Multi-Party Federated Privacy Forensics

About the research project

Digital forensic investigations are increasingly requiring collaboration across multiple organisations, jurisdictions, and agencies to combat sophisticated cybercrimes effectively. Traditional centralised forensic analysis approaches face critical limitations when sensitive evidence cannot be shared due to stringent privacy regulations, legal constraints, and confidentiality requirements. Current forensic frameworks fundamentally lack robust mechanisms for enabling collaborative analysis whilst simultaneously preserving data sovereignty and maintaining privacy standards across participating entities.

The motivation for this research stems from the reality that modern cyber threats routinely span organisational boundaries, necessitating coordinated forensic responses that leverage collective intelligence. Law enforcement agencies, cybersecurity firms, and enterprises possess complementary forensic datasets that could significantly enhance threat detection, pattern recognition, and attribution capabilities when analysed collectively. However, privacy legislation such as GDPR and CCPA, national security concerns, and proprietary data restrictions prevent effective information sharing, creating forensic intelligence silos that substantially limit investigation effectiveness and cross-organisational learning opportunities.

This project addresses the fundamental research question: how can multiple organisations collaboratively perform comprehensive digital forensic analysis and share forensic intelligence without exposing sensitive evidence, violating privacy regulations, or compromising data sovereignty requirements? The research objectives include developing a federated learning architecture that enables collaborative forensic model training without raw data sharing, implementing differential privacy mechanisms ensuring individual case confidentiality whilst enabling pattern discovery, designing secure aggregation protocols for multi-party forensic intelligence synthesis, creating privacy-preserving threat attribution capabilities, and establishing legal compliance frameworks for cross-jurisdictional forensic collaboration.

Expected outcomes include novel federated learning algorithms for forensic data, privacy-preserving protocols with provable security, a prototype system for collaborative malware detection and threat attribution, comprehensive framework guidelines for federated forensic investigations, and performance benchmarks comparing centralised vs. federated analysis.

Primary supervisor

Meet Doctor Muhammad Bilal Amin

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

• a living allowance stipend funded by University of Tasmania of $34,315 per annum for 3.5 years
• a relocation allowance of up to $2,000
• a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

A tuition fee offset may be offered to eligible international applicants following competitive assessment 

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Other funding opportunities and fees

For further information regarding other scholarships on offer, and the various fees for undertaking a research degree, please visit our Scholarships and fees on research degrees page.

Eligibility

Applicants should review the Higher Degree by Research minimum entry requirements.

Ensure your eligibility for the scholarship round by referring to our Key Dates.

Additional eligibility criteria specific to this project/scholarship:

• Applicants must be able to undertake the project on-campus

Selection criteria

The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.

Additional essential selection criteria specific to this project:

• Masters by Research with specialisation in domains of Cybersecurity/AI/Distributed Systems Or Masters (Course work) with Industry experience in the domain of Cybersecurity Or Bachelors (Hons) with research component in the domains of Cybersecurity/AI/Distributed Systems
• Technical Skills - Expertise in digital forensic processes and evidence validation, fault-tolerant distributed systems, cyber-physical systems (CPS) and IoT infrastructure security, plus AI/machine learning capabilities for adaptive threat detection and behavioural analysis in adversarial environments.
• Research Skills - Strong critical thinking and analytical capabilities with a proven research record demonstrated through published article(s) in journals/conferences, along with experience in experimental design, data analysis, and interdisciplinary problem-solving methodologies

Additional desirable selection criteria specific to this project:

• Soft Skills - Excellent team collaboration and leadership abilities to work effectively across computer science domains. Strong communication skills for presenting complex technical concepts to diverse stakeholders, and adaptability to manage evolving project requirements in a dynamic research environment.

Application process

1. Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
2. Contact Doctor Muhammad Bilal Amin to discuss your suitability and the project's requirements; and
3. In your application:
   • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
   • Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
4. Apply prior to 1 October 2026.

Full details of the application process can be found under the ' How to apply ' section of the Research Degrees website.

Following the closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

Apply nowExplore other projects