Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

Full list of indices available on SPARK. Each index can be extracted from the weighted directed, weighted undirected, unweighted directed and unweighted undirected version of the underlying network depending on the scenario to deal with.

Synthetic data generating parameters. The table summarizes the generating parameters for synthetic networks showing the corresponding symbol, name and range after the application of the constraints in Section e.2.

ANOVA results for the distributions in Fig 8. Four different one-way ANOVA for each combination of and in the toy example #2. The corresponding p and F values are shown in this table.