research-article

Self-identifying sensor data

Authors:

Christian Skalka,

Jeffrey A. VaughanAuthors Info & Claims

IPSN '10: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks

Pages 82 - 93

https://doi.org/10.1145/1791212.1791223

Published: 12 April 2010 Publication History

Abstract

Public-use sensor datasets are a useful scientific resource with the unfortunate feature that their provenance is easily disconnected from their content. To address this we introduce a technique to directly associate provenance information with sensor datasets. Our technique is similar to traditional watermarking but is intended for application to unstructured datasets. Our approach is potentially imperceptible given sufficient margins of error in datasets, and is robust to a number of benign but likely transformations including truncation, rounding, bit-flipping, sampling, and reordering. We provide algorithms for both one-bit and blind mark checking. Our algorithms are probabilistic in nature and are characterized by a combinatorial analysis.

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Cited By

Islam M(2020)Memory Incentive Provenance (MIP) to Secure the Wireless Sensor Data StreamIntelligent Computing10.1007/978-3-030-52243-8_20(255-277)Online publication date: 4-Jul-2020
https://doi.org/10.1007/978-3-030-52243-8_20
Sørlie JAltmann J(2019)Sensing as a Service Revisited: A Property Rights Enforcement and Pricing Model for IIoT Data MarketplacesEconomics of Grids, Clouds, Systems, and Services10.1007/978-3-030-36027-6_11(127-139)Online publication date: 24-Nov-2019
https://doi.org/10.1007/978-3-030-36027-6_11
Sowmyadevi DKarthikeyan K(2017)Merkle-Hellman knapsack-side channel monitoring based secure scheme for detecting provenance forgery and selfish nodes in wireless sensor networks2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT)10.1109/ICECCT.2017.8117950(1-8)Online publication date: Feb-2017
https://doi.org/10.1109/ICECCT.2017.8117950
Show More Cited By

Index Terms

Self-identifying sensor data
1. Information systems
  1. Information retrieval
  2. Information storage systems
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Data types and structures

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Information & Contributors

Information

Published In

cover image ACM Conferences

IPSN '10: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks

April 2010

460 pages

ISBN:9781605589886

DOI:10.1145/1791212

Conference Chairs:
Tarek Abdelzaher
UIUC
,
Thiemo Voigt
SICS
,
Adam Wolisz
TU Berlin

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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IEEE-SPS: Signal Processing Society
SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 April 2010

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IPSN '10

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IEEE-SPS
SIGBED

IPSN '10: The 9th International Conference on Information Processing in Sensor Networks

April 12 - 16, 2010

Stockholm, Sweden

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Overall Acceptance Rate 143 of 593 submissions, 24%

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Cited By

Islam M(2020)Memory Incentive Provenance (MIP) to Secure the Wireless Sensor Data StreamIntelligent Computing10.1007/978-3-030-52243-8_20(255-277)Online publication date: 4-Jul-2020
https://doi.org/10.1007/978-3-030-52243-8_20
Sørlie JAltmann J(2019)Sensing as a Service Revisited: A Property Rights Enforcement and Pricing Model for IIoT Data MarketplacesEconomics of Grids, Clouds, Systems, and Services10.1007/978-3-030-36027-6_11(127-139)Online publication date: 24-Nov-2019
https://doi.org/10.1007/978-3-030-36027-6_11
Sowmyadevi DKarthikeyan K(2017)Merkle-Hellman knapsack-side channel monitoring based secure scheme for detecting provenance forgery and selfish nodes in wireless sensor networks2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT)10.1109/ICECCT.2017.8117950(1-8)Online publication date: Feb-2017
https://doi.org/10.1109/ICECCT.2017.8117950
Pavan Kumar S Nandini Prasad K S (2016)Novel approach for detecting malicious activity in WSN using securing scheme2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)10.1109/ICATCCT.2016.7911968(76-80)Online publication date: 2016
https://doi.org/10.1109/ICATCCT.2016.7911968
Soltani Panah AVan Schyndel RSellis TBertino E(2016)On the Properties of Non-Media Digital Watermarking: A Review of State of the Art TechniquesIEEE Access10.1109/ACCESS.2016.25708124(2670-2704)Online publication date: 2016
https://doi.org/10.1109/ACCESS.2016.2570812
Chong SSkalka CVaughan J(2015)Self-Identifying Data for Fair UseJournal of Data and Information Quality10.1145/26874225:3(1-30)Online publication date: 2-Mar-2015
https://dl.acm.org/doi/10.1145/2687422
Sultana SGhinita GBertino EShehab M(2015)A Lightweight Secure Scheme for Detecting Provenance Forgery and Packet DropAttacks in Wireless Sensor NetworksIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2013.4412:3(256-269)Online publication date: May-2015
https://doi.org/10.1109/TDSC.2013.44
Ramya KChithra Devi RRevathi MAnnapandi P(2014)Sensor Data Hiding Based on Image Watermarking Using Interpolation Technique over Inter-Packet DelaysApplied Mechanics and Materials10.4028/www.scientific.net/AMM.573.543573(543-548)Online publication date: Jun-2014
https://doi.org/10.4028/www.scientific.net/AMM.573.543
Sultana SShehab MBertino E(2013)Secure Provenance Transmission for Streaming DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2012.3125:8(1890-1903)Online publication date: 1-Aug-2013
https://dl.acm.org/doi/10.1109/TKDE.2012.31
Shebaro BSultana SReddy Gopavaram SBertino EYu TDanezis GGligor V(2012)Demonstrating a lightweight data provenance for sensor networksProceedings of the 2012 ACM conference on Computer and communications security10.1145/2382196.2382312(1022-1024)Online publication date: 16-Oct-2012
https://dl.acm.org/doi/10.1145/2382196.2382312
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