Privacy-Preserving Healthcare Analytics in Indonesia Using Lightweight Blockchain and Federated Learning: Current Landscape and Open Challenges
Downloads
Healthcare data are invaluable assets in today’s digital age; however, they are also highly vulnerable to misuse, breaches, and unauthorized access. The global healthcare sector faces a significant dilemma: To leverage exceptionally enormous and heterogeneous datasets, the protection of patient privacy must be ensured while simultaneously improving medical services and public health understanding. In recent years, blockchain technology has emerged as a promising solution to manage healthcare data in a decentralized, transparent, tamperproof, as well as secure way. However, several natural limitations often obstruct many conventional blockchain systems. These limitations include scalability issues, high energy consumption, in addition to increased latency, and they can greatly impede practical adoption in resource-limited settings, particularly in developing countries such as Indonesia. These many limitations considerably spurred developers to create lightweight blockchain frameworks. These frameworks aim to retain all of the core benefits of blockchain, such as its immutability in addition to traceability, and optimize both performance and efficiency. In the event that an individual integrates the proposed system by means of federated learning, which allows training of machine learning models across distributed data sources without data privacy being compromised, the system subsequently offers a compelling solution for healthcare analytics that preserves privacy in its entirety. This paper explores integrated technologies in Indonesian healthcare and highlights their potential and limitations. This study discusses how data can improve services while protecting patient confidentiality despite increasing cyber threats. It also considers regional policies like the Personal Data Protection Law and the BPJS health insurance. Identified are certain open challenges, in addition to particular future research directions, for the purpose of addressing the practical, technical, and regulatory hurdles that must be overcome to realize secure and privacy-aware healthcare analytics in Indonesia.
Copyright (c) 2025 Viddi Mardiansyah, Luhur Bayuaji, Iwa Ovyawan Herlistiono, Sriyani Violina, Adi Purnama, Bagus Alit Prasetyo, Phuoc-Hai Hyunh (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
