Keynotes
Title: When decentralization, security, and privacy are not friends
Carmela Troncoso, Associate Professor @ EPFL
Carmela Troncoso is an Associate Professor at EPFL (Switzerland) where she heads the SPRING Lab. Her work focuses on analyzing, building, and deploying secure and privacy-preserving systems. Troncoso holds a Ph.D. in engineering from KULeuven. Her work on privacy engineering has received multiple awards, and she has been named 40 under 40 in technology by Fortune in 2020.
Decentralization is often seen as a main tool to achieve security and privacy. It has worked in a number of systems, for which decentralization help protect identities and data of users. Thus, it is not a surprise that a new trend of machine learning algorithms opt for decentralization to increase data privacy. In this talk, we will analyze decentralized machine learning proposals and show how they not only don’t improve privacy or robustness, but also increase the surface of attack resulting in less protection than federated alternatives.
Title: Emerging challenges in securing frontier AI systems
Mikel Rodriguez, AI Red Teaming @ Google Deepmind
Dr. Mikel Rodriguez has spent over two decades working in the public and private sector securing the application of Artificial Intelligence in high-stakes consequential environments. At Google DeepMind, Mikel defines and leads the cross-functional AI Red and Blue “ReBl” team to ensure that foundational models are battle-tested with the rigor and scrutiny of real-world adversaries, and help drive research and tooling that will make this red-blue mindset scalable in preparation for AGI. In his role as the Managing Director at MITRE Labs, Mikel built and led the AI Red Team that focuses on deployed AI systems that can be susceptible to bias in their data, attacks involving evasion, data poisoning, model replication; and the exploitation of software flaws to deceive, manipulate, compromise, and render them ineffective. Mikel’s team worked on developing methods to mitigate bias and defend against emerging ML attacks, securing the AI supply chain, and generally ensuring the trustworthiness of AI systems so they perform as intended in mission-critical environments. While at MITRE, his team in collaboration with many industry partners, published ATLAS (Adversarial Threat Landscape for AI Systems) - a knowledge base of adversary tactics, techniques, and case studies for machine learning (ML) systems based on real-world observations, demonstrations from ML red teams and security groups, and the state of the possible from academic research. Mikel firmly believes that AI’s potential will only be realized through collaborations that help produce reliable, resilient, fair, interpretable, privacy preserving, and secure technologies. Mikel received his Ph.D. in 2010 while working at University of Central Florida’s computer vision lab with professor Mubarak Shah. He then moved to Paris where he worked as a post-doctoral research fellow at INRIA.
As advanced AI assistants have become more general purpose, sophisticated and capable, they create new opportunities in a variety of fields, such as education, science and healthcare. Yet the rapid speed of progress has made it difficult to adequately prepare for, or even understand, security and privacy vulnerabilities that may emerge as a result of these new capabilities. Several foreseeable developments in advanced AI assistants including tool use, multimodality, planning and deeper reasoning, and memory have the potential to significantly expand the security and misuse risk profile of these systems. In this talk we will explore a number of best practices and future research directions that can help us better prepare society for managing these risks.
Title: Trustworthy AI and A Cybersecurity Perspective on Large Language Models
Mario Fritz, Faculty @ CISPA Helmholtz Center for Information Security
Prof. Dr. Mario Fritz is a faculty at the CISPA Helmholtz Center for Information Security, an honorary professor at Saarland University, and a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). Until 2018, he led a research group at the Max Planck Institute for Computer Science. Previously, he was a PostDoc at the International Computer Science Institute (ICSI) and UC Berkeley after receiving his PhD from TU Darmstadt and studying computer science at FAU Erlangen-Nuremberg. His research focuses on trustworthy artificial intelligence, especially at the intersection of information security and machine learning. He is Associate Editor of the journal "IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and has published over 100 articles in top conferences and journals. Currently, he is coordinating the Network of Excellence in AI "ELSA -- European Lighthouse on Secure and Safe AI" which is an ELLIS (https://ellis.eu/) initiative that is funded by the EU and connects universities, research institutes, and industry partners across Europe (elsa-ai.eu).
As AI technology is getting increasingly mature, we see a broad deployment of AI in many application domains. However, this increases the demands on properties related to trustworthiness like robustness, privacy, transparency, accountability as well as explainability. Besides the trustworthiness of AI, misinformation and deepfakes are becoming key concerns in terms of the negative effects that AI can have on society. I'll discuss the larger ecosystem around misinformation and different approaches to mitigate these pressing issues in the future. Finally, Large Language Models (LLMs) like GPT4 have demonstrated how AI deployment is reaching millions of users, which in turn puts a magnifying glass on some of the issues mentioned before. I'll demonstrate cybersecurity concerns and threats that emerge from the recent trend of application-integrated LLMs and AI assistants as well as sketch how future development will initiate new research challenges in this domain.