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~ Sibghat Ullah

Doctor of Philosophy, (Information Technology)
Study Completed: 2020
College of Sciences

Citation

Thesis Title
Building Privacy-Preservation Models for Distributed Processing Platforms

Data anonymization has been proposed as a way of preserving data privacy. However, most existing data anonymization approaches have only been designed to work with a small dataset within a single machine environment, and thus are often not suitable for big data. Of the approaches that can work with distributed processing platforms, taking advantage of scalability and other supports required for big data, many encounter implementation and performance bottlenecks. To overcome these, Mr Bazai proposed a set of novel data anonymization approaches for two popular distributed processing platforms for big data, MapReduce and Spark. Experimental studies confirmed that his models provide high performance and scalability while supporting high levels of data privacy and utility.

Supervisors
Associate Professor Julian Jang-Jaccard
Dr Xuyun Zhang
Professor Ruili Wang