Towards Secure and Decentralized EDRMS: A Multi-Party Computation and Federated Byzantine Approach

Legacy centralized Electronic Document and Records Management Systems (EDRMS) are confronted with security vulnerabilities, single failure points, and regulatory compliance. This paper envisions a decentralized EDRMS with Secure Multi-Party Computation (SMPC) to ensure privacy-preserving data computation and a Federated Byzantine Agreement (FBA) for fault-tolerant consensus. Secure Multi-Party Computation provides secure computation with secret inputs without sacrificing individual data inputs, while the Federated Byzantine Agreement enhances system reliability by consensus despite faulty or malicious nodes. The proposed solution significantly increases data security, scalability, and compliance with regulatory requirements through cryptographic techniques and distributed protocols. The performance assessment demonstrates a significant rise in accuracy (93%), efficiency (92%), recall (94%), and F1-score (91%), performing better than traditional centralized models. The framework is a solid, privacy-compliant solution for businesses handling sensitive data, like healthcare and finance, to provide safe, resilient, and efficient document management in decentralized settings.

Authors

Institutions

Publication Details

Published
2025-06-06
DOI
https://doi.org/10.1109/iceti4t63625.2025.11132258
Citations
1
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
2.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Towards Secure and Decentralized EDRMS: A Multi-Party Computation and Federated Byzantine Approach

Faheem Khan, Raj Kumar Gudivaka, Rajya Lakshmi Gudivaka, Basava Ramanjaneyulu Gudivaka et al.
1 citations
Privacy-Preserving Technologies in Data
2.48
article

Towards Secure and Decentralized EDRMS: A Multi-Party Computation and Federated Byzantine Approach

Faheem Khan, Raj Kumar Gudivaka, Rajya Lakshmi Gudivaka, Basava Ramanjaneyulu Gudivaka, Sri Harsha Grandhi, Dinesh Kumar Reddy Basani
article en
1 citations

Abstract

Legacy centralized Electronic Document and Records Management Systems (EDRMS) are confronted with security vulnerabilities, single failure points, and regulatory compliance. This paper envisions a decentralized EDRMS with Secure Multi-Party Computation (SMPC) to ensure privacy-preserving data computation and a Federated Byzantine Agreement (FBA) for fault-tolerant consensus. Secure Multi-Party Computation provides secure computation with secret inputs without sacrificing individual data inputs, while the Federated Byzantine Agreement enhances system reliability by consensus despite faulty or malicious nodes. The proposed solution significantly increases data security, scalability, and compliance with regulatory requirements through cryptographic techniques and distributed protocols. The performance assessment demonstrates a significant rise in accuracy (93%), efficiency (92%), recall (94%), and F1-score (91%), performing better than traditional centralized models. The framework is a solid, privacy-compliant solution for businesses handling sensitive data, like healthcare and finance, to provide safe, resilient, and efficient document management in decentralized settings.

Gachon University (KR), Intel (United States) (US), Wipro (India) (IN), CGI (Canada) (CA)
Reduced inequalities
Openalex Percentile: Top 9%
Privacy-Preserving Technologies in Data
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Towards Secure and Decentralized EDRMS: A Multi-Party Computation and Federated Byzantine Approach — Faheem Khan, Raj Kumar Gudivaka, et al. · (2025) | TGRS Research Map | TGRS