Privacy by Design for JavaScript Data Systems
summary
Conference-level claim: 140 countries have strong privacy laws and average breach cost is ~$5M, and 87% of US citizens are reidentifiable from three attributes. The session shows a JavaScript architecture that uses node-differential-privacy and the Web Crypto API, applies K-anonymity (example K=5), tokenizes card data at collection for real-time fraud detection, runs federated learning with differential updates so models train locally and share privacy-preserved aggregates, and cites homomorphic encryption keeping model accuracy near 95%.
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chapters (10)
- 0:00Introduction to Privacy by Design
- 1:05The Importance of Privacy
- 2:21Core Principles of Privacy Design
- 3:25Techniques for Privacy Preservation
- 6:50Advanced Privacy Technologies
- 7:30Case Studies in Privacy Implementation
- 9:48Architectural Blueprint for Privacy
- 11:47JavaScript Tools and Patterns
- 12:36Common Pitfalls to Avoid
- 14:41Key Takeaways and Conclusion
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