Google TechTalks

Google TechTalks

Differential PrivacyMachine LearningData PrivacyLarge Language Models (LLMs)Machine Learning SecurityData poisoningData SecurityPrompt EngineeringFine-tuningLarge Language ModelsPrivacy AuditingLLM securityFederated LearningAI EthicsAdversarial AttacksMembership Inference AttacksModel MemorizationDeep LearningMachine learning vulnerabilitiesSynthetic Data GenerationMachine Learning PrivacyRetrieval Augmented Generation (RAG)AI SecurityNatural Language ProcessingLanguage ModelsAI SafetyContinual CountingGenerative AIStreaming AlgorithmsApproximation AlgorithmsData MemorizationPrivacyPrivacy-Preserving Data AnalysisCopyright InfringementInformation Theory
Private Adaptations of Large Language Models
Large Language Models (LLMs)Differential Privacy (DP)Machine Learning Adaptation

Private Adaptations of Large Language Models

Private adaptations of open-source Large Language Models (LLMs) offer superior privacy, performance, and cost-effectiveness compared to adapting closed-source LLMs, especially for sensitive data.

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Worst-Case Membership Inference of Language Models
Large Language ModelsMembership Inference AttacksPrivacy Auditing

Worst-Case Membership Inference of Language Models

This talk introduces a novel, highly effective strategy for generating 'canaries' to audit language models for membership inference, revealing a critical disconnect between audit success and actual privacy risk.

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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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