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Evaluation of Data Warehouse Design Methodologies in the Context of Big Data Read chapter Read first chapter

2017 | OriginalPaper | Chapter

K-Means Clustering Using Homomorphic Encryption and an Updatable Distance Matrix: Secure Third Party Data Clustering with Limited Data Owner Interaction

Authors : Nawal Almutairi, Frans Coenen, Keith Dures

Published in: Big Data Analytics and Knowledge Discovery

Publisher: Springer International Publishing

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Abstract

Third party data analysis raises data privacy preservation concerns, therefore raising questions as to whether such outsourcing is viable. Cryptography allows a level of data confidentiality. Although some cryptography algorithms, such as Homomorphic Encryption (HE), allow a limited amount of data manipulation, the disadvantage is that encryption precludes any form of sophisticated analysis. For this to be achieved the encrypted data needs to coupled with additional information to facilitate third party analysis. This paper proposes a mechanism for secure k-means clustering that uses HE and the concept of an Updatable Distance Matrix (UDM). The mechanism is fully described and analysed. The reported evaluation shows that the proposed mechanism produces identical clustering results as when “standard” k-means is applied, but in a secure manner. The proposed mechanism thus allows the application of clustering algorithms to encrypted data while preserving both correctness and data privacy.

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previous chapter Diverse Selection of Feature Subsets for Ensemble Regression
next chapter Reweighting Forest for Extreme Multi-label Classification
Footnotes
1
A “trapdoor” in this context is a function that can be simply computed on one direction but is difficult to compute in the reverse direction without additional knowledge; the concept is widely used in cryptography.
 
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Metadata
Title
K-Means Clustering Using Homomorphic Encryption and an Updatable Distance Matrix: Secure Third Party Data Clustering with Limited Data Owner Interaction
Authors
Nawal Almutairi
Frans Coenen
Keith Dures
Copyright Year
2017
Book
Big Data Analytics and Knowledge Discovery

Print ISBN: 978-3-319-64282-6

Electronic ISBN: 978-3-319-64283-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-64283-3_20

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