Skip to content

Privacy-Preserving Process Analytics

Study level

PhD

Master of Philosophy

Honours

Vacation research experience scheme

Faculty/Lead unit

Science and Engineering Faculty

School of Information Systems

Topic status

We're looking for students to study this topic.

Supervisors

Professor Moe Thandar Wynn
Position
Professor
Division / Faculty
Science and Engineering Faculty

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 (incl. location, videos, pictures, emails, etc.) 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 by such organisations.

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, such 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 such (sanitised) data as the whole truth, thus reducing the accuracy of analysis results. There exists an open research question on 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 the Business Process Management discipline.

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

The aims of this research initiative are to:

  1. 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
  2. develop 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.

You should also have 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

Contact

Contact the supervisor for more information.