SPARK-Based Partitioning Algorithm for k-Anonymization of Large RDFs

Temuujin, Odsuren; Jeon, Minhyuk; Seo, Kwangwon; Ahn, Jinhyun; Im, Dong-Hyuk

doi:10.1007/978-981-32-9244-4_41

Cited by 1 publication

(1 citation statement)

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“…K-anonymity, originally proposed by Sweeney [1], has received the most research attention among models aimed at preserving data privacy and numerous algorithms have been developed for k-anonymity and are used in practice [2]- [7]. Furthermore, several studies have been performed by applying such algorithms and large data platforms such as Apache Hadoop and Apache Spark to preserve privacy of large datasets [11]- [14]. A dataset satisfies k-anonymity when every record in the dataset is indistinguishable from at least k-1 other records with respect to quasi-identifier attributes.…”

Section: A K-anonymitymentioning

confidence: 99%

Efficient L-Diversity Algorithm for Preserving Privacy of Dynamically Published Datasets

Temuujin

Ahn

2019

IEEE Access

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Although most conventional methods of preserving data privacy focus on static datasets, which remain unchanged after processing, real-world datasets may be dynamically modified often. Therefore, privacy-preservation methods must maintain data privacy after dataset modification. Re-anonymization of entire datasets is inefficient when large datasets are frequently modified. Although several previous studies have addressed data privacy for incremental data updates (i.e., record insertions), they have not adequately it for dynamic changes made to existing datasets (i.e., record updates and deletions). Therefore, we identified limitations of data-privacy preservation for dynamically evolving datasets and used anatomy instead of generalization and suppression to develop a more efficient l-diversity algorithm for preserving privacy of such datasets. We also used a Cuckoo filter, a new probabilistic data structure for approximate set-membership tests, to improve data-processing efficiency. Experimental results demonstrated that our proposed data-anonymization algorithm processed data more efficiently than other conventional algorithms, requiring much less running time than conventional re-anonymization of entire datasets. The Cuckoo-filtered algorithm was especially efficient, dramatically reducing operation execution times while maintaining privacy of dynamically evolving datasets.

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