Please note: This PhD seminar will take place online.
Miti Mazmudar, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ian Goldberg
Differential privacy (DP) allows data analysts to query databases that contain users’ sensitive information while providing a quantifiable privacy guarantee to users. Recent interactive DP systems such as APEx provide accuracy guarantees over the query responses but fail to support a large number of queries with a limited total privacy budget, as they process incoming queries independently from past queries.
In this talk, I will present an interactive, accuracy-aware DP query engine, CacheDP, which utilizes a differentially private cache of past responses, to answer the current workload at a lower privacy budget, while meeting strict accuracy guarantees. We integrate complex DP mechanisms with our structured cache, through a novel cache-aware DP cost optimization. Our thorough evaluation illustrates that CacheDP can accurately answer various workload sequences, while lowering the privacy loss as compared to related work.