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Ram, Kim Sa
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Computer Science & IT

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Blockchain Based Zero Knowledge Proof Protocol For Privacy Preserving Healthcare Data Sharing Myeong, Go Eun; Ram, Kim Sa
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i1.296

Abstract

The rise of digital healthcare has intensified concerns over data privacy, particularly in cross-institutional medical data exchanges. This study introduces a blockchain-based protocol leveraging Zero-Knowledge Proofs (ZKP), specifically zk-SNARK, to enable verifiable yet privacy-preserving health data sharing. Built on a permissioned Ethereum blockchain, the protocol ensures that medical data validity can be confirmed without disclosing sensitive content. System implementation involves Python-based zk-circuits, smart contracts in Solidity, and RESTful APIs supporting HL7 FHIR formats for interoperability. Performance evaluations show promising results: proof verification times remained under 100 ms, with average proof sizes below 2 KB, even under complex transaction scenarios. Gas consumption analysis indicates a trade-off—ZKP-enabled transactions consumed approximately 93,000 gas units, compared to 52,800 in baseline cases. Interoperability testing across 10 FHIR-based scenarios resulted in 100% parsing success and an average data integration time of 1.7 seconds. Security assessments under white-box threat models confirmed that sensitive information remains unreconstructable, preserving patient confidentiality. Compared to previous implementations using zk-STARK, this protocol offers a 30% improvement in verification efficiency and a 45% reduction in proof size. The novelty lies in combining lightweight ZKP mechanisms with an interoperability-focused design, tailored for realistic hospital infrastructures. This research delivers a scalable, standards-compliant architecture poised to advance secure digital healthcare ecosystems while complying with regulations like GDPR
A Hybrid Noise Reduction And Normalization Framework For Improving Multimodal Sensor Data Quality In Real-Time Systems Ram, Kim Sa; Hoon, Park Ji; Yeon, Hong Jae
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.440

Abstract

Multimodal sensor data, integrating signals such as RGB, LiDAR, and IMU, plays a pivotal role in enabling intelligent decision-making in real-time Internet of Things (IoT) systems. However, these data streams are inherently prone to complex noise patterns, cross-sensor inconsistencies, and scaling disparities that conventional preprocessing techniques often fail to address comprehensively. This paper presents a hybrid data preprocessing framework that unifies advanced denoising and adaptive normalization in a single, context-aware pipeline. The framework leverages wavelet-based denoising for high-frequency noise suppression, Kalman filtering for dynamic state estimation, and a real-time adaptive normalization mechanism that calibrates data scaling based on temporal and environmental contexts. Evaluations on synchronized multimodal IoT datasets comprising RGB, LiDAR, and IMU recordings under low-light, high-noise, and adverse-weather conditions (≈ 18,000 aligned samples; 30 Hz, 10 Hz, 100 Hz) show significant performance gains. Results indicate a 30.4% RMSE reduction (p < 0.05), 33% faster convergence, and only 34% computational overhead, while maintaining real-time feasibility with a 41 ms latency per frame. These findings confirm that combining complementary denoising paradigms with adaptive, context-driven normalization enhances signal fidelity and responsiveness in dynamic sensing environments. This contribution presents a reproducible, statistically validated hybrid preprocessing framework for enhancing the quality of multimodal sensor data, enabling more reliable deployments in industrial automation, environmental monitoring, and intelligent transport systems.
Co-Authors Hoon, Park Ji Myeong, Go Eun Yeon, Hong Jae