January 30, 1-2 PM, Location In Campus Group.
We are excited to invite you to an upcoming event hosted by the Women in CyberSecurity (WiCyS) student chapter at UCI! Joann Chen (https://joannqc.com/), PhD candidate in the EECS Department at UCI, will be giving a talk about her research entitled “Envisioning the Future of Digital Privacy: Bridging Theory and Practice in Differential Privacy”. This event will also serve as a rehearsal for Joann’s job talk as she is currently on the academic job market. This event will be beneficial for anyone who is interested in academic or industry research positions to see what a job talk is like and provide feedback. Faculty, researchers, & students are welcome!
Joann’s talk will explore novel technologies related to differential privacy (DP), privacy-enhancing technologies, and privacy in machine learning. Her experience includes work on differentially private DNS resolution, DP for data stream release, differentially private resource allocation, and quantifying privacy risks in machine learning. Please see Joann’s bio and talk abstract below. Snacks and beverages will be provided!
Please RSVP here: https://forms.gle/XKjw5TH1HhofSnR2A. If you would like to join via Zoom, please RSVP and the Zoom link will be provided.
Abstract: In today’s technologically driven world, personal data stands at a crucial juncture, representing both immense potential and significant peril. Such potential, when leveraged properly, can lead to groundbreaking advancements in sectors such as healthcare and finance. Yet, the misuse of this same data can result in severe privacy breaches. This talk is dedicated to navigating the complex terrain of personal data privacy, highlighting Differential Privacy (DP) as a pivotal solution that provides strong, provable privacy guarantees. With a focus on DP-aware system design, this talk delves into the challenges of integrating DP into various systems, emphasizing the necessity for infrastructures that prioritize privacy preservation. It outlines the progression of DP from theoretical concepts to practical implementations, detailing both the challenges encountered and the progress made in applying academic DP concepts to real-world solutions. Moreover, this talk extends beyond the scope of DP, exploring the broader realm of privacy-enhancing technologies (PETs). These technologies are crucial not only for meeting legal compliance and ensuring privacy in systems and machine learning but also for paving the way towards a future where data utility and individual privacy coexist in harmony.
Bio: Joann Chen is a fifth-year Ph.D. candidate in the EECS department at UC Irvine, advised by Dr. Zhou Li. Her research interests center around Differential Privacy (DP), privacy-enhancing technologies, and privacy in machine learning. She has experience in quantifying privacy risks in machine learning and building DP into DNS resolution, data stream release, resource allocator, ad conversion measurements, and network data synthesis. Her research aims to bridge the gap between DP theory and its practical applications for real-world deployments.