Conference item
Asymmetric distances for approximate differential privacy
- Abstract:
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Differential privacy is a widely studied notion of privacy for various models of computation, based on measuring differences between probability distributions. We consider (epsilon,delta)-differential privacy in the setting of labelled Markov chains. For a given epsilon, the parameter delta can be captured by a variant of the total variation distance, which we call lv_{alpha} (where alpha = e^{epsilon}). First we study lv_{alpha} directly, showing that it cannot be computed exactly. However, ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
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(Preview, Version of record, pdf, 691.7KB, Terms of use)
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- Publisher copy:
- 10.4230/LIPIcs.CONCUR.2019.10
Authors
Bibliographic Details
- Publisher:
- Schloss Dagstuhl - Leibniz-Zentrum für Informatik
- Host title:
- LIPIcs
- Journal:
- LIPIcs. More from this journal
- Volume:
- 140
- Pages:
- 10:1--10:17
- Publication date:
- 2019-08-26
- Acceptance date:
- 2019-06-14
- DOI:
- EISSN:
-
1868-8969
Item Description
- Keywords:
- Pubs id:
-
pubs:1035671
- UUID:
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uuid:c2bb8a8a-a4e8-41af-8f7b-c0f6fa981999
- Local pid:
-
pubs:1035671
- Source identifiers:
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1035671
- Deposit date:
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2019-07-27
Terms of use
- Copyright holder:
- Chistikov et al
- Copyright date:
- 2019
- Notes:
-
© Dmitry Chistikov, Andrzej S. Murawski, and David Purser;
licensed under Creative Commons License CC-BY
- Licence:
- CC Attribution (CC BY)
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