Erza received the bachelor and Master degrees in electrical engineering from the Bandung Institute of Technology (ITB), Indonesia, in 2013 and 2014, respectively, and the Ph.D. degree from the School of Computing, Korea Advanced Institute of Science and Technology (KAIST), South Korea, in 2018. He is currently an Assistant Professor with Monash University Indonesia, Cyber Security program.

He was a researcher at National Institute of Information and Communications Technology (NICT), Tokyo, Japan in AI x Security and a lecturer at University of Indonesia (UI) in cyber crime. He is also a Senior Research (Data) Scientist at Jakarta Smart City and advisory board for several government and private organisations. His current research interests include information security, artificial intelligence, anomaly detection, intrusion detection, cybersecurity, digital transformation and smart city.

Teaching interests:

• Introduction to Cryptography
• Information Security
• Digital Forensics
• Information Security and Risk Management
• Business Continuity Plan and Disaster Recovery Plan
• Professional Practice

Research Interests:

• information security,
• artificial intelligence,
• anomaly detection,
• intrusion detection,
• cybersecurity,
• Privacy Preserving Machine Learning
• digital transformation,
• smart city.

Program Coordinator of Master Cyber Security, Monash University Indonesia

Project Title: Enabling Privacy-Preserving Artificial Intelligence in Smart City Context

Project Description: 

Preserving privacy while deriving policy-relevant insights from citizen statistics poses a critical challenge for governments worldwide. This research integrates interdisciplinary perspectives to develop a holistic framework for privacy-preserving machine learning in the government context. Acknowledging the rise of personal data laws globally, we emphasise the importance of data privacy and explore the use of differential privacy as a technique to address privacy concerns during data analysis. However, recent studies indicate potential trade-offs in machine learning model performance when implementing differential privacy. Hence, our study aims to evaluate its impact on real data. We analyse the challenges and opportunities associated with privacy-preserving machine learning in government contexts. This research examines the delicate balance between privacy preservation, model accuracy, and policy formulation, considering factors from various aspects, as well as the broader societal implications of data-driven policies. This study will contribute to understanding the practicality and limitations of employing differential privacy in government settings, fostering interdisciplinary collaboration. Furthermore, this study should provide recommendations for organisations handling personal data, facilitating effective utilisation of privacy-preserving machine learning techniques. Ultimately, this interdisciplinary perspective empowers any organisation to make informed policy decisions based on accurate insights while safeguarding people's privacy.