Abstract
Recent studies in data anonymization techniques have primarily focused on MapReduce. However, these existing MapReduce based approaches often suffer from many performance overheads due to their inappropriate use of data allocation, expensive disk I/O access and network transfer, and no support for iterative tasks. We propose “SparkDA” which is a new novel anonymization technique that is designed to take the full advantage of Spark platform to generate privacy-preserving anonymized dataset in the most efficient way possible. Our proposal offers a better partition control, in-memory operation and cache management for iterative operations that are heavily utilised for data anonymization processing. Our proposal is based on Spark’s Resilient Distributed Dataset (RDD) with two critical operations of RDD, such as FlatMapRDD and ReduceByKeyRDD, respectively. The experimental results demonstrate that our proposal outperforms the existing approaches in terms of performance and scalability while maintaining high data privacy and utility levels. This illustrates that our proposal is capable to be used in a wider big data applications that demands privacy.
Highlights
The rapid growth of data from many domainshas brought in a new era where extracting potential information using data analytic and data mining has become a top business priority to many organizations
In addition to additional utility measurement matrices for Discenibility Metric (DM) and Minimal Distortion (MD), we provide a new set of privacy measurement matrices, such as Kullback-Leibler-Divergence (KLD) and Information Entropy (IE ), to extensively investigate the privacy and utility trade-offs of our proposal
Data anonymization starts by counting the frequency, which represents the number of appearances given the record set, over the Quasi Identifiers Attributes (QID)
Summary
The rapid growth of data from many domains (e.g., social media, smartphones, IoT etc.). With the surge in the population of Spark and shift from MapReduce approach, many Spark-based data anonymization techniques have been proposed [15,16,17,18,19] These existing proposals often tend to focus their efforts on improving and readdressing the scalability aspects to be more suited for Spark instead of investigating the suitability of Spark as a platform of choice for data anonymization techniques. This is an extension of the earlier version which we presented in Reference [20].
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