Privacy-preserving process analytics

Overview

Modern organisations consider data to be their lifeblood. While the importance of data science and the potential benefits of data analytics are widely acknowledged, many people have grave concerns about irresponsible use of their data.

Negative publicity surrounding large collections of personal data (including location, videos, pictures and emails) by corporations such as Google and Facebook and subsequent breaches have resulted in people losing trust in, and becoming more suspicious about, how their personal data is being used.

Process mining is a specialised form of data-driven process analytics where process data, collated from different IT systems typically available in organisations, is analysed to uncover the real behaviour and performance of business operations.

Typically, this personal data is de-identified or sanitised during the pre-processing step by anonymising the names of customers, employees and business partners using codes. Most process mining techniques are ‘privacy agnostic’. These techniques consider sanitised data as the whole truth and reduces the accuracy of analysis results.

This research aims to address how to answer process-related questions with a guaranteed level of accuracy without compromising personal information.

Research activities

This research initiative is being conducted by a team of researchers from QUT's Business Process Management research group.

If you are selected, you will work closely with researchers with expertise in the area of process mining to design and develop a new software framework.

Outcomes

This project aims to develop:

• a privacy-preserving process analytics (PPPA) framework that supports data transformation and analysis techniques to guarantee the privacy of personal data while maintaining its utility
• novel process mining algorithms that utilise sanitised process data to generate accurate insights.

Skills and experience

You should have:

• a strong technical background in computer science, information technology, data science or equivalent.
• programming experience in Java and an interest in applied research with industry partners.

Scholarships

You may be able to apply for a research scholarship in our annual scholarship round.

Annual scholarship round

Keywords

• computer science
• data science
• information systems
• process mining
• business process management
• information privacy

Contact

Contact the supervisor for more information.