Bloom Filter Bootstrap: Privacy-Preserving Estimation of the Size of an Intersection
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- Kikuchi Hiroaki
- Department of Frontier Media Science, School of Interdisciplinary Mathematical Sciences, Meiji University
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- Sakuma Jun
- Graduate School of Systems and Information Engineering, Computer Science Department, University of Tsukuba
Bibliographic Information
- Other Title
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- Neutralized Empirical Risk Minimization with Generalization Neutrality Bound
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Abstract
This paper proposes a new privacy-preserving scheme for estimating the size of the intersection of two given secret subsets. Given the inner product of two Bloom filters (BFs) of the given sets, the proposed scheme applies Bayesian estimation under an assumption of beta distribution for an a priori probability of the size to be estimated. The BF retains the communication complexity and the Bayesian estimation improves the estimation accuracy. A possible application of the proposed protocol is an epidemiological datasets regarding two attributes, Helicobacter pylori infection and stomach cancer. Assuming information related to Helicobacter Pylori infection and stomach cancer are separately collected, the protocol demonstrates that a χ2-test can be performed without disclosing the contents of the two confidential databases.
Journal
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- Journal of Information Processing
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Journal of Information Processing 22 (2), 388-400, 2014
Information Processing Society of Japan
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Details 詳細情報について
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- CRID
- 1390282680271510400
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- NII Article ID
- 130003394485
- 110009752408
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- NII Book ID
- AA00700121
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- ISSN
- 18827764
- 18826652
- 16113349
- 03029743
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- Text Lang
- en
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- Data Source
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- JaLC
- IRDB
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed