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

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Decarbonizing mobility and integrating more renewable sources in electricity production are necessary levers to meet the climate targets. Coupling electric vehicle (EV) charging with photovoltaic (PV) electricity generation could help to provide clean elec ...

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 ...

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 ...

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 ...

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and gr ...

The electrification of the transport sector is a key element in decarbonizing our societies. However, energy systems will have to cope with additional electricity demand due to the charging needs of electric vehicles (EVs) together with the integration of ...

The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...

Wearable activity trackers (WATs) have recently gained worldwide popularity, with over a billion devices collecting a range of personal data. To receive additional services, users commonly share this data with third-party applications (TPAs). However, this ...

In this study, we harness the information-theoretic Privacy Funnel (PF) model to develop a method for privacy-preserving representation learning using an end-to-end training framework. We rigorously address the trade-off between obfuscation and utility. Bo ...

The ongoing electrification of the transportation fleet will increase the load on the electric power grid. Since both the transportation network and the power grid already experience periods of significant stress, joint analyses of both infrastructures wil ...

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 ...

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

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 ...

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 ...

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...

This paper studies the routing and charging behaviors of electric vehicles in a competitive ride-hailing market. When the vehicles are idle, they can choose whether to continue cruising to search for passengers, or move a charging station to recharge. The ...

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

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

Chattez avec Graph Search

A Spatiotemporal analysis of photovoltaic electricity storage potential in electric vehicles

Differentially private multi-agent constraint optimization

The Privacy Power of Correlated Noise in Decentralized Learning

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

From PV to EV: Mapping the Potential for Electric Vehicle Charging with Solar Energy in Europe

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

Our Data, Our Solutions: A Participatory Approach for Enhancing Privacy in Wearable Activity Tracker Third-Party Apps

Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition

GreenEVT: Greensboro Electric Vehicle Testbed

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

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Privacy-Preserving Machine Learning on Graphs

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

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

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Multi-agent Learning with Privacy Guarantees

Enforcing Privacy in Distributed Learning With Performance Guarantees

Routing and charging game in ride-hailing service with electric vehicles

Privacy-preserving federated neural network training and inference