Moderator: Debra J Farber, The Shifting Privacy Left Podcast
Panelists: Engin Bozdag, Uber; Vasudha Hegde, DoorDash; Sri Pravallika, Google
As industries increasingly embrace AI technologies, the risk of privacy breaches and unlawful data processing escalates. This panel proposes a comprehensive discussion on identifying essential privacy patterns for AI systems and advocating for privacy by design principles within ML pipelines. We will explore legal obligations surrounding the implementation of robust security measures, delve into technical risks associated with ML algorithms, and examine prevailing privacy-preserving machine learning technologies. Additionally, we will analyze the specific challenges posed by large language models (LLMs) and generative AI, including their susceptibility to privacy and ethical risks. By sharing insights and strategies, this session aims to equip participants with actionable knowledge to enhance privacy in AI/ML practices.
Debra J Farber, The Shifting Privacy Left Podcast
Debra J Farber is a globally-recognized expert in Privacy and Ethical Tech; GTM Advisor to Privacy Tech companies like Privado, Secuvy, Privaini, and Privacy Quest; and Host and Producer of 'The Shifting Privacy Left Podcast.' With 19 years of experience managing privacy and data protection programs, Debra has recently shifted her work left to inspire organizations to unlock the value of their data with privacy by design (PbD) strategies, PETs, and privacy engineering approaches. She helped create ISO31700 (the world's 1st privacy by design standard); served on the IAPP CIPT Exam Development Board; co-authored the TROPT Privacy Tech Landscape and Tech Stack whitepapers; and currently works with the Institute for Operational Privacy Design (IOPD) to develop a comprehensive list of privacy controls based on assessed privacy risk.
Engin Bozdag, Uber
Engin is Uber's Principal Privacy Architect and the team lead of Uber's Privacy Architecture team. He holds a PhD in AI Ethics and authored one of the first works on algorithmic bias. He also helped create ISO31700 (the world's first standard on Privacy by Design) and OWASP AI Security and Privacy Guide. Engin has gained extensive experience in diverse organizational settings, cultivating a privacy-focused career that has evolved over the course of a decade. Throughout his journey, he has assumed multifaceted roles, encompassing legal expertise, privacy engineering,engineering management, research, and consultancy in the realm of privacy.
Vasudha Hegde, DoorDash
Vasudha Hegde leads ML governance and technical product privacy development at DoorDash. Previously at Tiktok, she led the implementation of privacy tooling for vendor privacy assessments, privacy incident response, and operationalization of data subject rights. Vasudha's interests include responsible AI, privacy metrics, and data governance. When not working on privacy, Vasudha enjoys watching and producing live theater.
Sri Pravallika, Autodesk
Sri Pravallika is a Privacy Tech Lead at Google's Privacy Trust Response team. Besides managing the response to complex Privacy incidents, she also leads incident prevention and remediation programs. She built her career in Security with a Masters in Cybersecurity from Northeastern University and eventually pivoted to Privacy.
author = {Debra J Farber and Engin Bozdag and Vasudha Hegde and Sri Pravallika},
title = {Panel: Privacy Design Patterns for {AI} Systems: Threats and Protections},
year = {2024},
address = {Santa Clara, CA},
publisher = {USENIX Association},
month = jun
}