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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 PrivacyPrivacy AmplificationMachine Learning
Privacy Amplification for Correlated-Noise Mechanisms via b-Min-Sep Subsampling
This research introduces B-min-sep subsampling, a novel method that enhances privacy amplification for differentially private matrix factorization (DPMF) by leveraging correlated noise and enabling practical application in complex multi-attribution settings.
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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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