ATLANTIC: making database differentially private and faster with accuracy guarantee
Name
p2755-cao.pdf
Description
Published version
Size
549.22 KB
Format
Adobe PDF
Checksum (MD5)
b243fe911ee57b0604f7b2e2ebca0b65
Author(s)
Cao, Lei
Xiao, Dongqing
Yan, Yizhou
Madden, Samuel
Li, Guoliang
Date Issued
2021
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Citation
Cao, Lei, Xiao, Dongqing, Yan, Yizhou, Madden, Samuel and Li, Guoliang. 2021. "ATLANTIC: making database differentially private and faster with accuracy guarantee." Proceedings of the VLDB Endowment, 14 (12).
Version
Final published version
Abstract
Differential privacy promises to enable data sharing and general data analytics while protecting individual privacy. Because the private data is often stored in the form of relational database that supports SQL queries, making SQL-based analytics differentially private is thus critical. However, the existing SQL-based differentially private systems either only focus on specific type of SQL queries such as COUNT or substantially modify the database engine, thus obstructing adoption in practice. Worse yet, these systems often do not guarantee the desired accuracy by the applications. In this demonstration, using the driving trace workload from Cambridge Mobile Telematics (CMT), we show that our ATLANTIC system, as a database middleware, enforces differential privacy for real-world SQL queries with provable accuracy guarantees and is compatible with existing databases. Moreover, using a sampling-based technique, ATLANTIC significantly speeds up the query execution, yet effectively amplifying the privacy guarantee.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.14778/3476311.3476337
