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

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Book cover Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1222))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, UK

    Nawal Almutairi, Frans Coenen & Keith Dures

  2. Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Nawal Almutairi

Authors
  1. Nawal Almutairi
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  2. Frans Coenen
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  3. Keith Dures
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Corresponding author

Correspondence to Frans Coenen .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  2. Federal University of Pernambuco, Recife, Brazil

    Ana Salgado

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. Delft University of Technology, Delft, The Netherlands

    Jan Dietz

  5. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  6. Polytechnic Institute of Setúbal/INSTIC, Setúbal, Portugal

    Joaquim Filipe

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Almutairi, N., Coenen, F., Dures, K. (2020). Secure Outsourced kNN Data Classification over Encrypted Data Using Secure Chain Distance Matrices. In: Fred, A., Salgado, A., Aveiro, D., Dietz, J., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2018. Communications in Computer and Information Science, vol 1222. Springer, Cham. https://doi.org/10.1007/978-3-030-49559-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-49559-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49558-9

  • Online ISBN: 978-3-030-49559-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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