Latest Articles for Differential Privacy

PMixED improves privacy without sacrificing performance in large language models.

A new method enhances privacy in online federated learning while adapting to changing data.

Learn how heavy-tailed noise enhances privacy in machine learning models.

Examining privacy concerns and solutions for Large Language Models in text data.

Explores the privacy risks and defenses related to synthetic data created by GANs.

A framework for training machine learning models while protecting privacy.

Exploring the impact of public data on differential privacy in machine learning.

This study examines methods for maintaining privacy in language processing tasks.

Banks face challenges sharing trading data while protecting client privacy.

New algorithms improve privacy and optimization in machine learning models.

Exploring the relationship between anomaly detection, privacy, and explainability.

Exploring privacy risks and solutions in large language models.

This article examines how preprocessing steps can impact data privacy guarantees.

Exploring how BR-DP balances privacy and data analysis.

Exploring fine-tuning methods to improve model accuracy while ensuring data privacy.

This article explores methods to protect privacy while analyzing data effectively.

Optimizing noise generation methods for better data privacy in streaming applications.

New algorithms enhance privacy in complex optimization tasks.

A new method that balances privacy and text quality in language models.

A guide to protecting individual data privacy while enabling data analysis.

Exploring federated learning methods to protect user privacy and enhance model performance.

A new method poses significant risks to privacy in federated learning.

A new method addresses privacy and fairness in regression models using innovative adjustments.

Shadow-free MIAs reveal vulnerabilities in user recommendation systems.

A new framework generates human mobility data while protecting individual privacy.

Learn how differential privacy can enhance data security in LLS regression.

A look at differential privacy and its application in machine learning.

Research improves ECG interpretation while ensuring patient data privacy.

FedSC improves model training while maintaining user privacy in federated learning.

New techniques improve scalability in differentially private machine learning.

This study improves the security of quantum machine learning against adversarial attacks through noise channels and privacy methods.

This article discusses differential privacy's impact on trust in the U.S. Census.

A method to rewrite texts while protecting individuals' privacy.

A new method improves data privacy for discrete data analysis.

Discover the impact of B5G and 6G on location privacy.

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

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

Methods to estimate dataset characteristics while keeping individual data safe.

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

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