An Ensemble Learning Approach for Privacy–Quality–Efficiency Trade-Off in Data Analytics

Peethambaran, Geetha; Naikodi, Chandrakant; Suresh, L. Padma

doi:10.1109/icosec49089.2020.9215250

Cited by 2 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address the overheads associated with MapReduce, several Spark-based approaches have been proposed in recent years [18,[25][26][27][28]. In [29], the authors proposed the INCOG-NITO framework for full-domain generalization using Spark RDDs.…”

Section: Related Workmentioning

confidence: 99%

Scalable, High-Performance, and Generalized Subtree Data Anonymization Approach for Apache Spark

2021

View full text Add to dashboard Cite

Data 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.

show abstract