Speeding up Privacy Preserving Record Linkage for Metric Space Similarity Measures

2016 ◽  
Vol 16 (3) ◽  
pp. 227-236 ◽  
Author(s):  
Ziad Sehili ◽  
Erhard Rahm
Keyword(s):  
Metric Space ◽  
Record Linkage ◽  
Similarity Measures ◽  
Privacy Preserving

Towards Privacy-Preserving Record Linkage with Record-Wise Linkage Policy

2017 ◽  
pp. 233-248
Author(s):  
Takahito Kaiho ◽  
Wen-jie Lu ◽  
Toshiyuki Amagasa ◽  
Jun Sakuma
Keyword(s):  
Record Linkage ◽  
Privacy Preserving

scholarly journals PRIVACY PRESERVING CLUSTERING BASED ON LINEAR APPROXIMATION OF FUNCTION

2013 ◽  
Vol 12 (5) ◽  
pp. 3443-3451
Author(s):  
Rajesh Pasupuleti ◽  
Narsimha Gugulothu
Keyword(s):  
Linear Approximation ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Privacy Preserving ◽  
Distance Measures ◽  
Clustering Methods ◽  
Sensitive Data ◽  
Processing Information ◽  
Data Objects ◽  
Approximation Of Function

Clustering analysis initiatives  a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of the  requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected by  user.  In this paper, we proposed new clustering method based on linear approximation of function by getting over all idea of behavior knowledge of clustering function, then pick the initial seeds of clusters as the points on linear approximation line and perform clustering operations, unlike grouping data objects into clusters by using distance measures, similarity measures and statistical distributions in traditional clustering methods. We have shown experimental results as clusters based on linear approximation yields good  results in practice with an example of  business data are provided.  It also  explains privacy preserving clusters of sensitive data objects.

Explanation and answers to critiques on: Blockchain-based Privacy-Preserving Record Linkage

2021 ◽  
pp. 101935
Author(s):  
Thiago Nóbrega ◽  
Carlos Eduardo S. Pires ◽  
Dimas Cassimiro Nascimento
Keyword(s):  
Record Linkage ◽  
Privacy Preserving

Accurate privacy-preserving record linkage for databases with missing values

2021 ◽  
pp. 101959
Author(s):  
Sirintra Vaiwsri ◽  
Thilina Ranbaduge ◽  
Peter Christen ◽  
Rainer Schnell
Keyword(s):  
Record Linkage ◽  
Missing Values ◽  
Privacy Preserving

Privacy-Preserving Record Linkage

2019 ◽  
pp. 1300-1307
Author(s):  
Dinusha Vatsalan ◽  
Dimitrios Karapiperis ◽  
Vassilios S. Verykios
Keyword(s):  
Record Linkage ◽  
Privacy Preserving
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