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Privacy Preserving Approximate K-means Clustering

Published: 03 November 2019 Publication History

Abstract

Privacy preserving computation is of utmost importance in a cloud computing environment where a client often requires to send sensitive data to servers offering computing services over untrusted networks. Eavesdropping over the network or malware at the server may lead to leaking sensitive information from the data. To prevent this, we propose to encode the input data in such a way that, firstly, it should be difficult to decode it back to the true data, and secondly, the computational results obtained with the encoded data should not be substantially different from those obtained with the true data. Specifically, the computational activity that we focus on is the K-means clustering, which is widely used for many data mining tasks. Our proposed variant of the K-means algorithm is capable of privacy preservation in the sense that it requires as input only binary encoded data, and is not allowed to access the true data vectors at any stage of the computation. During intermediate stages of K-means computation, our algorithm is able to effectively process the inputs with incomplete information seeking to yield outputs relatively close to the complete information (non-encoded) case. Evaluation on real datasets show that the proposed methods yields comparable clustering effectiveness in comparison to the standard K-means algorithm on image clustering (MNIST-8M dataset), and in fact outperforms the standard K-means on text clustering (ODPtweets dataset).

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cover image ACM Conferences
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
November 2019
3373 pages
ISBN:9781450369763
DOI:10.1145/3357384
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 November 2019

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Author Tags

  1. centroid estimation
  2. k-means clustering
  3. privacy preservation

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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  • (2024)Privacy-Preserving Continual Federated Clustering via Adaptive Resonance TheoryIEEE Access10.1109/ACCESS.2024.346711412(139692-139710)Online publication date: 2024
  • (2024) On the Security of Distributed Multi-Agent K -Means Clustering With Local Differential Privacy IEEE Access10.1109/ACCESS.2024.345482312(124751-124763)Online publication date: 2024
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  • (2023)Heterogeneous Federated Learning for Balancing Job Completion Time and Model Accuracy2022 IEEE 28th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS56603.2022.00079(562-569)Online publication date: Jan-2023
  • (2023)Weakly supervised deep metric learning on discrete metric spaces for privacy-preserved clusteringInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10310960:1Online publication date: 20-Jan-2023
  • (2023)K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big dataInformation Sciences10.1016/j.ins.2022.11.139622(178-210)Online publication date: Apr-2023
  • (2022)Gromov-Wasserstein Multi-modal Alignment and ClusteringProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557339(603-613)Online publication date: 17-Oct-2022
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