From Basic Idea to Impact: the story of modern cryptography
summary
Chronicles how a handful of concrete ideas become everyday infrastructure: the 1976 Diffie-Hellman/RSA blueprint for public key systems that takes roughly 25 years to standardize, Shor’s 1994 quantum algorithm and its current 143 factoring milestone prompting post-quantum work, Ajtai/Regev’s lattice Learning With Errors giving quantum‑resilient primitives, and zero knowledge constructions that let one prove factorization or identity without revealing secrets. Discusses prototypes for encrypted ML, proof‑based audit trails for surveillance warrants, and efficiency versus practicality tradeoffs.
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chapters (7)
- 0:00Start
- 42:00After 30+ years of working on methods to ensure the privacy and correctness of computation as well as communication
- 43:03Power of ML comes from Data of individuals Ensure privacy of both data & model during training and classifying (even when not mandated by current regulations) to maintain "power to the people"
- 46:313. Adversarial ML where clever manipulations of an input by an adversary can cause misclassifications and fool applications emerges as a real threat in applications such as self driving cars or virus detection
- 48:16Trace the unauthorized use of your data and model Develop methods to trace training data used for learning a model without introducing new vulnerabilities.
- 48:49Trace the unauthorized use of your data/model How about tracing unauthorized use of the model ? Develop methods to water mark (or leash) your models.
- 50:54Proper Use of Proper Randomness Randomness seems key to training phase in DNN, what type of randomness? does it affect stability? Is secrecy of the randomness important?
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