Bazai SUJang-Jaccard JAlavizadeh HGuitart J2023-11-142023-11-202021-03-032023-11-142023-11-202021-03-03Bazai SU, Jang-Jaccard J, Alavizadeh H. (2021). Scalable, high-performance, and generalized subtree data anonymization approach for apache spark. Electronics (Switzerland). 10. 5. (pp. 1-28).https://mro.massey.ac.nz/handle/10179/69139Data anonymization strategies such as subtree generalization have been hailed as techniques that provide a more efficient generalization strategy compared to full-tree generalization counterparts. Many subtree-based generalizations strategies (e.g., top-down, bottom-up, and hybrid) have been implemented on the MapReduce platform to take advantage of scalability and parallelism. However, MapReduce inherent lack support for iteration intensive algorithm implementation such as subtree generalization. This paper proposes Distributed Dataset (RDD)-based implementation for a subtree-based data anonymization technique for Apache Spark to address the issues associated with MapReduce-based counterparts. We describe our RDDs-based approach that offers effective partition management, improved memory usage that uses cache for frequently referenced intermediate values, and enhanced iteration support. Our experimental results provide high performance compared to the existing state-of-the-art privacy preserving approaches and ensure data utility and privacy levels required for any competitive data anonymization techniques.(c) 2021 The Author/sCC BYhttps://creativecommons.org/licenses/by/4.0/Sparksubtree generalizationprivacydata anonymizationResilient Distributed Dataset (RDD)Journal article10.3390/electronics100505892079-9292journal-article1-28http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000628013800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fefARTN 589