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
Differential PrivacyContinual CountingMatrix Mechanism
Chasing the Constants and its Implications in Differential Privacy
Discover how refining mathematical constants in differential privacy algorithms significantly reduces error in continual data streams, impacting applications from disease tracking to private federated learning.
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Differential PrivacyStreaming AlgorithmsContinual Counting
Streaming Private Continual Counting via Binning
This talk introduces 'binning,' a novel matrix structure that enables space-efficient streaming private continual counting by approximating complex factorizations with piecewise constant segments, often outperforming theoretical bounds.
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