publications

2025

1. IEEE Conf.

   TruChain: A Multi-Layer Architecture for Trusted, Verifiable, and Immutable Open Banking Data

   Aufa Rahman, Bimo Hantono, and Guntur Putra

   Aug 2025

   Abs DOI

   Open banking framework enables third party providers to access financial data across banking institutions, leading to unprecedented innovations in the financial sector. However, some open banking standards remain susceptible to severe technological risks, including unverified data sources, inconsistent data integrity, and lack of immutability. In this paper, we propose a layered architecture that provides assurance in data trustworthiness with three distinct levels of trust, covering source validation, data-level authentication, and tamper-proof storage. The first layer guarantees the source legitimacy using decentralized identity and verifiable presentation, while the second layer verifies data authenticity and consistency using cryptographic signing. Lastly, the third layer guarantees data immutability through the Tangle, a directed acyclic graph distributed ledger. We implemented a proof-of-concept implementation of our solution to evaluate its performance, where the results demonstrate that the system scales linearly with a stable throughput, exhibits a 100% validation rate, and utilizes under 35% of CPU and 350 MiB memory. Compared to a real-world open banking implementation, our solution offers significantly reduced latency and stronger data integrity assurance. Overall, our solution offers a practical and efficient system for secure data sharing in financial ecosystems while maintaining regulatory compliance.

2023

1. IEEE Conf.

   Unmanned Aerial Vehicle (UAV) Data-Driven Modeling Software with Integrated 9-Axis IMU- GPS Sensor Fusion and Data Filtering Algorithm

   Azfar Arfakhsyad, Aufa Rahman, Larasati Kinanti, and 2 more authors

   Oct 2023

   Abs DOI

   Unmanned Aerial Vehicles (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper proposes data-driven modeling software for UAV. Emphasizes the utilization of cost-effective sensors to obtain orientation and location data subsequently processed through the application of data filtering algorithms and sensor fusion techniques to improve the data quality to make a precise model visualization on the software. UAV’s orientation is obtained using processed Inertial Measurement Unit (IMU) data and represented using Quaternion Representation to avoid the gimbal lock problem. The UAV’s location is determined by combining data from the Global Positioning System (GPS), which provides stable geographic coordinates but slower data update frequency, and the accelerometer, which has higher data update frequency but integrating it to get position data is unstable due to its accumulative error. By combining data from these two sensors, the software is able to calculate and continuously update the UAV’s real-time position during its flight operations. The result shows that the software effectively renders UAV orientation and position with high degree of accuracy and fluidity.