Latest Articles for Membership Inference Attacks

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.