The aim of this research is to explore the vulnerabilities in Activity Recognition (AR) systems during Industrial Human-Robot Interaction. AR refers to the interpretation of sensor data to understand and recognize various activities performed by human and robot agents. These systems heavily rely on machine learning (ML) approaches for efficient processing. However, they are susceptible to different adversarial attacks, where untrusted agents manipulate sensor readings in a variety of ways to contaminate the training process. This manipulation can mislead the AR system and result in erroneous outcomes. This study aims to thoroughly investigate the vulnerabilities present in AR-ML systems and propose real-time, lightweight solutions that are efficient and effective. Read more 

This research project focuses on developing a secure, privacy-preserving, and resource-aware blockchain framework for interdependent smart cities. In smart city environments, various interconnected systems rely on data sharing and collaboration, making security and privacy crucial concerns. The proposed blockchain solution aims to ensure the integrity and confidentiality of data while optimizing resource utilization. By leveraging advanced cryptographic techniques and distributed consensus mechanisms, the framework will provide a trustworthy platform for secure transactions and data exchange. Additionally, resource-awareness will enable efficient utilization of computing resources, addressing the scalability challenges associated with blockchain technology. The research project aims to contribute to the development of sustainable and resilient smart city infrastructures by enabling secure and privacy-preserving interconnectivity between diverse systems. Read more

This research project focuses on developing innovative machine learning techniques that prioritize privacy preservation, transparency, and interpretability in digital healthcare. It involves designing models that provide accurate predictions while safeguarding sensitive patient data through differential privacy techniques. The project also emphasizes creating explainable machine learning algorithms to enhance trust and understanding in healthcare decision-making. Collaboration with healthcare institutions and real-world data validation aim to advance privacy-aware and transparent machine learning solutions for accurate predictions and informed healthcare choices, while upholding patient privacy and regulatory compliance. Read more

The research project aims to develop a usable privacy-preserving mechanism for continuous location sharing in the Internet of Social Things systems. In IoT environments, continuous location sharing is crucial for various applications, including social networking and personalized services. However, privacy concerns arise as users are required to share their location data with IoT systems. This project seeks to design a mechanism that allows users to maintain control over their location information while still enjoying the benefits of IoT services. The proposed solution will employ privacy-enhancing techniques such as secure data aggregation and differential privacy to protect sensitive location information. Read more