Quantifying Location Privacy: The Case of Sporadic Location Exposure

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Context. Imaging in radio interferometry requires solving an ill-posed noisy inverse problem, for which the most adopted algorithm is the original CLEAN algorithm and its variants. Alternative explicit optimization methods have gained increasing attention, ...

This thesis aimed to estimate the effect of demographic and biomechanical preoperative variables on total shoulder arthroplasty (TSA) outcomes. To achieve this, various biomechanical variables were calculated using a clinical database comprising normal and ...

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 ...

Adaptive Social Learning (ASL) enables consistent truth learning in nonstationary environments. In this framework, agents linked by a graph, exchange their local beliefs with neighbors to track some underlying state of interest. This state can drift over t ...

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 ...

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 ...

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 ...

The transition to sustainable energy demands a fundamental redesign of the electrical grid, especially at the distribution level. With the increasing deployment of distributed energy resources, the development of reliable models, accurate state estimation ...

The advancement of data-driven decision making has become a key priority in political agendas and private sector strategies. However, the growing demand for data and the opacity of data-driven systems raise concerns about the threats to individual privacy ...

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 ...

The conceptual design phase is a fascinating moment to observe how a design task is interpreted, as the (often implicit) relative importance students accord to the various requirements and constraints offers a window into the thinking underpinning their de ...

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 ...

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 ...

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 ...

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

To make social robots effective in education, they need to be autonomous both in terms of assessing the student's engagement state as well as intervening effectively in soft real-time when necessary. Hidden Markov Model (HMM) is an interpretable machine le ...

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

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 ...

Quantifying Location Privacy: The Case of Sporadic Location Exposure

Chattez avec Graph Search

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