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Unleashing Sensitive Datasets with Distributed Data Science
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- 1 [] Musical introduction to Blaise Thomson
- 2 [] From Intractable to Interactable: Unleashing Sensitive Datasets with Distributed Data Science
- 3 [] Outline
- 4 [] Introduction to Distributed Data Science
- 5 [] The context
- 6 [] What is going wrong?
- 7 [] Distributed Data Science
- 8 [] Sending Algorithms to Data
- 9 [] How Distributed Data Science solves the problem
- 10 [] Usage-based access control
- 11 [] Privacy protections
- 12 [] Dataset-level privacy-enhancing technologies
- 13 [] Collaboration-level privacy-enhancing technologies
- 14 [] Confidential Collaboration: Secure Aggregation and Federated Learning
- 15 [] Secure Aggregation
- 16 [] Federated Learning
- 17 [] An example experimental setup Retinal OCT Kaggle
- 18 [] An example: Error rates
- 19 [] Confidential Collaboration: Private Set Intersection
- 20 [] Private Set Intersection - Simple hashing
- 21 [] Private Set Intersection - more complex hashing
- 22 [] Other use cases
- 23 [] Configurations
- 24 [] Internal use within the company or group
- 25 [] Embedded: Single lead, leveraging partners' data
- 26 [] Data Resellers
- 27 [] Alliance - Consortium of data providers and data scientists
- 28 [] Summary
- 29 [] Sign up for the Bitfount Open Beta Launch at https://www.bitfount.com/!
- 30 [] Data Errors Obfuscation
- 31 [] Histograms with SQL queries on structured or unstructured data
- 32 [] Implicit bias detection or model failures
- 33 [] Theory vs Application
- 34 [] Skipping details approach
- 35 [] OpenMined PySyft
- 36 [] MLOops!
- 37 [] Siri products MLOops!
- 38 [] Wrap up
