Quantifying leakage in the presence of unreliable sources of information
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20752330
- Type
- D - Journal article
- DOI
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10.1016/j.jcss.2017.03.013
- Title of journal
- Journal of Computer and System Sciences
- Article number
- -
- First page
- 27
- Volume
- 88
- Issue
- -
- ISSN
- 0022-0000
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work’s innovation consists of new metric for information flow which takes into account the attacker’s beliefs. Such beliefs cannot be discounted in a realistic analysis, yet they can be inaccurate, misleading, outdated, and can be used to confuse (rather than support) the attacker. This work has inspired a long-standing research path at the Ecole Polytechnique Paris, which eventually contributed to my co-author Catuscia Palamidessi to win a ERC Advanced Grant (“Privacy and Utility Allied”, PE6, ERC-2018-ADG) in which quantifying privacy in the presence of unreliable sources of information is a strand.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -