DATE 2021 became a virtual conference due to the worldwide COVID-19 pandemic (click here for more details)

Taking into consideration the continued erratic development of the worldwide COVID-19 pandemic and the accompanying restrictions of worldwide travelling as well as the safety and health of the DATE community, the Organizing Committees decided to host DATE 2021 as a virtual conference in early February 2021. Unfortunately, the current situation does not allow a face-to-face conference in Grenoble, France.

The Organizing Committees are working intensively to create a virtual conference that gives as much of a real conference atmosphere as possible.

 
 
 
 

M05 Automation goes both ways: ML for security and security for ML

Start
Monday, 1 February 2021 15:00
End
Monday, 1 February 2021 18:40
Organizer
Alexandra Dimitrenko, University of Würzburg, United States
Organizer
Siddarth Garg, New York University, United States
Organizer
Farinaz Koushanfar, University of California San Diego, United States

This tutorial focuses on the state of the art research in the intersection of AI and security. On the one hand, recent advances in Deep Learning (DL) have enabled a paradigm shift to include machine intelligence in a wide range of autonomous tasks. As a result, a largely unexplored surface has opened up for attacks jeopardizing the integrity of DL models and hindering their ubiquitous deployment across various intelligent applications. On the other hand, DL-based algorithms are also being employed for identifying several security vulnerabilities on long streams of multi-modal data and logs. In distributed complex settings, often times this is the only way to monitor and audit the security and robustness of the system. The tutorial integrates the views from three experts: Prof. Garg explores the emerging landscape of "adversarial ML" with the goal of answering basic questions about the trustworthiness and reliability of modern machine learning systems. Prof. Dmitrienko presents novel usages of federated and distributed learning for risk detection on mobile platforms with proof-of-concept realization and evaluation on data from millions of users. Prof. Koushanfar discusses how end-to-end automated frameworks based on algorithm/hardware co-design help with both (1) realizing accelerated low-overhead shields against DL attacks, and (2) enabling low overhead and real-time intelligent security monitoring.