research-article

A data- and workload-aware algorithm for range queries under differential privacy

Authors:

Yue WangAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 7, Issue 5

Pages 341 - 352

https://doi.org/10.14778/2732269.2732271

Published: 01 January 2014 Publication History

Abstract

We describe a new algorithm for answering a given set of range queries under ε-differential privacy which often achieves substantially lower error than competing methods. Our algorithm satisfies differential privacy by adding noise that is adapted to the input data and to the given query set. We first privately learn a partitioning of the domain into buckets that suit the input data well. Then we privately estimate counts for each bucket, doing so in a manner well-suited for the given query set. Since the performance of the algorithm depends on the input database, we evaluate it on a wide range of real datasets, showing that we can achieve the benefits of data-dependence on both "easy" and "hard" databases.

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Cited By

Zhao PZhang KZhang HChen H(2025)Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125279259:COnline publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125279
Wang LYe QHu HMeng X(2024)PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential PrivacyProceedings of the VLDB Endowment10.14778/3681954.368198117:11(3031-3044)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3681981
Ni WMiao XZhao XWu YLiang SYin J(2024)Automatic Data Repair: Are We Ready to Deploy?Proceedings of the VLDB Endowment10.14778/3675034.367505117:10(2617-2630)Online publication date: 6-Aug-2024
https://dl.acm.org/doi/10.14778/3675034.3675051
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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 7, Issue 5

Janary 2014

100 pages

ISSN:2150-8097

Editors:
H. V. Jagadish
University of Michigan
,
Aoying Zhou
East Normal University, China

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VLDB Endowment

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Published: 01 January 2014

Published in PVLDB Volume 7, Issue 5

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Citations

Cited By

Zhao PZhang KZhang HChen H(2025)Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125279259:COnline publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125279
Wang LYe QHu HMeng X(2024)PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential PrivacyProceedings of the VLDB Endowment10.14778/3681954.368198117:11(3031-3044)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3681981
Ni WMiao XZhao XWu YLiang SYin J(2024)Automatic Data Repair: Are We Ready to Deploy?Proceedings of the VLDB Endowment10.14778/3675034.367505117:10(2617-2630)Online publication date: 6-Aug-2024
https://dl.acm.org/doi/10.14778/3675034.3675051
Wang SPeng YLi JWen ZLi ZYu SWang DYang W(2024)Privacy Amplification via Shuffling: Unified, Simplified, and TightenedProceedings of the VLDB Endowment10.14778/3659437.365944417:8(1870-1883)Online publication date: 31-May-2024
https://dl.acm.org/doi/10.14778/3659437.3659444
Lahjouji NGhayyur SHe XMehrotra SLuo BLiao XXu JKirda ELie D(2024)ProBE: Proportioning Privacy Budget for Complex Exploratory Decision SupportProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3670394(1924-1938)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3670394
Lv CXiao XZhang LYu TQuek TGao DZhou JCardenas A(2024)Publishing Common Neighbors Histograms of Social Networks under Edge Differential PrivacyProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3637646(1099-1113)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3634737.3637646
Sun XYe QHu HDuan JXue QWo TZhang WXu J(2024)Generating Location Traces With Semantic- Constrained Local Differential PrivacyIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.348071219(9850-9865)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3480712
Fahrenkrog-Petersen Svan der Aa HWeidlich M(2024)Privacy-Aware Analysis based on Data Series2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00406(5365-5370)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00406
Sun XYe QHu HDuan JWo TXu JYang R(2024)LDPRecover: Recovering Frequencies from Poisoning Attacks Against Local Differential Privacy2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00132(1619-1631)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00132
Fu CLi HLou JCui J(2024)Towards answering analytical query over hierarchical histogram under untrusted serversDistributed and Parallel Databases10.1007/s10619-024-07447-343:1Online publication date: 12-Nov-2024
https://dl.acm.org/doi/10.1007/s10619-024-07447-3
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