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A differentially private kernel two-sample test

Abstract:

Kernel two-sample testing is a useful statistical tool in determining whether data samples arise from different distributions without imposing any parametric assumptions on those distributions. However, raw data samples can expose sensitive information about individuals who participate in scientific studies, which makes the current tests vulnerable to privacy breaches. Hence, we design a new framework for kernel twosample testing conforming to differential privacy constraints, in order to gua...

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Publication status:
Published
Peer review status:
Reviewed (other)

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Publisher copy:
10.1007/978-3-030-46150-8_41

Authors


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Institution:
University of Oxford
Department:
Statistics
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Mansfield College
Role:
Author
ORCID:
0000-0001-5547-9213
Publisher:
Springer Nature
Host title:
ECML PKDD 2019: Machine Learning and Knowledge Discovery in Databases
Journal:
Lecture Notes in Computer Science More from this journal
Volume:
11906
Pages:
697-724
Publication date:
2020-04-30
Acceptance date:
2019-06-09
Event location:
Würzburg, Germany
Event start date:
2019-09-16
Event end date:
2019-09-19
DOI:
ISSN:
0302-9743
EISBN:
9783030461508
ISBN:
9783030461492
Language:
English
Keywords:
Pubs id:
pubs:1032739
UUID:
uuid:05901228-958c-4d25-8c62-1a0dac73102d
Local pid:
pubs:1032739
Source identifiers:
1032739
Deposit date:
2019-07-16

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