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Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing

Author

Listed:
  • Elham Shamsinejad

    (Islamic Azad University)

  • Touraj Banirostam

    (Islamic Azad University)

  • Mir Mohsen Pedram

    (Kharazmi University)

  • Amir Masoud Rahmani

    (Islamic Azad University)

Abstract

In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approach is founded on the principle of K-anonymity and propels the field forward by critically evaluating various anonymization methods and algorithms, benchmarking their performance with respect to time and space complexities. A distinctive formula for optimized cluster determination in the K-means algorithm is presented, along with a novel tuple expiration time strategy for the efficient purging of clusters. The integration of these components into Spark’s RDD and MLlib modules results in a significant decrease in execution time and data loss rates, even with increasing data volumes. The paper’s notable contributions are its methodological advancements that offer a robust, scalable solution for data anonymization, safeguarding user privacy without sacrificing data utility or processing efficiency.

Suggested Citation

  • Elham Shamsinejad & Touraj Banirostam & Mir Mohsen Pedram & Amir Masoud Rahmani, 2025. "Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing," Annals of Data Science, Springer, vol. 12(1), pages 223-252, February.
    • Handle: RePEc:spr:aodasc:v:12:y:2025:i:1:d:10.1007_s40745-024-00556-x
    DOI: 10.1007/s40745-024-00556-x
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