Latest Articles for Data Privacy

A look at federated learning algorithms and frameworks for privacy-preserving machine learning.

Learn about a method that speeds up predictions while keeping data safe.

A new method improves medical models while keeping patient data private.

Advanced techniques improve video streaming quality with minimal interruptions.

A new approach supports video activity recognition while protecting user privacy.

This article examines the role and challenges of synthetic tabular data in ML.

Examining the interplay of fairness, privacy, and predictive performance in machine learning.

Learn how FGL protects data privacy while improving machine learning models.

A new model addresses privacy concerns in smart healthcare networks using blockchain and trust-based approaches.

Federated learning enhances medical NLP while protecting patient privacy.

A look into improving VFL speed while ensuring data privacy.

Learn how synthetic data can protect privacy in longitudinal research.

Understanding how differential privacy secures sensitive information in data analysis.

A new approach to enhance privacy in encrypted databases.

A study reveals discrepancies between app privacy labels and actual data practices.

Learn how DP-OPH techniques safeguard user data in analytics.

A new method improves stability in federated learning through better model adaptation.

Combining federated learning with differential privacy enhances medical image classification while ensuring patient data safety.

A framework improving federated learning by managing diverse tasks across devices.

A new technique improves detection of backdoor attacks in federated learning models.

A new model enhances efficiency and accuracy in medical image classification.

AI chatbots are transforming medical imaging by enhancing efficiency and communication.

Examining the risks of data poisoning in SplitFed Learning applications.

A novel approach to personalized recommendations in decentralized networks.

AI software improves medical report processing for better patient care.

Examining global DNS hosting and its effects on national control and security.

Empowering patients with control over their health data and ensuring privacy.

A look at how Local Differential Privacy can ensure personal data safety while allowing research.

Examining local differential privacy within Thompson Sampling for better decision-making.

A new framework enhances distributed optimization for complex non-convex problems.

FedInit improves device collaboration in federated learning by addressing client drift.

New methods improve privacy in data analysis using Kernel Density Estimation.

A new framework improves decentralized learning for dynamic data environments.

Examining how neural networks can recall training data and the privacy risks involved.

Introducing a new defense mechanism against membership inference attacks in machine learning models.

Organizations can better protect privacy while ensuring data usefulness through a structured framework.

New open platforms aim to improve Federated Learning through user collaboration.

Introducing VertiBench, a tool for evaluating vertical federated learning algorithms with realistic datasets.

FedDec enhances federated learning by enabling direct device communication for better model training.

Invariant Dropout improves training efficiency by managing device performance variations.