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<p>Modern data science requires large amounts of data and computational power to perform effectively. However, there is a lot of sensitive data that should not be shared with others. To still do data science, methods like Homomorphic Encryption exist that enable calculations on encrypted data. One well-known data science algorithm is K-Means, which clusters data points into a previously fixed number of clusters. We provided a homomorphic implementation by demonstrating how comparisons and assignments of clusters can be calculated. Furthermore, the algorithm is evaluated in an offline approach with bootstrapping and an online approach with client re-encryption. Both achieve an accuracy of 100%, but take significantly longer, ranging from 50,000 to 16 million times compared to the non-encrypted case.</p>

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Performance analysis of the homomorphic implementation of K-Means using CKKS

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Thomas Prantl,
  • Patrick Amann,
  • Lukas Horn,
  • Simon Engel,
  • André Bauer,
  • Rafael Bonilla,
  • Anwar Benhnini,
  • Samuel Kounev

摘要

Modern data science requires large amounts of data and computational power to perform effectively. However, there is a lot of sensitive data that should not be shared with others. To still do data science, methods like Homomorphic Encryption exist that enable calculations on encrypted data. One well-known data science algorithm is K-Means, which clusters data points into a previously fixed number of clusters. We provided a homomorphic implementation by demonstrating how comparisons and assignments of clusters can be calculated. Furthermore, the algorithm is evaluated in an offline approach with bootstrapping and an online approach with client re-encryption. Both achieve an accuracy of 100%, but take significantly longer, ranging from 50,000 to 16 million times compared to the non-encrypted case.