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

MBZUAI Talks | How will our cryptographic toolkit be impacted by quantum computers and Machine Learning? | Professor Najwa Aaraj

Bio:
Dr. Najwa Aaraj is a Professor in the Department of Machine Learning @ Mohamed Bin Zayed University of Artificial Intelligence. Dr Najwa Aaraj is Chief Researcher at the Cryptography Research Centre at Technology Innovation Institute (TII) and leads the research and development of cryptographic and technologies, including post-quantum cryptography (PQC) software libraries and hardware implementations, lightweight cryptographic libraries for embedded and RF systems, cryptanalysis, quantum random number generation, and applied machine learning for cryptographic technologies. She is also Acting Chief Researcher at TII’s Autonomous Robotics Research Centre, which is dedicated to breakthrough developments in robotics and autonomy. Dr Najwa Aaraj holds a PhD with Highest Distinction in Applied Cryptography and Embedded Systems Security from Princeton University (USA). She brings to her roles over 18 years of experience in applied cryptography, trusted platforms, security architecture for embedded systems, software exploit detection and prevention systems, and biometrics.


Abstract:
A part of this talk will focus on the impact of quantum computers on cryptographic algorithms and the changes that are required to protect against both passive and active quantum attacks. We cover 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. We 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 briefly discuss the role of Machine Learning in advancing cyber security solutions, including (1) cryptographic schemes for privacy preserving technologies; (2) theoretical and implementation-focused (side channel) cryptanalysis techniques; and (3) vulnerability management and automated incident response systems. We cover the role of cryptography in securing Machine Learning models by (1) ensuring confidentiality of both data &, 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; and (4) introducing cryptographic randomness in training Deep Neural Networks