Membership Inference Attacks
PulseAugur coverage of Membership Inference Attacks — every cluster mentioning Membership Inference Attacks across labs, papers, and developer communities, ranked by signal.
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New framework unifies membership inference attacks across generative models
Researchers have developed a unified framework for membership inference attacks (MIA) that can be applied across various generative model modalities, including text-to-text, text-to-image, and image-to-text. This new ap…
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New AI Cyber Attack Type Targets Patient Privacy
A new category of cyber attack, known as Membership Inference Attacks (MIAs), has been identified as a threat to patient privacy. These attacks specifically target individuals within medical datasets, potentially exposi…
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New attack reveals privacy risks in tabular foundation models
Researchers have identified significant privacy vulnerabilities in tabular foundation models, particularly within their attention layers. A new attack, AMIA, leverages transformer attention patterns to effectively perfo…
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New benchmarks tackle privacy risks in large language models
Researchers have developed new methods to evaluate membership inference attacks (MIAs) against large language models (LLMs), particularly focusing on audio and text modalities. The first study introduces a systematic ev…
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Researchers propose $\mu \approx \varepsilon/5$ conversion for Gaussian differential privacy
Researchers have published a paper detailing methods for converting privacy parameters between pure differential privacy ($\varepsilon$) and Gaussian differential privacy (GDP, $\mu$). The study proposes principled mapp…
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Counterfactuals pose privacy risks, new research shows
Researchers have demonstrated that counterfactual explanations, used to clarify machine learning model decisions, can be exploited for privacy attacks. By adapting methods developed for synthetic data, these attacks can…
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Bayesian sampling enhances AI model privacy attack efficiency
Researchers have developed a new method called Bayesian Membership Inference Attack (BMIA) to more efficiently detect if specific data points were used in a model's training. This approach uses Bayesian sampling and Lap…
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New research refines evaluation of AI model privacy attacks
Researchers are developing new frameworks and methods to evaluate the effectiveness and reliability of membership inference attacks (MIAs), which are used to detect if specific data was used in training machine learning…
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New attack method breaches privacy of AI safety classifiers
Researchers have developed a new method to attack the privacy of safety classifiers used in generative AI systems. These classifiers, trained on sensitive data like discussions of self-harm, are vulnerable to membership…