Sadegh Riazi on Collaborative AI Without Compromising Data Privacy.
Subscribe: Apple • Spotify • Overcast • Google • AntennaPod • Podcast Addict • Amazon • RSS.
Sadegh Riazi, CEO and co-founder of Pyte, a startup offering secure, encrypted data collaboration solutions, enabling partners to maximize insights without compromising privacy or data integrity. As reliance on AI and analytics grows, ensuring data privacy and security becomes critical. Regulations restrict directly sharing or accessing sensitive datasets, causing roadblocks for data science and ML teams. Pyte provides secure multi-party computation (SMPC) solutions that allow teams to collaborate on models and analysis with real data, without ever exposing the raw data. Their tools facilitate faster, more flexible analytics while guaranteeing confidentiality.
Interview highlights – key sections from the video version:
- Data collaboration challenges and solutions
- Secure computation for generative AI models
- Data collaboration and security in AI
- Securely sharing data for machine learning
- Secure multi-party computation for data matching
- Securely sharing data for AI model training
- SMPC, LLM, and data collaboration
Related content:
- A video version of this conversation is available on our YouTube channel.
- Improving Data Privacy in AI Systems Using Secure Multi-Party Computation
- Entity Resolution: Insights and Implications for AI Applications
- Get Ready For Confidential Computing
- Sadegh Riazi: Confidential Computing for Machine Learning
- Nir Shavit: LLMs on CPUs, Period
- Jeff Jonas: Using Data and AI to Democratize Entity Resolution and Master Data Management
- Philipp Moritz and Goku Mohandas: Navigating the Nuances of Retrieval Augmented Generation
- Sharon Zhou and Greg Diamos: Software Meets Hardware – Enabling AMD for Large Language Models
If you enjoyed this episode, please support our work by encouraging your friends and colleagues to subscribe to our newsletter:
