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

Q1 Decision Sciences
Elham Shamsinejad, Touraj Banirostam, Mir Mohsen Pedram, Amir Masoud Rahmani
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引用次数: 0

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.

Abstract Image

使用内存处理来表示大数据流匿名化模型
针对大数据时代不断升级的隐私风险,本文介绍了一种利用Spark框架内内存处理的大数据流匿名化创新模型。该方法建立在k -匿名原则的基础上,通过批判性地评估各种匿名化方法和算法,对其在时间和空间复杂性方面的性能进行基准测试,推动了该领域的发展。在K-means算法中提出了一种独特的优化聚类确定公式,以及一种新的元组过期时间策略,用于有效地清除聚类。将这些组件集成到Spark的RDD和MLlib模块中,即使在数据量增加的情况下,也能显著减少执行时间和数据损失率。该论文的显著贡献是其方法上的进步,为数据匿名化提供了一个强大的、可扩展的解决方案,在不牺牲数据效用或处理效率的情况下保护用户隐私。
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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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