optimize space heating and cooling through the use of machine learning, for example for thermal model identification. Nonetheless, its full potential remains unta ...

Assoc Computing Machinery2023

Private Message Franking with After Opening Privacy

Serge Vaudenay, Iraklis Leontiadis

Recently Grubbs et al. [GLR17] initiated the formal study of message franking protocols. This new type of service launched by Facebook, allows the receiver in a secure messaging application to verifiably report to a third party an abusive message some send ...

2023

Protecting privacy through metadata analysis

Sandra Deepthy Siby

Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we demonstr ...

EPFL2022

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

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

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

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