Protective Security for social media: Developing A privacy-preserving Mechanism to Calculate Social Trust Factor

Social media has become one of the widely embraced online activities, with a staggering number of users. In 2021, the global user base for social media exceeded 4.26 billion individuals, and this figure is expected to surge to nearly six billion by 2027. While the cyber harm and cyber security has been a concern in the connected social networking platforms, the social media data of the individuals can help to build an index that can help to identify behavioural metrics of individuals based on their activities. This PhD project will use a privacy-preserving mechanism to capture and analyse individual personal information and build a Social Trust Factor (STF) that can be used for establishing trust and credibility in a physical world. The proposed social trust factor will contribute to enhance the security aspects of two big pillars of protective security defined by the Australian Government, namely “personnel security” and “information security”. To this end, the proposed trust factor will help to measures the integrity and reliability of individuals who have access to sensitive areas or information .

Specific objective includes:

1. What privacy-preserving techniques (e.g., federated learning, differential privacy, homomorphic encryption) best balance utility for trust modeling vs. anonymity?

2. Which online behavioral patterns (e.g., consistency, network diversity, content authenticity) strongly correlate with real-world integrity/reliability?

3. How resilient is the STF against adversarial manipulation (e.g., fake endorsements, curated activity)?

This project will support building a cybersafe community by offering a data-driven method to assess individual trustworthiness using social media behaviour. It will support personnel and information security by enabling informed decisions. Based on the team of expertsfrom the IITM and Deakin, the PhD student will get a world-class research training and supervision. The project expects that the prospective students will have fantastic coding skills and knowledge in related areas with track record of publications and good academic results.