<div class="csl-bib-body">
<div class="csl-entry">Dobe, O., Schupp, S. A., Bartocci, E., Bonakdarpour, B., Legay, A., Pajic, M., & Wang, Y. (2023). Lightweight Verification of Hyperproperties. In É. André & J. Sun (Eds.), <i>Automated Technology for Verification and Analysis : 21st International Symposium, ATVA 2023, Singapore, October 24–27, 2023, Proceedings, Part II</i> (pp. 3–25). Springer. https://doi.org/10.1007/978-3-031-45332-8_1</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/191169
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dc.description.abstract
Hyperproperties have been widely used to express system properties like noninterference, observational determinism, conformance, robustness, etc. However, the model checking problem for hyperproperties is challenging due to its inherent complexity of verifying properties across sets of traces and suffers from scalability issues. Previously, statistical approaches have proven effective in tackling the scalability of model checking for temporal logic. In this work, we have attempted to combine these two concepts to propose a tractable solution to model checking of hyperproperties expressed as HyperLTL on models involving nondeterminism. We have implemented our approach in PLASMA and experimented with a range of case studies to showcase its effectiveness.
en
dc.description.sponsorship
FWF - Österr. Wissenschaftsfonds
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dc.language.iso
en
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dc.relation.ispartofseries
Lecture Notes in Computer Science
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dc.subject
Hyperproperties
en
dc.subject
Statistical Model Checking
en
dc.subject
Nondeterminism
en
dc.title
Lightweight Verification of Hyperproperties
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
Michigan State University, United States of America (the)
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dc.contributor.affiliation
Michigan State University, United States of America (the)
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dc.contributor.affiliation
UCLouvain, Belgium
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dc.contributor.affiliation
Duke University, United States of America (the)
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dc.contributor.affiliation
University of Florida, United States of America (the)
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dc.contributor.editoraffiliation
Université Sorbonne Paris Nord, France
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dc.contributor.editoraffiliation
Singapore Management University, Singapore
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dc.relation.isbn
978-3-031-45332-8
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dc.relation.doi
10.1007/978-3-031-45332-8
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dc.description.startpage
3
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dc.description.endpage
25
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dc.relation.grantno
ZK 35-G
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Automated Technology for Verification and Analysis : 21st International Symposium, ATVA 2023, Singapore, October 24–27, 2023, Proceedings, Part II
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tuw.container.volume
14216
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tuw.peerreviewed
true
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tuw.relation.publisher
Springer
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tuw.relation.publisherplace
Cham
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tuw.project.title
High-dimensional statistical learning: New methods to advance economic and sustainability policies
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tuw.researchTopic.id
I2
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tuw.researchTopic.name
Computer Engineering and Software-Intensive Systems
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E191-01 - Forschungsbereich Cyber-Physical Systems
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tuw.publisher.doi
10.1007/978-3-031-45332-8_1
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dc.description.numberOfPages
23
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tuw.author.orcid
0000-0002-0799-1498
-
tuw.author.orcid
0000-0002-2055-7581
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tuw.author.orcid
0000-0002-8004-6601
-
tuw.author.orcid
0000-0003-1800-5419
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tuw.author.orcid
0000-0002-5357-0117
-
tuw.author.orcid
0000-0002-0431-1039
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tuw.event.name
21st International Symposium on Automated Technology for Verification and Analysis
en
tuw.event.startdate
24-10-2023
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tuw.event.enddate
27-10-2023
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Singapore
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tuw.event.country
SG
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tuw.event.presenter
Dobe, Oyendrila
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tuw.event.track
Single Track
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.value
100
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.languageiso639-1
en
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item.fulltext
no Fulltext
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item.grantfulltext
none
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item.openairetype
conference paper
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item.cerifentitytype
Publications
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crisitem.author.dept
Michigan State University
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crisitem.author.dept
E191-01 - Forschungsbereich Cyber-Physical Systems
-
crisitem.author.dept
E191-01 - Forschungsbereich Cyber-Physical Systems