Vol. 3 No. 2 (2023): Journal of Millimeterwave Communication, Optimization and Modelling
Articles

Blockchain-based Privacy Preserving Linear Regression

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  • Zeynep Mutlu,
  • Yesem Kurt Peker,
  • Ali Aydın Selçuk
Zeynep Mutlu
TOBB University

Published 31.12.2023

Keywords

  • blockchain,
  • homomorphic encryption,
  • statistics,
  • linear regression,
  • ethereum

Copyright (c) 2023 Zeynep Mutlu; Yesem Kurt Peker, Ali Aydın Selçuk (Co-Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

In this study we propose a blockchain-based architecture that uses smart contracts and homomorphic encryption to allow statistical computations on confidential data by third parties. The use of blockchain provides the much-desired security properties of integrity and fault tolerance and homomorphic encryption preserves the privacy of the data. We present the design, implementation, and testing of our system. Our results show that a blockchain-based data sharing mechanism with homomorphic calculations via a smart contract is feasible and provides improvements in protecting the data from unauthorized users. Even though our work focused on linear regression, the architecture can be used for other statistical analysis and machine learning algorithms.

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