Simple And Scalable Encryption Of Data In Use For Analytics And Machine Learning With Opaque Systems
Data Engineering Podcast
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The Performance Overhead of Confident Computing
Overhead is determined by how frequently data needs to be moved in and out of the environment. Overhead has gone over because newer generation Intel machines have several gigabytes of memory available to process its workloads. We also now have confidential virtual machines where the entire virtual machine is effectively an enclave. Policy enforcement can be granular as well as generic. You can only run computations on certain data rows that meet a certain criteria.
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Summary
Encryption and security are critical elements in data analytics and machine learning applications. We have well developed protocols and practices around data that is at rest and in motion, but security around data in use is still severely lacking. Recognizing this shortcoming and the capabilities that could be unlocked by a robust solution Rishabh Poddar helped to create Opaque Systems as an outgrowth of his PhD studies. In this episode he shares the work that he and his team have done to simplify integration of secure enclaves and trusted computing environments into analytical workflows and how you can start using it without re-engineering your existing systems.
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- Your host is Tobias Macey and today I'm interviewing Rishabh Poddar about his work at Opaque Systems to enable secure analysis and machine learning on encrypted data
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what you are building at Opaque Systems and the story behind it?
- What are the core problems related to security/privacy in data analytics and ML that organizations are struggling with?
- What do you see as the balance of internal vs. cross-organization applications for the solutions you are creating?
- comparison with homomorphic encryption
- validation and ongoing testing of security/privacy guarantees
- performance impact of encryption overhead and how to mitigate it
- UX aspects of not being able to view the underlying data
- risks of information leakage from schema/meta information
- Can you describe how the Opaque Systems platform is implemented?
- How have the design and scope of the product changed since you started working on it?
- Can you describe a typical workflow for a team or teams building an analytical process or ML project with your platform?
- What are some of the constraints in terms of data format/volume/variety that are introduced by working with it in the Opaque platform?
- How are you approaching the balance of maintaining the MC2 project against the product needs of the Opaque platform?
- What are the most interesting, innovative, or unexpected ways that you have seen the Opaque platform used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Opaque Systems/MC2?
- When is Opaque the wrong choice?
- What do you have planned for the future of the Opaque platform?
Contact Info
- Website
- @Podcastinator on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links
- Opaque Systems
- UC Berkeley RISE Lab
- TLS
- MC²
- Homomorphic Encryption
- Secure Multi-Party Computation
- Secure Enclaves
- Differential Privacy
- Data Obfuscation
- AES == Advanced Encryption Standard
- Intel SGX (Software Guard Extensions)
- Intel TDX (Trust Domain Extensions)
- TPC-H Benchmark
- Spark
- Trino
- PyTorch
- Tensorflow
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
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