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  1. Journal of Data and Information Quality (JDIQ)
  2. Journal of Data and Information Quality (JDIQ) : Volume 5
  3. Issue 3(Special Issue on Provenance, Data and Information Quality), February 2015
  4. Self-Identifying Data for Fair Use
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Journal of Data and Information Quality (JDIQ) : Volume 8
Journal of Data and Information Quality (JDIQ) : Volume 7
Journal of Data and Information Quality (JDIQ) : Volume 6
Journal of Data and Information Quality (JDIQ) : Volume 5
Issue 4, February 2015
Issue 3(Special Issue on Provenance, Data and Information Quality), February 2015
Editorial
Provenance Quality Assessment Methodology and Framework
A Hybrid Approach to Answering Why-Not Questions on Relational Query Results
Self-Identifying Data for Fair Use
QUAL: A Provenance-Aware Quality Model
Issue 1-2, August 2014
Journal of Data and Information Quality (JDIQ) : Volume 4
Journal of Data and Information Quality (JDIQ) : Volume 3
Journal of Data and Information Quality (JDIQ) : Volume 2
Journal of Data and Information Quality (JDIQ) : Volume 1

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Self-Identifying Data for Fair Use

Content Provider ACM Digital Library
Author Chong, Stephen Skalka, Christian Vaughan, Jeffrey A
Copyright Year 2015
Abstract Public-use earth science datasets are a useful resource with the unfortunate feature that their provenance is easily disconnected from their content. “Fair-use policies” typically associated with these datasets require appropriate attribution of providers by users, but sound and complete attribution is difficult if provenance information is lost. 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 time-series 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 and show how our system can be adapted to various data representation types. Our algorithms are probabilistic in nature and are characterized by both combinatorial and empirical analyses. Mark embedding can be applied at any point in the data life cycle, allowing adaptation of our scheme to social or scientific concerns.
Starting Page 1
Ending Page 30
Page Count 30
File Format PDF
ISSN 19361955
e-ISSN 19361963
DOI 10.1145/2687422
Journal Journal of Data and Information Quality (JDIQ)
Volume Number 5
Issue Number 3
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-03-02
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Provenance Self-identifying data
Content Type Text
Resource Type Article
Subject Information Systems and Management Information Systems
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