Abstract: The adoption of AI systems in daily life and critical applications is becoming ubiquitous. This wide availability has at the same time raise questions about the trustworthiness, security, and privacy implications of using these systems. While novel technologies and methodologies have been emerging to protect the privacy and security of AI Systems, there are still open challenges that need to be addressed by the community. Over the past years, my research has focused on the creation of defenses to protect the machine learning pipeline and the design of privacy-aware methodologies to enable the training of accurate machine learning models without transmitting the training data to a central place. In this talk, I will first provide an overview of the challenges and threats inherent to the machine learning pipeline in traditional setups where all the training data is available in the same place and some mitigation techniques to deter these attacks. In the second part of the talk, I will cover a game-changing and privacy-by-design paradigm known as federated learning (FL), where data owners do not need to share or transfer their data to collaboratively train a model. During this part of the talk, I will present multiple cutting-edge approaches, interesting aspects of making FL available in a product and some open research directions.

Bio:  Nathalie Baracaldo leads the AI Security and Privacy Solutions team and is a Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Nathalie is passionate about delivering machine learning solutions that are highly accurate, withstand adversarial attacks and protect data privacy. Nathalie has led her team to the design of the IBM Federated Learning framework, which is now part of the Watson Machine Learning product. In 2020, Nathalie received the IBM Master Inventor distinction for her contributions to IBM Intellectual Property and innovation. Nathalie also received the 2021 Corporate Technical Recognition, one of the highest recognitions provided to IBMers for breakthrough technical achievements that have led to notable market and industry success for IBM. This recognition was awarded for Nathalie's contribution to the Trusted AI Initiative. Nathalie has received multiple best paper awards and published in top-tier conferences and journals. Nathalie’s research interests include security and privacy, distributed systems and machine learning. Nathalie received her Ph.D. degree from the University of Pittsburgh in 2016.

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