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

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Neuroprosthetics research has entered a stage in which animal models and proof-of-concept studies are translated into clinical applications, often combining implants with artificial intelligence techniques. This new phase raises the question of how clinica ...

Smart contracts have emerged as the most promising foundations for applications of the blockchain technology. Even though smart contracts are expected to serve as the backbone of the next-generation web, they have several limitations that hinder their wide ...

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 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 ...

The detection of inelastically scattered soft x-rays with high energy resolution usually requires large grating spectrometers. Recently, photoelectron spectrometry for analysis of x-rays (PAX) has been rediscovered for modern spectroscopy experiments at sy ...

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 ...

Conducting neutron scattering experiments in the presence of high pulsed magnetic fields, namely above 40 T, provides valuable information about the magnetic structures of materials. However, these experiments are challenging and time-consuming because the ...

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 ...

Artificial intelligence (AI) is increasingly used in an ever larger number of industries. Alongside this development, however, abundant works argue that AI-driven systems are lacking in terms of safety, ethics and transparency. As a direct consequence, the ...

We introduce the SPRIG (Smart Proofs via Recursive Information Gathering) protocol. SPRIG allows agents to propose, question, and defend mathematical proofs in a decentralized fashion. A structure of stakes and bounties aims at producing debates in good fa ...

The scale and pervasiveness of the Internet make it a pillar of planetary communication, industry and economy, as well as a fundamental medium for public discourse and democratic engagement. In stark contrast with the Internet's decentralized infrastructur ...

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 ...

The increasing implementation of programs supported by machine learning in medical contexts will affect psychiatry. It is crucial to accompany this development with careful ethical considerations informed by empirical research involving experts from the fi ...

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 ...

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 ...

The transition towards a human-centered indoor climate is beneficial from occupants’ thermal comfort and from an energy reduction perspective. However, achieving this goal requires the knowledge of the thermal state of individuals at the level of body part ...

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

This dataset contains aerosol optical absorption coefficients at seven different wavelengths (babs(λ)), averaged to 10 min time resolution, measured during the year-long MOSAiC expedition from October 2019 to September 2020. The measurements were performed ...

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 ...

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 ...

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

Chattez avec Graph Search

Clinical trials for implantable neural prostheses: understanding the ethical and technical requirements

Towards General-Purpose Decentralized Computing with Permissionless Extensibility

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

On the fundamental limits of privacy-enhancing technologies

A compact approach to higher-resolution resonant inelastic x-ray scattering detection using photoelectrons

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

High brightness neutron source optimized for very high pulsed magnetic field experiments

The Privacy Power of Correlated Noise in Decentralized Learning

Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act

Smart Proofs via Recursive Information Gathering: Decentralized Refereeing by Smart Contracts

Planetary-Scale Byzantine Fault Tolerance

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

Explainability as fig leaf? An exploration of experts’ ethical expectations towards machine learning in psychiatry

Sustainability and Ethicality are Peripheral to Students’ Software Design

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

Toward contactless human thermal monitoring: A framework for Machine Learning-based human thermo-physiology modeling augmented with computer vision

Scalable and Privacy-Preserving Federated Principal Component Analysis

Aerosol optical absorption coefficients at seven wavelengths in 10 min resolution measured in the Swiss container during MOSAiC 2019/2020

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

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data