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dc.contributor.author
Pournaras, Evangelos
dc.contributor.author
Nikolić, Jovan
dc.contributor.author
Velásquez, Pablo
dc.contributor.author
Trovati, Marcello
dc.contributor.author
Bessis, Nik
dc.contributor.author
Helbing, Dirk
dc.date.accessioned
2019-02-27T12:27:20Z
dc.date.available
2017-06-12T03:00:39Z
dc.date.available
2019-02-27T12:27:20Z
dc.date.issued
2016-04
dc.identifier.issn
2193-1127
dc.identifier.other
10.1140/epjds/s13688-016-0074-4
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/114407
dc.identifier.doi
10.3929/ethz-b-000114407
dc.description.abstract
Participation in social sensing applications is challenged by privacy threats. Large-scale access to citizens’ data allow surveillance and discriminatory actions that may result in segregation phenomena in society. On the contrary are the benefits of accurate computing analytics required for more informed decision-making, more effective policies and regulation of techno-socio-economic systems supported by ‘Internet-of Things’ technologies. In contrast to earlier work that either focuses on privacy protection or Big Data analytics, this paper proposes a self-regulatory information sharing system that bridges this gap. This is achieved by modeling information sharing as a supply-demand system run by computational markets. On the supply side lie the citizens that make incentivized but self-determined decisions about the level of information they share. On the demand side stand data aggregators that provide rewards to citizens to receive the required data for accurate analytics. The system is empirically evaluated with two real-world datasets from two application domains: (i) Smart Grids and (ii) mobile phone sensing. Experimental results quantify trade-offs between privacy-preservation, accuracy of analytics and costs from the provided rewards under different experimental settings. Findings show a higher privacy-preservation that depends on the number of participating citizens and the type of data summarized. Moreover, analytics with summarization data tolerate high local errors without a significant influence on the global accuracy. In other words, local errors cancel out. Rewards can be optimized to be fair so that citizens with more significant sharing of information receive higher rewards. All these findings motivate a new paradigm of truly decentralized and ethical data analytics.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
SpringerOpen
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Privacy
en_US
dc.subject
Summarization
en_US
dc.subject
Analytics
en_US
dc.subject
Aggregation
en_US
dc.subject
Self-regulation
en_US
dc.subject
Social sensing
en_US
dc.subject
Supply-demand
en_US
dc.subject
Reward
en_US
dc.subject
Incentive
en_US
dc.title
Self-regulatory information sharing inparticipatory social sensing
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2016-04-01
ethz.journal.title
EPJ Data Science
ethz.journal.volume
5
en_US
ethz.pages.start
14
en_US
ethz.size
24 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Heidelberg
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02045 - Dep. Geistes-, Sozial- u. Staatswiss. / Dep. of Humanities, Social and Pol.Sc.::03784 - Helbing, Dirk / Helbing, Dirk
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02045 - Dep. Geistes-, Sozial- u. Staatswiss. / Dep. of Humanities, Social and Pol.Sc.::03784 - Helbing, Dirk / Helbing, Dirk
ethz.date.deposited
2017-06-12T03:02:24Z
ethz.source
ECIT
ethz.identifier.importid
imp5936543e069e642230
ethz.ecitpid
pub:176191
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-18T12:24:46Z
ethz.rosetta.lastUpdated
2019-02-27T12:27:40Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
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