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
Local Node Differential PrivacyDifferential PrivacyDistributed Networks
Local Node Differential Privacy
This talk introduces Local Node Differential Privacy (LNDP), a novel model for privately analyzing distributed network data without a trusted third party, revealing surprising algorithmic capabilities and fundamental limitations compared to traditional differential privacy models.
Explore Insights →
Differential PrivacyGraph AlgorithmsMultiway Cut
Differentially Private Multiway and k-Cut
This talk details novel algorithms and lower bounds for achieving differential privacy in graph cut problems, specifically multiway and k-cut, crucial for protecting sensitive user data in graph-based applications.
Explore Insights →
Want more on lower bounds?
Explore deep-dive summaries and actionable takeaways from the best minds across different podcasts discussing this topic.
View All Lower Bounds Episodes→Don't see the episode you're looking for?
We're constantly adding new episodes, but if you want to see a specific one from Google TechTalks summarized, let us know!
Submit an Episode