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

被引：0
作者：
Shamsinejad E. [1 ]
Banirostam T. [1 ]
Pedram M.M. [2 ]
Rahmani A.M. [3 ]
机构：
[1] Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran
[2] Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran
[3] Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran
来源：
Annals of Data Science | 2025年 / 12卷 / 1期
关键词：
Anonymity; Big data; Confidentiality; Data disclosure; Privacy;
D O I：
10.1007/s40745-024-00556-x
中图分类号：
学科分类号：
摘要：
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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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收藏
页码：223 / 252
页数：29
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