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Anonymizing Big Data Streams Using In-memory Processing: A Novel Model Based on One-time Clustering

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Abstract

Big data privacy preservation is a critical challenge for data mining and data analysis. Existing methods for anonymizing big data streams using k-anonymity algorithms may cause high data loss, low data quality, and identity disclosure. In this paper, we propose a novel model for anonymizing big data streams using in-memory processing. The model uses a Spark framework to parallelize the anonymization process and a one-time clustering algorithm to avoid multiple iterations and allocate the data to optimal clusters. We evaluate the performance and effectiveness of the model using a real-world dataset and compare it with three popular k-anonymity algorithms: CRUE, Mean-Shift, and DBSCAN. The results show that the model has the lowest data loss and the highest data quality for different data sizes and k-values. The model is scalable, robust, adaptable, and flexible. The model can provide better data for data mining and data analysis while protecting data privacy and preventing data disclosure.

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Data Availability

The data that were generated and analyzed during the current study are available in the UCI Machine Learning Repository. The data are licensed under the CC BY-SA 4.0 license, and can be freely downloaded and used for research purposes: https://archive.ics.uci.edu/ml/datasets/adult.

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Acknowledgements

The authors thank the anonymous reviewers and the editor for their useful comments and suggestions.

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Elham Shamsinejad & Touraj Banirostam

  2. Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

    Mir Mohsen Pedram

  3. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Amir Masoud Rahmani

Authors
  1. Elham Shamsinejad
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  2. Touraj Banirostam
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  3. Mir Mohsen Pedram
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  4. Amir Masoud Rahmani
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Contributions

E.SH., T.B., and M.M.P. conceived and designed the study, collected and analyzed the data, and drafted the manuscript. A.M.R. critically revised the manuscript and gave final approval of the version to be published. All authors reviewed the manuscript.

Corresponding author

Correspondence to Touraj Banirostam.

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Shamsinejad, E., Banirostam, T., Pedram, M.M. et al. Anonymizing Big Data Streams Using In-memory Processing: A Novel Model Based on One-time Clustering. J Sign Process Syst 96, 333–356 (2024). https://doi.org/10.1007/s11265-024-01920-z

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