AK Das

BK Samanthula

D Liu

DE Robling Denning

H Takabi

H Xu

J Domingo-Ferrer

J Liu

L Liu

LO Gostin

ML Yiu

N Almutairi

P Samarati

S Xu

X Sun

Y Lindell

Y Zhu

Z Liu

The paper introduces the Secure kNN (SkNN) approach to data classification and querying. The approach is founded on the concept of Secure Chain Distance Matrices (SCDMs) whereby the classification and querying is entirely delegated to a third party data miner without sharing either the original dataset or individual queries. Privacy is maintained using two property preserving encryption schemes, a homomorphic encryption scheme and bespoke order preserving encryption scheme. The proposed solution provides advantages of: (i) preserving the data privacy of the parties involved, (ii) preserving the confidentiality of the data owner encryption key, (iii) hiding the query resolution process and (iv) providing for scalability with respect to alternative data mining algorithms and alternative collaborative data mining scenarios. The results indicate that the proposed solution is both efficient and effective whilst at the same time being secure against potential attack

Almutairi, Nawal

Coenen, Frans

Dures, Keith

University of Liverpool Repository

Secure Outsourced kNN Data Classification over Encrypted Data Using Secure Chain Distance Matrices

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http://livrepository.liverpool.ac.uk/3099940/1/ic3kBook_almutairi.pdf

Secure Outsourced kNN Data Classification over Encrypted Data Using Secure Chain Distance Matrices

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