Building privacy-preservation models for distributed processing platforms : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (Ph.D.) in Computer Science, Massey University, New Zealand
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Date
2020
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Massey University
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Abstract
The widespread proliferation of data collection has increased a serious privacy concern in recent years. Data anonymization approaches have been proposed as a privacy-preserving technique to preserve the privacy of data. However, most existing data anonymization approaches have been designed to work with a small number of datasets within a single machine environment thus often not suitable for big data. To resolve these limitations, many scalable data anonymization solutions that can work with the distributed processing platform (e.g., MapReduce and Spark) has emerged to take advantage of scalability and other supports required for big data. However, due to lack of inherent support for the algorithms involved in data anonymization techniques, these existing proposals often encounter many implementation and performance bottlenecks. In the studies presented in this thesis, we propose a set of novel data anonymization approaches that can work well in the most popular distributed processing platforms for big data such as MapReduce and Spark.
Our first set of studies address the privacy concerns involved in MapReduce platform that processes sensitive data without an appropriate privacy protection which may allow adversaries to break two very important security principals such as data confidentiality and integrity. Firstly, we propose a privacy-preservation platform as an extra layer on MapReduce to provide a set of privacy services to produce different sets of privacy-preserving anonymized datasets that can be safely processed by MapReduce. Secondly, we also offer a privacy-preserving $k$-NN based classifier for MapReduce. Instead of working with plaintext, our $k$-NN classifier can work on any anonymized datasets to protect the privacy concern of input data while still providing accurate classification results.
In our second set of studies, we address the concerns in Apache Spark that lack appropriate supports for many popular data anonymization techniques. We first investigate the requirement for the types of support required for many data anonymization approaches which often demand multiple read and write operations. We argue that existing approaches fail to provide supports for caching intermediate data in memory which found to contribute performance degradation. To address this problem, we propose a Resilient Distributed Dataset (RDD) based data anonymization model that avoids expensive disk I/O. We also argue that many existing methods do not provide support for iterative intensive operations which appear in many data anonymization technique such as subtree generalization. We propose a generic approach for implementing subtree-based data anonymization techniques for Spark that provide more effective support for iteration intensive operations. Extending from this, we also provide a novel hybrid approach that can more effectively apply different data anonymization techniques for multi-dimensional data. We argue that our hybrid approach offers much better control for the expensive RDD creation and the size of partitions attached for each RDD which is much better suited to reduce many overheads such as involved in re-computation, shuffle operations, message exchange, and cache management.
The experimental studies confirm that our novel privacy-preserving models implemented on both MapReduce and Spark provide high performance and scalability while supporting high levels of data privacy and utility.
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MapReduce (Computer file), Spark (Electronic resource : Apache Software Foundation), Data protection, Mathematical models