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

Elham Shamsinejad (), Touraj Banirostam (), Mir Mohsen Pedram () and Amir Masoud Rahmani ()
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Elham Shamsinejad: Islamic Azad University
Touraj Banirostam: Islamic Azad University
Mir Mohsen Pedram: Kharazmi University
Amir Masoud Rahmani: Islamic Azad University

Annals of Data Science, 2025, vol. 12, issue 1, No 10, 223-252

Abstract: 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.

Keywords: Big data; Anonymity; Confidentiality; Data disclosure; Privacy (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s40745-024-00556-x

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