Anonymizing Big Data Streams Using In-memory Processing: A Novel Model Based on One-time Clustering
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| Format: | Recurso digital |
| Langue: | anglais |
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2025
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| _version_ | 1866901260190351360 |
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| author | Shamsinejad, Elham BaniRostm, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud |
| author_facet | Shamsinejad, Elham BaniRostm, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud |
| contents | <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_1007_s11265-024-01920-z |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Anonymizing Big Data Streams Using In-memory Processing: A Novel Model Based on One-time Clustering Shamsinejad, Elham BaniRostm, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Big Data Anonymity In-Memory Processing One-Time Clustering Data Loss <p>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.</p> |
| title | Anonymizing Big Data Streams Using In-memory Processing: A Novel Model Based on One-time Clustering |
| topic | Big Data Anonymity In-Memory Processing One-Time Clustering Data Loss |
| url | https://doi.org/10.1007/s11265-024-01920-z |