Latest Articles for Data Privacy

A new method improves data privacy for discrete data analysis.

SpaFL enhances communication and computation in Federated Learning while securing personal data.

LayerCAM-AE enhances detection of malicious updates in federated learning while preserving data privacy.

Research aims to balance privacy and accuracy in vector mean estimation methods.

Counterfactuals reveal insights but pose privacy risks in machine learning.

Examining the risks and misuse of large language models in cybercrime.

A new method tackles wireless errors in federated learning for IoT devices.

This article discusses privacy and bias in predictive algorithms for sensitive data.

A new method enhances model predictions for better adaptation without source data.

A new framework enhances synthetic data creation while protecting personal information.

A new multi-server approach improves federated learning efficiency and speed.

Introducing PrivacyRestore to protect user data while using large language models.

This study addresses difficulties in unlearning data from machine learning models.

Methods to estimate dataset characteristics while keeping individual data safe.

New method improves federated learning while protecting user privacy.

Increasing communication rounds reduces costs and improves model performance in federated learning.

A new approach to improve user-specific experiences in language models.

A look into privacy methods and their effectiveness in data sharing.

A new method enhances privacy protection while training deep learning models.

A look into new methods for ad measurement that prioritize user privacy.

This article addresses how to simulate client data for faster federated learning.

New methods in Federated Learning enhance privacy and model performance while handling diverse data.

Explore the privacy challenges posed by inference attacks in machine learning models.

A new framework enhances prediction accuracy and data privacy in federated learning.

Introducing new methods to improve forgetting processes in contrastive learning models.

New attack method raises concerns about privacy in document understanding models.

Exploring federated learning and unlearning for user privacy and model integrity.

Introducing a method to fine-tune LLMs on low-resource devices.

A study on improving federated learning models for devices with limited resources.

A new method sheds light on how language models remember training data.

A new method creates reliable prediction intervals using diverse data while ensuring privacy.

Learn about cookies, their risks, and how to protect your privacy.

A study on preserving privacy while analyzing sensitive data using differential privacy techniques.

New framework enables efficient learning of diseases without storing past data.

Quantum machine learning offers new ways to protect sensitive data.

A new framework improves recommendations while protecting user privacy.

A structured approach to responsible AI design and validation.

An analysis of how online services handle children's data privacy.

A breakthrough system allows fast LLM operations on smartphones, enhancing user privacy.

Exploring vulnerabilities in Personalized Federated Learning and emerging backdoor attack methods.