Concept
Expectation of privacy (United States)
Related publications (49)
The Privacy Power of Correlated Noise in Decentralized Learning
Rachid Guerraoui, Martin Jaggi, Anastasiia Koloskova, Youssef Allouah, 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 ...
PMLR2024A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles
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 ...
Piscataway2024Bridging the gap between theoretical and practical privacy technologies for at-risk populations
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 ...
EPFL2023P3LI5: 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 ...
New York2023Not 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 SOC2023Generating 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