I graduated with a MSIT degree with specialization in Very Large Information Systems from CMU, where I worked with Dave Andersen in his FAWN group.
My research interests revolve around systems and how they can be improved to benefit users, providers, and the environment. I am currently working in the area of system security and privacy, with a particular focus on private data analytics.
In my thesis I present the first steps towards usable privacy protection mechanisms for data analytics systems. Usable in my context means more queries and less distortion of answers than previous systems. I developed the UniTraX system, the first differentially private analytics system that is able to support personalized privacy budgets without giving up on answer accuracy.
With my thesis work I further laid the foundations for Diffix, a commercially deployed private analytics system. Diffix uses a defense in depth approach to privacy protection. It provides guarantees of usability to analysts and is one of the first systems to focus on usability in order to foster industry adoption of private analytics systems.
Before my work on usable privacy with Paul, Deepak, and Matteo, I improved user satisfaction during peak system loads with Allen and devised energy efficient and failure resilient mechanisms for large-scale distributed environments with Umut and Dave.
During my time in different research groups I always had and still have the pleasure to work with amazing people.
“Towards Usability in Private Data Analytics”. PhD thesis. Department of Computer Science, Technische Universität Kaiserslautern, 2019.
UniTraX: Protecting Data Privacy with Discoverable Biases. With Fabienne Eigner, Matteo Maffei, Paul Francis, and Deepak Garg. Conference on Principles of Security and Trust (POST), 2018. [Technical report with proofs][Gitlab repository]
Persistent, Protected and Cached: Building Blocks for Main Memory Data Stores. With Iulian Moraru, David G. Andersen, Michael Kaminsky, Nathan Binkert, Niraj Tolia, and Parthasarathy Ranganathan. Carnegie Mellon University Parallel Data Lab Technical Report CMU-PDL-11-114v2, Nov 2012. [v1 Dec 2011]