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"Demystifying Privacy Preserving Computing" by Tejas Chopra (Strange Loop 2022)
Discover the basics of privacy-preserving computing, including differential privacy, zero-knowledge proof, and homomorphic encryption, with real-world applications from Apple and Google.
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Demystifying Privacy Preserving Computing:
- Differential privacy: mathematically prove privacy
- Split private data into many parts to maintain privacy
- Zero-knowledge proof: prove knowledge without revealing data
- Client-side processing
- Processing on encrypted data
- Federated learning: learning on mobile devices
- Homomorphic encryption: performing computations on encrypted data
- Secure multiparty computation: multiple parties colluding to perform computation
- Privacy preserving computation uses mathematical proof
- By-products: zero-knowledge proof and digital signatures
- Companies like Box, Apple, and Microsoft using privacy preserving computation
- Apple uses differential privacy
- Google uses federated learning
- sh glad that good actors also unnecessarily disclose personal data
- Zero-knowledge proof
- Secure Multiparty Computation
- Clientside processing
- Homomorphic encryption
- Federated learning
- Client-side processing is key