Faculty/School

Faculty of Science

School of Information Systems

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Primary Supervisor

Dr Gowri Ramachandran
Position
Senior Lecturer in Information Systems
Division / Faculty
Faculty of Science

Overview

Modern travel apps can automatically detect whether you are travelling by bus, train, or ferry. However, doing this often requires sending your location to external transport services, potentially revealing where you are, where you are going, and your daily travel habits.

This project aims to develop privacy-preserving techniques that allow Travelly, a smart mobility app, to identify nearby public transport without unnecessarily exposing users' locations.

You will investigate whether it is possible to accurately determine if someone is travelling on public transport while protecting their privacy using modern privacy-enhancing technologies.

Research engagement

  • Investigate the privacy risks of sharing location data with real-time public transport services.
  • Design techniques that minimise the amount of location information shared while maintaining accurate transport detection.
  • Implement and evaluate your solution within the Travelly mobile application.
  • Compare different privacy-preserving approaches and measure their impact on both accuracy and user trust.

Research activities

You may explore one or more of the following:

  • Client-side processing to avoid sending precise locations
  • Location obfuscation (geo-masking)
  • Anonymous or dummy location queries
  • Differential privacy
  • Secure matching techniques
  • Comparing direct transport feeds with third-party aggregators

Research skills

  • Privacy-enhancing technologies (PETs)
  • Location privacy and cybersecurity
  • Mobile application development (Flutter)
  • Secure software design
  • Data analysis and experimental evaluation

Outcomes

  • Literature review on privacy-enhancing technologies for smart mobility
  • Threat analysis of real-time transport data
  • Prototype implementation in Flutter
  • Evaluation using Brisbane public transport data
  • User study investigating the balance between privacy and usability

Skills and experience

  • Interest in cybersecurity, privacy, or mobile application development
  • Willingness to read and understand research papers
  • Knowledge of cybersecurity or privacy concepts
  • Familiarity with Git/GitHub
  • Basic data analysis using Python
  • Understanding of location services, GPS, or mapping APIs (desirable)

Start date

2 November, 2026

End date

19 February, 2027

Location

Y Block, QUT Gardens Point

Additional information

No prior knowledge of privacy-enhancing technologies is required. Students with an interest in cybersecurity, mobile applications, or software engineering are encouraged to apply.

Keywords

Contact

Deepak Honnalli

07 3138 9474

deepak.honnalli@hdr.qut.edu.au