Zero Knowledge Oasis Labs & Privacy with Vishwanath Raman
In this episode, we catch up with Vishwanath Raman, Privacy Architect at Oasis Labs where he works on privacy and confidentiality technologies for Direct to Consumer and Business to Business use cases. His interest and experience span formal methods, applied machine learning, security, and privacy. Vishwa got his PhD in Computer Science at UC Santa Cruz followed by postdoctoral work at Carnegie Mellon.
The Oasis Network is a privacy-enabled blockchain platform for responsible data use. Using a combination of secure computing and privacy technologies, the Oasis Network enables data owners to take control of their data and treat their data as a digital asset via data tokenization. They provide privacy as a service and use secure enclaves and differential privacy in order build a platform for a responsible data economy.
We dive into the way the Oasis system is architected for privacy, who the ideal customers are and what the use cases for this protocol is. We also chatted about some of the tradeoffs in using SGX as well as the recent Binance-led CryptoSafe Alliance announcement where Oasis is the primary infrastructure builder.
Here are some of the papers and links that we discuss:
Chorus: Differential Privacy via Query Rewriting
Towards Practical Differential Privacy for SQL Queries
Problems with machine learning and privacy (one of many)
Towards Practical Differentially Private Convex Optimization
One small correction: Vishwa mentions the number of Validators on Oasis as numbering in the hundreds, but upon checking, we were informed that the number of validators that will participate in the consensus committee at mainnet launch is 70-100. More details in the following blog post: https://docs.oasis.dev/oasis-network-primer/token-metrics-and-distribution
Thank you to this week’s sponsor Trail of Bits.
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Check out this guide for tips on how to build security into your dapps from the start and how to use the Trail of Bits suite of tools for automated vulnerability detection.
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