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Haze: privacy-preserving real-time traffic statistics

Authors:

Joshua W. S. Brown,

Olga Ohrimenko,

Roberto TamassiaAuthors Info & Claims

SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 540 - 543

https://doi.org/10.1145/2525314.2525323

Published: 05 November 2013 Publication History

Abstract

We consider mobile applications that let users learn traffic conditions based on reports from other users. However, the providers of these mobile services have access to such sensitive information as timestamped locations and movements of its users. In this paper, we introduce the model and general approach of Haze, a system for traffic-update applications that supports the creation of traffic statistics from user reports while protecting the privacy of the users. We also present preliminary experiments that indicate potential for a practical deployment of Haze.

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J. W. S. Brown, O. Ohrimenko, and R. Tamassia. Haze: Privacy-preserving real-time traffic statistics. CoRR, abs/1309.3515, 2013.

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Cited By

Mohammady MXie SHong YZhang MWang LPourzandi MDebbabi MLigatti JOu XKatz JVigna G(2020)R2DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal DistributionsProceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security10.1145/3372297.3417259(677-696)Online publication date: 30-Oct-2020
https://dl.acm.org/doi/10.1145/3372297.3417259
Zhang GZhang AZhao P(2020)LocMIA: Membership Inference Attacks against Aggregated Location DataIEEE Internet of Things Journal10.1109/JIOT.2020.3001172(1-1)Online publication date: 2020
https://doi.org/10.1109/JIOT.2020.3001172
Wang QJi TGuo YYu LChen XLi P(2020)TrafficChain: A Blockchain-Based Secure and Privacy-Preserving Traffic MapIEEE Access10.1109/ACCESS.2020.29802988(60598-60612)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2980298
Show More Cited By

Index Terms

Haze: privacy-preserving real-time traffic statistics
1. Security and privacy
  1. Cryptography
  2. Systems security
    1. Operating systems security

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Information & Contributors

Information

Published In

cover image ACM Conferences

SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2013

598 pages

ISBN:9781450325219

DOI:10.1145/2525314

General Chairs:
Craig Knoblock
University of Southern California
,
Markus Schneider
University of Florida
,
Program Chairs:
Peer Kröger
Ludwig-Maximilians-Universität München, Germany
,
John Krumm
Microsoft Research
,
Peter Widmayer
ETH Zürich, Switzerland

Copyright © 2013 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

SIGSPATIAL: ACM Special Interest Group on Spatial Information

In-Cooperation

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2013

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SIGSPATIAL'13

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SIGSPATIAL

SIGSPATIAL'13: 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2013

Florida, Orlando

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Mohammady MXie SHong YZhang MWang LPourzandi MDebbabi MLigatti JOu XKatz JVigna G(2020)R2DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal DistributionsProceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security10.1145/3372297.3417259(677-696)Online publication date: 30-Oct-2020
https://dl.acm.org/doi/10.1145/3372297.3417259
Zhang GZhang AZhao P(2020)LocMIA: Membership Inference Attacks against Aggregated Location DataIEEE Internet of Things Journal10.1109/JIOT.2020.3001172(1-1)Online publication date: 2020
https://doi.org/10.1109/JIOT.2020.3001172
Wang QJi TGuo YYu LChen XLi P(2020)TrafficChain: A Blockchain-Based Secure and Privacy-Preserving Traffic MapIEEE Access10.1109/ACCESS.2020.29802988(60598-60612)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2980298
Song YWang ZZhang JMa ZMa J(2020)A Decentralized Weighted Vote Traffic Congestion Detection Framework for ITSSecurity and Privacy in Digital Economy10.1007/978-981-15-9129-7_18(249-262)Online publication date: 22-Oct-2020
https://doi.org/10.1007/978-981-15-9129-7_18
Rauti SLaato S(2020)Location-Based Games as Interfaces for Collecting User DataTrends and Innovations in Information Systems and Technologies10.1007/978-3-030-45691-7_59(631-642)Online publication date: 8-Jun-2020
https://doi.org/10.1007/978-3-030-45691-7_59
Popa IThat DZeitouni KBorcea C(2019)Mobile participatory sensing with strong privacy guarantees using secure probesGeoInformatica10.1007/s10707-019-00389-4Online publication date: 20-Dec-2019
https://doi.org/10.1007/s10707-019-00389-4
Bennati SDusparic IShinde RJonker C(2018)Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered MeasuresSensors10.3390/s1811370718:11(3707)Online publication date: 31-Oct-2018
https://doi.org/10.3390/s18113707
Alaggan MCunche MGambs S(2018)Privacy-preserving Wi-Fi AnalyticsProceedings on Privacy Enhancing Technologies10.1515/popets-2018-00102018:2(4-26)Online publication date: 1-Apr-2018
https://doi.org/10.1515/popets-2018-0010
Wang GWang BWang TNika AZheng HZhao B(2018)Ghost RidersIEEE/ACM Transactions on Networking10.1109/TNET.2018.281807326:3(1123-1136)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1109/TNET.2018.2818073
Gong YZhang CFang YSun J(2018)Protecting Location Privacy for Task Allocation in Ad Hoc Mobile Cloud ComputingIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2015.24900216:1(110-121)Online publication date: Jan-2018
https://doi.org/10.1109/TETC.2015.2490021
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