Latest Articles for Data Privacy

A new method for counting cycles in graphs while ensuring user privacy.

Graph unlearning offers a solution for removing outdated data without full retraining.

CryptoTrain combines cryptographic techniques to protect sensitive data during machine learning training.

FeDETR uses federated learning for improved coronary stenosis evaluation.

Personalized systems enhance monitoring of health and behavior through adjusted models.

Methods to improve privacy in federated Cox analysis are explored.

TLSNotary ensures data authenticity while maintaining client privacy in online communications.

A look at how differential privacy safeguards individual data in a data-driven world.

Exploring the need for effective enforcement of data usage policies.

An overview of information leakage, its impact, and how to measure it.

Examining local insights on mobile data privacy during a pandemic aid initiative.

Survey reveals attitudes toward health apps and data sharing across generations.

A look at privacy risks and secure methods for generative AI.

Examining how SSL models memorize data points and its implications.

A-FedPD aligns model updates to reduce training instability in federated learning.

A new method predicts memorization risks in Large Language Models to enhance privacy.

A new method improves privacy in data sharing for smart city predictions.

TAKFL optimizes knowledge sharing in federated learning for diverse device capabilities.

This paper enhances federated learning for GNNs while preserving user privacy.

New methods improve data usage auditing in Federated Learning systems.

A study comparing privacy threats in spiking and artificial neural networks.

A new method safeguards decentralized learning from data poisoning attacks.

A new method to improve Federated Learning's resilience against data attacks.

Understanding the complexities of proving data usage in AI training.

Combining global and local prompts enhances federated learning models while preserving data privacy.

A new model offers insights into job trends while protecting sensitive data.

A look at federated learning's challenges and solutions in maintaining data privacy and model accuracy.

Innovative strategies improve rare event detection in federated learning systems.

FLeNS improves federated learning by balancing learning speed and communication efficiency.

Examining privacy vulnerabilities in federated learning and their implications for data security.

AI enhances kidney stone detection while ensuring patient data privacy.

Research shows LLMs can generate useful synthetic code for teaching.

Learn how federated learning adapts AI to individual preferences while preserving privacy.

Client-Customized Adaptation improves federated learning efficiency and privacy.

A look into membership inference attacks and their relevance in data privacy.

A new method protects sensitive data in QUBO optimization.

MASA offers a solution to enhance security in Federated Learning systems.

Learn how synthetic data can enhance employee behavior analysis while protecting privacy.

WaKA reveals how data points impact models while assessing privacy risks.

A new method to protect smart meters from FDI attacks while preserving privacy.