Latest Articles for Membership Inference Attacks

A look into the risks of Membership Inference Attacks on data privacy.

Introducing a new way to assess privacy risks in machine learning models.

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

This article discusses the challenges of machine unlearning and a new approach to balance privacy and accuracy.

A new method to verify machine unlearning effectively and securely.

New methods reveal serious privacy threats from location data sharing.

Code poisoning enhances risks of membership inference attacks on sensitive data.

Examining membership inference attacks on time-series forecasting models in healthcare.

Analyzing vulnerabilities in LLMs due to human preference data.

Exploring privacy risks in synthetic data and introducing the Data Plagiarism Index.

A study introduces SeqMIA to improve privacy against membership inference attacks.

This article explores Knowledge Recycling for improving synthetic data training in classifiers.

Examining privacy risks in model explanations and strategies to enhance security.

Exploring the use of watermarks to tackle copyright issues in language models.

Examining differential privacy in natural language processing for better data protection.

A fresh approach highlights surprising tokens to assess language model training data.

Examining vulnerabilities and defenses in diffusion models for safe content generation.

A new method reshapes privacy auditing in machine learning.

A study on improving methods for assessing Membership Inference Attacks in language models.

Exploring privacy risks in masked image modeling and their implications.

MIA-Tuner aims to address privacy issues in LLM training data.

Examining how important data points attract more security risks in machine learning.

A look at privacy concerns in centralized and decentralized learning systems.

Explore the privacy concerns surrounding membership inference attacks in machine learning.

This benchmark evaluates privacy threats and defense mechanisms in NLP models.

Selective encryption enhances privacy while maintaining model performance in collaborative learning.

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

Understanding the complexities of proving data usage in AI training.

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

Researchers present a cost-effective approach to privacy risks in large language models.

Exploring membership inference attacks to protect data privacy in advanced models.

A cost-effective way to assess privacy risks in machine learning models.

Research shows SNNs may enhance data privacy over traditional models.

PEFT methods enhance language models while safeguarding private data.

Explore how L2 regularization can enhance privacy in AI models.

Discover techniques for balancing privacy and fairness in machine learning models.

Exploring how Membership Inference Attacks reveal sensitive data risks in AI models.

Discover the risks of Membership Inference Attacks in decentralized learning.