Data sharing principles for Digital Transformation
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The document discusses data sharing principles essential for digital transformation, highlighting customer ownership, AI-driven outcomes, and the role of differential privacy in protecting individual privacy. It emphasizes collaboration among industries to harness open data while addressing challenges like algorithmic discrimination due to biased training data. Key tools and services, including Azure Data Share and privacy-preserving technologies, are mentioned as means to facilitate data sharing and insights among organizations.
Data sharing principles for Digital Transformation
- 1. Data sharing principles for Digital Transformation 33º Encuentro de la Economía Digital y las Telecomunicaciones Palacio de la Magdalena - Santander Monday, 02nd September 2019
- 2. Data Sharing & the Open Data Opportunity: The challenges and road to progress
- 9. do we
- 10. Open Data Initiative principles 1 Customer ownership and control 3 Open and extensible 2 AI-Driven Business Outcomes
- 12. Challenges . . .
- 13. Advancing open data is also important to:
- 14. “Amazon’s computer models were trained … by observing patterns in resumes submitted to the company over a 10-year period. Most came from men, a reflection of male dominance across the tech industry.” “Many AI systems, e.g. face recognition tools, rely on machine learning algorithms that are trained with labeled data. It has recently been shown that algorithms trained with biased data have resulted in algorithmic discrimination.”
- 15. The European Commission is expected to study ways to provide greater leeway for data sharing under competition laws Competition Policy for a Digital Era
- 17. Differential privacy introduces a small amount of noise, enabling researchers to extract useful answers, while protecting individuals’ privacy Differential Privacy Research Platform Shared data model and workflow built in Azure, with rich semantics and multi-party confidential compute with differential privacy to unlock the value
- 18. A structured approach to making progress with data sharing
- 19. Standard legal terms for data access and sharing Privacy preserving technologies Software tools to enable data sharing
- 21. Existing At rest Encrypt inactive data when stored in blob storage, database, etc. Examples include: Azure Storage Service Encryption for Data at Rest SQL Server Transparent Database Encryption (TDE) In use Protect/Encrypt data that is in use during computation Examples include: Trusted Execution Environments such as Intel SGX and VBS Homomorphic encryption In transit Encrypt data that is flowing between untrusted public or private networks Examples include: HTTPS TLS
- 22. Working with industry to collaborate on confidential computing open source technology and frameworks
- 23. Azure Data Share is a simple service for big data sharing, helping organizations to: • Enhance insights with data from partners and customers • Form industry-specific consortium to pool data among members • Innovate custom solutions and expand markets via new service capabilities Azure Data Factory Azure Databricks (Prep-only) Azure Data Lake Storage Azure SQL Data Warehouse Power BI Ingest & Prep Store Model & Serve Visualize Manufacturer Azure Data ShareSuppliers Azure Data Share Azure Data Share Company A Company B Company C Azure Data Share Share
- 25. Thank you To learn more about data sharing, open data, and recently released data use agreements and join the conversation, visit news.microsoft.com/datainnovation