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
Differential PrivacySynthetic Data GenerationGenerative AI
Differentially Private Synthetic Data without Training
Microsoft Research introduces 'Private Evolution,' a novel framework that generates differentially private synthetic data using only inference APIs, bypassing the high costs and limitations of traditional DP fine-tuning.
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Generative AIWatermarkingDeepfakes
Watermarking in Generative AI: Opportunities and Threats
This talk details the critical role of watermarking in combating generative AI misuse, from deepfakes and scams to intellectual property theft, by enabling detection and attribution across text and images.
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