ATLANTIC: making database differentially private and faster with accuracy guarantee
<jats:p>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...
Main Authors: | Cao, Lei, Xiao, Dongqing, Yan, Yizhou, Madden, Samuel, Li, Guoliang |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | English |
Published: |
VLDB Endowment
2022
|
Online Access: | https://hdl.handle.net/1721.1/143773 |
Similar Items
-
Machine Learning for Databases
by: Li, Guoliang, et al.
Published: (2022) -
SWIFT: Mining Representative Patterns from Large Event Streams
by: Yan, Yizhou, et al.
Published: (2021) -
AI Meets Database: AI4DB and DB4AI
by: Li, Guoliang, et al.
Published: (2022) -
AutoOD: Automatic Outlier Detection
by: Cao, Lei, et al.
Published: (2023) -
Epoch-based commit and replication in distributed OLTP databases
by: Lu, Yi, et al.
Published: (2021)