Publications related to Expectation of privacy (United States)

Differentially private multi-agent constraint optimization

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

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 ...

Springer2024

The Privacy Power of Correlated Noise in Decentralized Learning

Rachid Guerraoui, Martin Jaggi, Youssef Allouah, Anastasiia Koloskova, Aymane El Firdoussi

Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, wi ...

PMLR2024

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Sayan Biswas

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries du ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Bridging the gap between theoretical and practical privacy technologies for at-risk populations

Kasra Edalatnejadkhamene

With the pervasive digitalization of modern life, we benefit from efficient access to information and services. Yet, this digitalization poses severe privacy challenges, especially for special-needs individuals. Beyond being a fundamental human right, priv ...

EPFL2023

P3LI5: Practical and confidEntial Lawful Interception on the 5G core

Apostolos Pyrgelis, Francesco Intoci

Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain insightful data from network communications for criminal proceedings, e.g., ne ...

IEEE2023

Not Yet Another Digital ID: Privacy-Preserving Humanitarian Aid Distribution

Carmela González Troncoso, Boya Wang, Wouter Lueks, Justinas Sukaitis

Humanitarian aid-distribution programs help bring physical goods to people in need. Traditional paper-based solutions to support aid distribution do not scale to large populations and are hard to secure. Existing digital solutions solve these issues, at th ...

IEEE COMPUTER SOC2023

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

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

Examining the Effects of Privacy-Aware Blended Learning Scenarios in Executive Training for Policymakers and Government Officials

Denis Gillet, Isabelle Barbara Marie-Hélène Cardia, Juan Carlos Farah, Maria Gaci

Enabling blended learning scenarios that foster participation and interaction is an important consideration in the design of executive training programs. To investigate this design process, our study focuses on two aspects that are present in such scenario ...

Springer2020

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

High dynamic range image sharing with privacy protection

Touradj Ebrahimi, Evgeniy Upenik, Pol Fernandez Marti

Sharing pictures has become a very popular practice among consumers. Most recent cameras, displays, and smartphones can capture and display images in high dynamic range and wide colour gamut, contributing to an increase of this type of content. It is a wel ...

SPIE2020

Data-Aware Privacy-Preserving Machine Learning

Aleksei Triastcyn

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 ...

EPFL2020

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

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

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

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

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

Formal Analysis of Distance Bounding with Secure Hardware

Serge Vaudenay, Handan Kilinç Alper

A distance bounding (DB) protocol is a two-party authentication protocol between a prover and a verifier which is based on the distance between the prover and the verifier. It aims to defeat threats by malicious provers who try to convince that they are cl ...

2018

Enhancing Privacy Protection : Set Membership, Range Proofs, and the Extended Access Control

Rafik Chaabouni

Privacy has recently gained an importance beyond the field of cryptography. In that regard, the main goal behind this thesis is to enhance privacy protection. All of the necessary mathematical and cryptographic preliminaries are introduced at the start of ...

EPFL2017