Privacy and Confidentiality in Machine Learning and Data Analysis: Understanding Risks and Developing Protections

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Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling collaborative model training across decentralized devices while preserving data privacy. However, FL's success is highly contingent on the quality and integrity ...

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datas ...

Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used to infer training data. Conventional defenses such as differential privacy ...

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models ############# Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis Affiliation: EPFL Date: January, 2024 Link to the article: ...

The training of automatic speech recognition (ASR) with little to no supervised data remains an open question. In this work, we demonstrate that streaming Transformer-Transducer (TT) models can be trained from scratch in consumer and accessible GPUs in the ...

Previous research on causal reasoning often overlooks the subtleties crucial to understanding causal reasoning. To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language M ...

The rapid evolution of Deep Learning (DL) has brought about significant transformations across scientific domains, marked by the development of increasingly intricate models demanding powerful GPU platforms. However, edge applications like wearables and mo ...

With the improvement of Internet of Things (IoT) technologies, services, and applications, there is a proliferation of access to smart devices in everyday life. However, granting access and controlling access rights for each resource is challenging in high ...

Compound structural identification for non-targeted screening of organic molecules in complex mixtures is commonly carried out using liquid chromatography coupled to tandem mass spectrometry (UHPLC-HRMS/MS and related techniques). Instrumental developments ...

We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn {compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of data is ...

MedCo is the first operational system that makes sensitive medical-data available for research in a simple, privacy-conscious and secure way. It enables a consortium of clinical sites to collectively protect their data and to securely share them with inves ...

Privacy and Confidentiality in Machine Learning and Data Analysis: Understanding Risks and Developing Protections

Chattez avec Graph Search

Robust and Personalized Federated Learning: Handling Data Corruption and Heterogeneity

Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

Noiseless Privacy-Preserving Decentralized Learning

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models

Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper

Nuance Matters: Probing Epistemic Consistency in Causal Reasoning

Constrained Zero-Shot Neural Architecture Search on Small Classification Dataset

A Blockchain-Based Interoperable Architecture for IoT with Selective Disclosure of Information

Harnessing data science to improve molecular structure elucidation from tandem mass spectrometry

giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

SPHN/PHRT - MedCo in Action: Empowering the Swiss Molecular Tumor Board with Privacy-Preserving and Real-Time Patient Discovery