How will our cryptographic toolkit be Impacted by quantum computers and Machine Learning?

This talk focusses on the impact of quantum computers on cryptographic algorithms and the changes that are required to protect against both passive and active quantum attacks. It covers the new set of post quantum cryptographic (PQC) schemes that are being proposed to protect current and future systems, the implied security thereof, as well as their practicality when deployed in real world systems. I also discuss standardization efforts, industry challenges, and complexities of the
roadmap to transition current cryptographic systems and secure communications solutions to quantum-resistant alternatives. We will also outline the role of machine learning in advancing cybersecurity solutions, including:

1. Cryptographic schemes for privacy preserving technologies.
2. Theoretical and implementation-focused (side channel) cryptanalysis techniques.
3. Vulnerability management and automated incident response systems.

We cover the role of cryptography in securing machine learning models:

1. Ensuring confidentiality of both data and model during training and classification.
2. Protection of models from being tampered-with or introducing bias for profit or control.
3. Protection against model poisoning.
4. Introducing cryptographic randomness in training Deep Neural Networks.