Privacy

Data is at the core of many of today’s innovations, but often the data used is highly personal or sensitive. My research aims to develop and deploy algorithms and technologies that enable data-driven innovations while preserving privacy. To that end, I study how the data-mining techniques commonly used in the context of the web and social applications have to be changed so that they preserve the rigorous privacy guarantee of differential privacy while remaining useful, and demonstrate how quantitative analyses and machine learning techniques can identify novel privacy risks and support development of tools to empower companies, governments, and individuals to protect privacy.

BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, Benjamin Livshits
26th USENIX Security Symposium (2017)

Privacy in the Amazon Alexa Skills Ecosystem
Abdulaziz Alhadlaq, Jun Tang, Marwan Almaymoni, Aleksandra Korolova
10th Workshop on Hot Topics in Privacy Enhancing Technologies (HotPETS’2017 )

Cloak and Swagger: Understanding Data Sensitivity Through the Lens of User Anonymity
Sai Teja Peddinti, Aleksandra Korolova, Elie Bursztein, Geetanjali Sampemane
IEEE Symposium on Security & Privacy (S&P’2014)

RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Úlfar Erlingsson, Vasyl Pihur, Aleksandra Korolova
21st ACM Conference on Computer and Communications Security (CCS’2014)