Privacy-safe Measurement on the Web: Open Questions From the Privacy Sandbox

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=BTuJGiOFIqI


Duration: 58:26
942 views
8

Christina Ilvento (Google)
https://simons.berkeley.edu/talks/privacy-safe-measurement-web-open-questions-privacy-sandbox
Societal Considerations and Applications

The Privacy Sandbox aims "to create technologies that both protect people's privacy online and give companies and developers tools to build thriving digital businesses." This talk will describe some of the design, implementation and practical challenges in evolving measurement solutions away from persistent cross-site identifiers.



Other Videos By Simons Institute for the Theory of Computing


2022-11-10Predictive Modeling in Healthcare – Special Considerations
2022-11-10Bringing Order to Chaos: Navigating the Disagreement Problem in Explainable ML
2022-11-09Pipeline Interventions
2022-11-09Algorithmic Challenges in Ensuring Fairness at the Time of Decision
2022-11-09Improving Refugee Resettlement
2022-11-09Learning to Predict Arbitrary Quantum Processes
2022-11-09A Kerfuffle: Differential Privacy and the 2020 Census
2022-11-08Chasing the Long Tail: What Neural Networks Memorize and Why
2022-11-08Concurrent Composition Theorems for all Standard Variants of Differential Privacy
2022-11-08Privacy Management: Achieving the Possimpible
2022-11-07Privacy-safe Measurement on the Web: Open Questions From the Privacy Sandbox
2022-10-29Transmission Neural Networks: From Virus Spread Models to Neural Networks
2022-10-29Spatial Spread of Dengue Virus: Appropriate Spatial Scales for Transmission
2022-10-28A Global Comparison of COVID-19 Variant Waves and Relationships with Clinical and...
2022-10-28Diversity and Inequality in Information Diffusion on Social Networks
2022-10-28Learning through the Grapevine and the Impact of the Breadth and Depth of Social Networks
2022-10-28Just a Few Seeds More: The Inflated Value of Network Data for Diffusion...
2022-10-27Bayesian Learning in Social Networks
2022-10-27Likelihood-based Inference for Stochastic Epidemic Models
2022-10-27Testing, Voluntary Social Distancing, and the Spread of an Infection
2022-10-27Complex Contagions and Hybrid Phase Transitions


Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Epidemics and Information Diffusion
Christina Ilvento