The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Secure Multiparty Computation for Privacy-Preserving Data Mining
Open accessAbstract
In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacy-preserving data mining. In addition to reviewing definitions and constructions for secure multiparty computation, we discuss the issue of efficiency and demonstrate the difficulties involved in constructing highly efficient protocols. We also present common errors that are prevalent in the literature when secure multiparty computation techniques are applied to privacy-preserving data mining. Finally, we discuss the relationship between secure multiparty computation and privacy-preserving data mining, and show which problems it solves and which problems it does not.
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Cites
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Self-citationFoundations of Cryptography: Volume 2, Basic Applications
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Efficient Private Matching and Set Intersection
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A survey of multiple classifier systems as hybrid systems
Woźniak, M., Graña, M., & Corchado, E. (2014) · Information Fusion · doi:10.1016/j.inffus.2013.04.006 · Cited by 912
A taxonomy of privacy-preserving record linkage techniques
Vatsalan, D., Christen, P., & Verykios, V. S. (2013) · Information Systems · doi:10.1016/j.is.2012.11.005 · Cited by 295
Deriving genomic diagnoses without revealing patient genomes
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A Survey of Self-Sovereign Identity Ecosystem
Soltani, R., Nguyen, U. T., & An, A. (2021) · Security and Communication Networks · doi:10.1155/2021/8873429 · Cited by 131
“Privacy-preserving data mining is another common application of SMC. author in [115] introduces an approach in which two parties, each with a separate data-base, are able to jointly perform data mining operations on the union of their data sets without disclosing their database to one another or…”
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Metadata
- Authors
- Lindell, Y., & Pinkas, B.
- Venue
- Journal of Privacy and Confidentiality
- Published
- 2009-04-01
- Volume
- 1
- Issue
- 1
- ISSN
- 2575-8527 (ISSN-L)
- OpenAlex
- W2116025923
- Cited by
- 554
- References
- 41
- FWCI
- 24.58
- Type
- journal article
- Language
- en
- Open access
- diamond
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
