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: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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VLDB Endowment
2022
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| Online Access: | https://hdl.handle.net/1721.1/143773 |
| _version_ | 1811086081973551104 |
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| author | Cao, Lei Xiao, Dongqing Yan, Yizhou Madden, Samuel Li, Guoliang |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Cao, Lei Xiao, Dongqing Yan, Yizhou Madden, Samuel Li, Guoliang |
| author_sort | Cao, Lei |
| collection | MIT |
| description | <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. 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.</jats:p> |
| first_indexed | 2024-09-23T13:20:36Z |
| format | Article |
| id | mit-1721.1/143773 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:20:36Z |
| publishDate | 2022 |
| publisher | VLDB Endowment |
| record_format | dspace |
| spelling | mit-1721.1/1437732023-01-26T21:55:10Z ATLANTIC: making database differentially private and faster with accuracy guarantee Cao, Lei Xiao, Dongqing Yan, Yizhou Madden, Samuel Li, Guoliang Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory <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. 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.</jats:p> 2022-07-15T16:32:04Z 2022-07-15T16:32:04Z 2021 2022-07-15T16:26:36Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/143773 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). en 10.14778/3476311.3476337 Proceedings of the VLDB Endowment Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf VLDB Endowment VLDB Endowment |
| spellingShingle | Cao, Lei Xiao, Dongqing Yan, Yizhou Madden, Samuel Li, Guoliang ATLANTIC: making database differentially private and faster with accuracy guarantee |
| title | ATLANTIC: making database differentially private and faster with accuracy guarantee |
| title_full | ATLANTIC: making database differentially private and faster with accuracy guarantee |
| title_fullStr | ATLANTIC: making database differentially private and faster with accuracy guarantee |
| title_full_unstemmed | ATLANTIC: making database differentially private and faster with accuracy guarantee |
| title_short | ATLANTIC: making database differentially private and faster with accuracy guarantee |
| title_sort | atlantic making database differentially private and faster with accuracy guarantee |
| url | https://hdl.handle.net/1721.1/143773 |
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