Please note: This master’s thesis presentation will take place online.
Shufan Zhang, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Xi He
Recent years have witnessed the adoption of differential privacy (DP) in practical database query systems. Such systems, like PrivateSQL and FLEX, allow data analysts to query sensitive data while providing a rigorous and provable privacy guarantee. However, existing systems may use more privacy budgets than necessary in certain cases where different data analysts with different privilege levels ask a similar or even the same query. In this paper, we propose DProvSQL, a fine-grained privacy provenance framework that tracks the privacy loss to each single data analyst and we build algorithms that make use of this framework to maximize the number of queries that could be answered. Preliminary empirical results show that our approach can answer 6x more queries than the baseline approach on average meanwhile the answer from our approach is 1.5x to 5.6x more accurate.
To join this master’s thesis presentation on Zoom, please go to https://uwaterloo.zoom.us/j/91301136806?pwd=UXppTG9rVXV4WVkwdVNPbi95cGpCZz09.
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Waterloo, ON N2L 3G1