Privacy-Preserving Statistical Learning and Testing

Channel:
Microsoft Research
Subscribers:
344,000
Published on ● Video Link: https://www.youtube.com/watch?v=wUIvHZdQ9Y0


Duration: 54:46
682 views
15

Identity Testing and Distributional Property Learning are two fundamental problems in statistical inference. However, in many settings, data may contain sensitive information about individuals. It is critical that our methods should protect sensitive information, meanwhile not preclude our overall goals of statistical analysis.

In this talk, I will talk about Differentially Private Identity Testing and Distributional Property Learning. We derive almost-tight sample complexity bounds for both problems. Our upper bounds come from privatizing non-private estimators. As to the lower bound, We establish a general coupling method which we believe can be used to obtain strong lower bounds for other statistical problems under privacy.

See more at https://www.microsoft.com/en-us/research/video/privacy-preserving-statistical-learning-and-testing/



Other Videos By Microsoft Research


2019-09-23Battling Unfair Demons in Peer Review
2019-09-19Engaging with Students and Parents in Bellevue School District in Multilingual Settings
2019-09-19Internship Program - MSR Montreal
2019-09-19Recent Advances in Unsupervised Image-to-Image Translation
2019-09-19Modeling User Experience in Games: Lessons Learned
2019-09-18HCI, IR and the search for better search with Dr. Susan Dumais [Podcast]
2019-09-17Efficient and Perceptually Plausible 3-D Sound For Virtual Reality
2019-09-17Dashboard Mechanisms for Online Marketplaces
2019-09-13Fly-through in the AirSim simulation Team Explorer created for the DARPA SubT Challenge
2019-09-12Compacting the Uncompactable: The Mesh Compacting Memory Allocator
2019-09-12Privacy-Preserving Statistical Learning and Testing
2019-09-12Program Synthesis meets Notebooks
2019-09-11Inside the Microsoft AI Residency Program with Dr. Brian Broll [Podcast]
2019-09-10Real-time Single-channel Speech Enhancement with Recurrent Neural Networks
2019-09-06AI Institute Geometry of Deep Learning 2019 [Workshop] Day 2 | Session 1]
2019-09-05AI Institute "Geometry of Deep Learning" 2019 [Workshop] Day 3 | Session 1
2019-09-05AI Institute "Geometry of Deep Learning" 2019 [Workshop] Day 3 | Session 2
2019-09-05EzPC (Easy Secure Multi-party Computation)
2019-09-04Time discretization invariance in Machine Learning, applications to reinforcement learning...
2019-09-04Antennas for light and their applications in classical optics, Dr Rupert Oulton, Imperial College
2019-09-04Photonics for Computing: from Optical Interconnects to Neuromorphic Architectures


Tags:
microsoft research
statistical inference