#model-privacy News & Analysis

3 articles tagged with #model-privacy. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBearisharXiv – CS AI · Mar 97/10

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Window-based Membership Inference Attacks Against Fine-tuned Large Language Models

Researchers developed WBC (Window-Based Comparison), a new membership inference attack method that significantly outperforms existing approaches by analyzing localized patterns in Large Language Models rather than global signals. The technique achieves 2-3 times better detection rates and exposes critical privacy vulnerabilities in fine-tuned LLMs through sliding window analysis and binary voting mechanisms.

AINeutralarXiv – CS AI · Jun 16/10

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idSCD: Identifying Training Datasets through Semantic Correlation Descriptors

Researchers have developed a new method called Semantic Correlation Descriptors (SCDs) to identify whether a specific dataset was used to train a machine learning model by analyzing the spurious correlations embedded in its learned structure. This white-box approach outperforms existing black-box membership inference techniques, achieving up to 60% higher accuracy in detecting dataset membership across natural language and medical text classification tasks.

AINeutralarXiv – CS AI · Apr 156/10

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Orthogonal Subspace Projection for Continual Machine Unlearning via SVD-Based LoRA

Researchers propose an SVD-based orthogonal subspace projection method for continual machine unlearning that prevents interference between sequential deletion tasks in neural networks. The approach maintains model performance on retained data while effectively removing influence of unlearned data, addressing a critical limitation of naive LoRA fusion methods.