MedCo: Enabling Secure and Privacy-Preserving Exploration of Distributed Clinical and Genomic Data

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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 ...

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 ...

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 ...

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 ...

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impa ...

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 ...

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 ...

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 ...

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 ...

Background: Increasingly, hospitals and research institutes are developing technical solutions for sharing patient data in a privacy preserving manner. Two of these technical solutions are homomorphic encryption and distributed ledger technology. Homomorph ...

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 ...

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 ...

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 ...

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Fe ...

This contribution outlines a research proposal combining ethical guidelines on AI and a law-as-data approach. Building upon the definitions of soft law discussed in legal scholarship, it proposes a way of structuring the regulatory landscape on AI and of a ...

Most communication systems (e.g., e-mails, instant messengers, VPNs) use encryption to prevent third parties from learning sensitive information. However, encrypted communications protect the contents but often leak metadata: the amount of data sent and th ...

In human-computer interaction, self-disclosure of sensitive information regarding distressing experiences requires the establishment of a trust channel between the user and the digital tool. As privacy and security have been identified as factors that cont ...

Medical studies are usually time consuming, cumbersome and extremely costly to perform, and for exploratory research, their results are also difficult to predict a priori. This is particularly the case for rare diseases, for which finding enough patients i ...

The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...

MedCo: Enabling Secure and Privacy-Preserving Exploration of Distributed Clinical and Genomic Data

Chat with Graph Search

Differentially private multi-agent constraint optimization

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

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

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

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

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

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

Privacy-Preserving Machine Learning on Graphs

Health data privacy through homomorphic encryption and distributed ledger computing: an ethical-legal qualitative expert assessment study

Differentially Private Multi-Agent Constraint Optimization

Data Privacy Concerns as a Source of Resistance to Complete Mobile Data Collection Tasks Via a Smartphone App

A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

TEE-based decentralized recommender systems: The raw data sharing redemption

Learning from the Ethics of AI-A Research Proposal on Soft Law and Ethics of AI

Analyzing and Protecting Communication Metadata

Privacy-Aware Digital Mediation Tools for Improving Adolescent Mental Well-being: Application to School Bullying

MedCo(2): Privacy-Preserving Cohort Exploration and Analysis

Private and Secure Distributed Learning