Master’s Thesis Presentation • Data Systems • Differentially Private Online AggregationExport this event to calendar

Monday, December 6, 2021 — 12:00 PM EST

Please note: This master’s thesis presentation will be given online.

Harry Sivasubramaniam, Master’s candidate
David. R. Cheriton School of Computer Science

Supervisor: Professor Xi He

Database operations are often preformed in batch mode, i.e., the analyst issuing the query must wait till the database has been processed in its entirety before getting feedback. Batch mode is inadequate for large databases since queries can take several hours to process and often an analyst is satisfied with an approximation. Online aggregation greatly improves user experience and saves resources by providing continuous feedback through running confidence intervals. Further, it provides an interface for users to terminate early and allocate resources elsewhere once a sufficient accuracy level has been achieved. Until now, online aggregation has not been studied in the differentially private setting.

In this work, we formulate differentially private online aggregation such that it captures the trade-offs between privacy, accuracy and usability. Further, we develop a family of differentially private mechanisms, which includes our optimal Gap mechanisms, for answering AVG, COUNT, and SUM queries with WHERE conditions. Also, we develop various optimizations to improving the accuracy of Gap mechanism and empirically confirm that the Gap mechanisms preform the best overall.


To join this master’s thesis presentation on Zoom, please go to https://us02web.zoom.us/j/87204653550?pwd=WlhuU3VjQjVadVdkVUR0dHloWHBFUT09.

Meeting ID: 872 0465 3550
Passcode: QBi9N3

Location 
Online master’s thesis presentation
200 University Avenue West

Waterloo, ON N2L 3G1
Canada
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