Efficient private multi-party numerical records matching

Shumin HAN, Derong SHEN, Tiezheng NIE, Yue KOU, Ge YU

PDF(123 KB)
PDF(123 KB)
Front. Comput. Sci. ›› 2020, Vol. 14 ›› Issue (5) : 145611. DOI: 10.1007/s11704-019-9063-7
LETTER

Efficient private multi-party numerical records matching

Author information +
History +

Cite this article

Download citation ▾
Shumin HAN, Derong SHEN, Tiezheng NIE, Yue KOU, Ge YU. Efficient private multi-party numerical records matching. Front. Comput. Sci., 2020, 14(5): 145611 https://doi.org/10.1007/s11704-019-9063-7

References

[1]
Vatsalan D, Christen P, Verykios V S. A taxonomy of privacypreserving record linkage techniques. Information Systems Journal, 2013, 38(6): 946–969
CrossRef Google scholar
[2]
Verykios V S, Karakasidis A, Mitrogiannis V. Privacy preserving record linkage approaches. International Journal of DataMining, Modelling and Management, 2009, 1(2): 206–221
CrossRef Google scholar
[3]
Churches T, Christen P. Some methods for blindfolded record linkage. BMC Medical Informatics and Decision Making, 2004, 4(1): 9
CrossRef Google scholar
[4]
O’Keefe C, Yung M, Gu L, Baxter R. Privacy-preserving data linkage protocols. In: Processings of ACM Workshop on Privacy in the Electronic Society. 2004, 94–102
CrossRef Google scholar
[5]
Trepetin S. Privacy-preserving string comparisons in record linkage systems: a review. Information Security Journal: A Global Perspective, 2008, 17(5–6): 253–266
CrossRef Google scholar
[6]
Ravikumar P, Cohen W, Fienberg S. A secure protocol for computing string distance metrics. In: Processings of IEEE Workshop on Privacy and Security Aspects of Data Mining Held at ICDM. 2004, 40–46
[7]
Vatsalan D, Christen P. Privacy-preserving matching of similar patients. Journal of Biomedical Informatics, 2016, 59: 285–298
CrossRef Google scholar
[8]
Xiang G L, Chen X M. Homomorapic encryption scheme in the range of the real. Journal of Computer Engineering Application, 2005, 20: 12–14

RIGHTS & PERMISSIONS

2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
AI Summary AI Mindmap
PDF(123 KB)

Accesses

Citations

Detail

Sections
Recommended

/