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Bisimilarity distances for approximate differential privacy

Abstract:

Differential privacy is a widely studied notion of privacy for various models of computation. Technically, it is based on measuring differences between probability distributions. We study ∈; δ-differential privacy in the setting of labelled Markov chains. While the exact differences relevant to ∈; δ-differential privacy are not computable in this framework, we propose a computable bisimilarity distance that yields a sound technique for measuring δ, the parameter that quantifies deviation from...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-01090-4_12

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
Worcester College
Role:
Author
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Name:
Royal Society
Grant:
IE161701
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/L016400/1
Publisher:
Springer
Host title:
16th International Symposium on Automated Technology for Verification and Analysis (ATVA 2018)
Journal:
16th International Symposium on Automated Technology for Verification and Analysis (ATVA 2018) More from this journal
Publication date:
2018-09-30
Acceptance date:
2018-06-27
DOI:
Keywords:
Pubs id:
pubs:892035
UUID:
uuid:8e8968ec-9c86-4bc5-ad35-538e33f77af0
Local pid:
pubs:892035
Source identifiers:
892035
Deposit date:
2018-08-01

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