Application and analysis of multidimensional negative surveys in participatory sensing applications

Michael M. Groat, Benjamin Edwards, James Horey, Wenbo He, Stephanie Forrest

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Participatory sensing applications rely on individuals to share personal data to produce aggregated models and knowledge. In this setting, privacy concerns can discourage widespread adoption of new applications. We present a privacy-preserving participatory sensing scheme based on negative surveys for both continuous and multivariate categorical data. Without relying on encryption, our algorithms enhance the privacy of sensed data in an energy and computation efficient manner. Simulations and implementation on Android smart phones illustrate how multidimensional data can be aggregated in a useful and privacy-enhancing manner.

Original languageEnglish (US)
Pages (from-to)372-391
Number of pages20
JournalPervasive and Mobile Computing
Volume9
Issue number3
DOIs
StatePublished - Jun 1 2013
Externally publishedYes

Fingerprint

Privacy
Sensing
Data privacy
Cryptography
Multidimensional Data
Nominal or categorical data
Privacy Preserving
Multivariate Data
Encryption
Energy
Simulation
Model
Knowledge

Keywords

  • Multidimensional data
  • Negative surveys
  • Participatory sensing applications
  • Privacy

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Applied Mathematics

Cite this

Application and analysis of multidimensional negative surveys in participatory sensing applications. / Groat, Michael M.; Edwards, Benjamin; Horey, James; He, Wenbo; Forrest, Stephanie.

In: Pervasive and Mobile Computing, Vol. 9, No. 3, 01.06.2013, p. 372-391.

Research output: Contribution to journalArticle

Groat, Michael M. ; Edwards, Benjamin ; Horey, James ; He, Wenbo ; Forrest, Stephanie. / Application and analysis of multidimensional negative surveys in participatory sensing applications. In: Pervasive and Mobile Computing. 2013 ; Vol. 9, No. 3. pp. 372-391.
@article{fc70606cfee54d3081ad35080a118d3f,
title = "Application and analysis of multidimensional negative surveys in participatory sensing applications",
abstract = "Participatory sensing applications rely on individuals to share personal data to produce aggregated models and knowledge. In this setting, privacy concerns can discourage widespread adoption of new applications. We present a privacy-preserving participatory sensing scheme based on negative surveys for both continuous and multivariate categorical data. Without relying on encryption, our algorithms enhance the privacy of sensed data in an energy and computation efficient manner. Simulations and implementation on Android smart phones illustrate how multidimensional data can be aggregated in a useful and privacy-enhancing manner.",
keywords = "Multidimensional data, Negative surveys, Participatory sensing applications, Privacy",
author = "Groat, {Michael M.} and Benjamin Edwards and James Horey and Wenbo He and Stephanie Forrest",
year = "2013",
month = "6",
day = "1",
doi = "10.1016/j.pmcj.2012.12.004",
language = "English (US)",
volume = "9",
pages = "372--391",
journal = "Pervasive and Mobile Computing",
issn = "1574-1192",
publisher = "Elsevier",
number = "3",

}

TY - JOUR

T1 - Application and analysis of multidimensional negative surveys in participatory sensing applications

AU - Groat, Michael M.

AU - Edwards, Benjamin

AU - Horey, James

AU - He, Wenbo

AU - Forrest, Stephanie

PY - 2013/6/1

Y1 - 2013/6/1

N2 - Participatory sensing applications rely on individuals to share personal data to produce aggregated models and knowledge. In this setting, privacy concerns can discourage widespread adoption of new applications. We present a privacy-preserving participatory sensing scheme based on negative surveys for both continuous and multivariate categorical data. Without relying on encryption, our algorithms enhance the privacy of sensed data in an energy and computation efficient manner. Simulations and implementation on Android smart phones illustrate how multidimensional data can be aggregated in a useful and privacy-enhancing manner.

AB - Participatory sensing applications rely on individuals to share personal data to produce aggregated models and knowledge. In this setting, privacy concerns can discourage widespread adoption of new applications. We present a privacy-preserving participatory sensing scheme based on negative surveys for both continuous and multivariate categorical data. Without relying on encryption, our algorithms enhance the privacy of sensed data in an energy and computation efficient manner. Simulations and implementation on Android smart phones illustrate how multidimensional data can be aggregated in a useful and privacy-enhancing manner.

KW - Multidimensional data

KW - Negative surveys

KW - Participatory sensing applications

KW - Privacy

UR - http://www.scopus.com/inward/record.url?scp=84876694067&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876694067&partnerID=8YFLogxK

U2 - 10.1016/j.pmcj.2012.12.004

DO - 10.1016/j.pmcj.2012.12.004

M3 - Article

AN - SCOPUS:84876694067

VL - 9

SP - 372

EP - 391

JO - Pervasive and Mobile Computing

JF - Pervasive and Mobile Computing

SN - 1574-1192

IS - 3

ER -