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Answering Range Queries Under Local Differential Privacy

Author:

Tejas KulkarniAuthors Info & Claims

SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data

Pages 1832 - 1834

https://doi.org/10.1145/3299869.3300102

Published: 25 June 2019 Publication History

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Abstract

Counting the fraction of a population having an input within a specified interval i.e. range count query is a fundamental database operation. Range count queries can also be used to compute other interesting statistics such as quantiles. The framework of differential privacy [6] (DP) is becoming a standard for privacy-preserving data analysis [1]. While many works address the problem of range counting queries in the trusted aggregation model, surprisingly, this problem has not been addressed specifically under untrusted aggregation (local DP [10]). In this work we study the problem of answering 1-dimensional range count queries under the constraint of LDP.

References

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Graham Cormode, Somesh Jha, Tejas Kulkarni, Ninghui Li, Divesh Srivastava, and Tianhao Wang. 2018a. Privacy at Scale: Local Differential Privacy in Practice. (2018). Tutorial at SIGMOD and KDD.

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Graham Cormode, Tejas Kulkarni, and Divesh Srivastava. 2018b. Marginal Release Under Local Differential Privacy. In ACM SIGMOD International Conference on Management of Data (SIGMOD) . https://arxiv.org/abs/1711.02952

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Cited By

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Li YXu LZhang JZhang L(2025)WF-LDPSR: A local differential privacy mechanism based on water-filling for secure release of trajectory statistics dataComputers & Security10.1016/j.cose.2024.104165148(104165)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104165
Li KZhang HXu YLiu Z(2024)A Range Query Scheme for Spatial Data with Shuffled Differential PrivacyMathematics10.3390/math1213193412:13(1934)Online publication date: 21-Jun-2024
https://doi.org/10.3390/math12131934
DeSmet CCook D(2024)HydraGAN: A Cooperative Agent Model for Multi-Objective Data GenerationACM Transactions on Intelligent Systems and Technology10.1145/365398215:3(1-21)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3653982
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Index Terms

Answering Range Queries Under Local Differential Privacy
1. Security and privacy
  1. Security services
    1. Privacy-preserving protocols
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Theory of database privacy and security

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Published In

SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data

June 2019

2106 pages

ISBN:9781450356435

DOI:10.1145/3299869

General Chairs:
Peter Boncz
CWI & Vrije Universiteit Amsterdam, The Netherlands
,
Stefan Manegold
CWI & Universiteit Leiden, The Netherlands
,
Program Chairs:
Anastasia Ailamaki
EPFL, Switzerland
,
Amol Deshpande
University of Maryland, USA
,
Tim Kraska
MIT, USA

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Association for Computing Machinery

New York, NY, United States

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Published: 25 June 2019

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SIGMOD/PODS '19

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SIGMOD

SIGMOD/PODS '19: International Conference on Management of Data

June 30 - July 5, 2019

Amsterdam, Netherlands

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SIGMOD '19 Paper Acceptance Rate 88 of 430 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Citations

Cited By

View all

Li YXu LZhang JZhang L(2025)WF-LDPSR: A local differential privacy mechanism based on water-filling for secure release of trajectory statistics dataComputers & Security10.1016/j.cose.2024.104165148(104165)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104165
Li KZhang HXu YLiu Z(2024)A Range Query Scheme for Spatial Data with Shuffled Differential PrivacyMathematics10.3390/math1213193412:13(1934)Online publication date: 21-Jun-2024
https://doi.org/10.3390/math12131934
DeSmet CCook D(2024)HydraGAN: A Cooperative Agent Model for Multi-Objective Data GenerationACM Transactions on Intelligent Systems and Technology10.1145/365398215:3(1-21)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3653982
Wang SPeng YChen KYang W(2024)Optimal Locally Private Data Stream AnalyticsIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621194(31-40)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621194
Yang MGuo TZhu TTjuawinata IZhao JLam K(2024)Local differential privacy and its applications: A comprehensive surveyComputer Standards & Interfaces10.1016/j.csi.2023.10382789(103827)Online publication date: Apr-2024
https://doi.org/10.1016/j.csi.2023.103827
Mirval JBouganim LSandu Popa I(2024)Handling Dropouts in Federating Learning with Personal Data Management SystemsTransactions on Large-Scale Data- and Knowledge-Centered Systems LVI10.1007/978-3-662-69603-3_2(37-75)Online publication date: 21-Jul-2024
https://doi.org/10.1007/978-3-662-69603-3_2
Ma CYuan LHan LDing MBhaskar RLi J(2023)Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential PrivacyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313704735:4(3619-3631)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2021.3137047
Wu Y(2023)An Effective Incentive Mechanism for Individual Data Sharing2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA)10.1109/ICPECA56706.2023.10076130(499-503)Online publication date: 29-Jan-2023
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Yan HYao LWang YZhong XZhao J(2023)A Stationary Random Process Based Privacy-Utility Tradeoff in Differential Privacy2023 International Conference on High Performance Big Data and Intelligent Systems (HDIS)10.1109/HDIS60872.2023.10499595(178-185)Online publication date: 6-Dec-2023
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Zhu HTang XYang LFu CPeng S(2023)Key-value data collection and statistical analysis with local differential privacyInformation Sciences: an International Journal10.1016/j.ins.2023.119058640:COnline publication date: 11-Jul-2023
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