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Efficient Two-Party Privacy Preserving Collaborative k-means Clustering Protocol Supporting both Storage and Computation Outsourcing

  • Zoe L. Jiang
  • Ning Guo
  • Yabin Jin
  • Jiazhuo Lv
  • Yulin Wu
  • Yating Yu
  • Xuan Wang
  • S. M. Yiu
  • Junbin Fang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11337)

Abstract

Privacy preserving collaborative data mining aims to extract useful knowledge from distributed databases owned by multiple parties while keeping the privacy of both data and mining result. Nowadays, more and more companies reply on cloud to store data and handle with data. In this context, privacy preserving collaborative k-means clustering framework was proposed to support both storage and computation outsourcing for two parties. However, the computing cost and communication overhead are too high to practical. In this paper, we propose to encrypt each party’s data once and then store them in cloud. Privacy preserving k-means collaborative clustering protocol is executed mainly at cloud side, with total \(O(k(m+n))\)-round interactions among the two parties and the cloud. Here, m and n means that the total numbers of records for the two parties, respectively. The protocol is secure in the semi-honest security model and especially secure in the malicious model supporting only one party corrupted during k centroids re-computation. We also implement it in real cloud environment using e-health data as the testing data.

Keywords

Privacy-preserving data mining k-means clustering Storage outsourcing Computation outsourcing Secure multiparty computation 

Notes

Acknowledgement

This work is supported by Basic Reasearch Project of Shenzhen of China (No. JCYJ20160318094015947), National Key Research and Development Program of China (No. 2017YFB0803002), National Natural Science Foundation of China (No. 61771222), Key Technology Program of Shenzhen, China (No. JSGG20160427185010977).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zoe L. Jiang
    • 1
  • Ning Guo
    • 1
  • Yabin Jin
    • 1
  • Jiazhuo Lv
    • 1
  • Yulin Wu
    • 1
  • Yating Yu
    • 1
  • Xuan Wang
    • 1
  • S. M. Yiu
    • 2
  • Junbin Fang
    • 3
  1. 1.Harbin Institute of Technology (Shenzhen)ShenzhenChina
  2. 2.The University of Hong KongHong KongChina
  3. 3.Jinan UniversityGuangzhouChina

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