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