Google TechTalks

Google TechTalks

Differential PrivacyMachine LearningData PrivacyLarge Language Models (LLMs)Large Language ModelsDeep LearningAI SafetyPrompt EngineeringMachine Learning SecurityData poisoningMembership Inference AttacksCopyright InfringementNatural Language ProcessingData SecurityFine-tuningPrivacy AuditingLLM securityFederated LearningEthics of AIAdversarial AttacksStochastic Gradient DescentMatrix FactorizationPrivacy AlgorithmsLower BoundsModel EvaluationImage GenerationModel MemorizationMachine learning vulnerabilitiesSynthetic Data GenerationMachine Learning PrivacyRetrieval Augmented Generation (RAG)AI SecurityLanguage ModelsContinual CountingGenerative AIStreaming AlgorithmsApproximation AlgorithmsData MemorizationPrivacyPrivacy-Preserving Data AnalysisInformation Theory
Machine Text Detectors are Membership Inference Attacks
Machine Text DetectionMembership Inference AttacksLarge Language Models

Machine Text Detectors are Membership Inference Attacks

This research reveals that machine text detection and membership inference attacks, traditionally studied as separate problems, are fundamentally linked both theoretically and empirically, sharing optimal methods and exhibiting high cross-task transferability.

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The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage
Membership Inference Attacks (MIA)Large Language Models (LLMs)N-gram Coverage

The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage

Discover how a simple n-gram coverage attack can surprisingly and effectively detect if specific data was used to train large language models, even with limited black-box access.

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Evaluating Data Misuse in LLMs: Introducing Adversarial Compression Rate as a Metric of Memorization
Large Language Models (LLMs)Data MemorizationCopyright Infringement

Evaluating Data Misuse in LLMs: Introducing Adversarial Compression Rate as a Metric of Memorization

This presentation introduces Adversarial Compression Rate (ACR) as a robust metric to quantify LLM memorization, addressing copyright concerns by focusing on the shortest prompt needed to elicit exact verbatim output.

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