Blockchain – Academy of Technology

Block Chain

Blockchain is a decentralized, distributed ledger technology that enables secure, transparent, and tamper-resistant recording of transactions. Originally designed as the underlying framework for cryptocurrencies, blockchain has evolved into a versatile technology with applications spanning finance, supply chain, healthcare, governance, and beyond.

At the Academy of Technology (AOT), research in blockchain focuses on advancing scalability, interoperability, and energy efficiency while maintaining the fundamental principles of security and decentralization. Faculty and students explore consensus mechanisms such as Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and emerging hybrid models to improve transaction throughput and reduce resource consumption. Smart contract development is another key area, enabling automated, trustless execution of agreements across diverse application domains. Applications of blockchain research at AOT extend across multiple sectors. In finance, blockchain ensures secure and transparent digital transactions while reducing reliance on intermediaries. In supply chain management, it enables end-to-end traceability of goods, enhancing accountability and reducing fraud. In healthcare, blockchain protects patient data and facilitates secure sharing between stakeholders. Emerging areas include decentralized identity management, voting systems, intellectual property protection, and integration with the Internet of Things (IoT) for autonomous, trust-based operations. Security and privacy in blockchain systems are critical research priorities. AOT’s work addresses vulnerabilities such as 51% attacks, double-spending, and smart contract exploits by developing robust encryption techniques, secure key management systems, and privacy-preserving transaction methods like zero-knowledge proofs. Research is supported by state-of-the-art computing facilities and collaboration with fintech companies, cybersecurity experts, and academic partners. Students gain hands-on experience through blockchain development platforms, hackathons, and interdisciplinary projects that combine blockchain with AI, IoT, and cloud computing.

Looking forward, AOT aims to explore blockchain integration with emerging technologies such as quantum computing and Web3 infrastructure, driving innovation toward more secure, decentralized, and transparent digital ecosystems

Researchers:

Dr. Nabanita Das (CSE Department)
Mr. Subhashis Das (CSE Department)

Projects:

Secure and Automated Relief Distribution in Smart Cities Using Blockchain and Machine Learning over Wireless Networks
PredictGuard: Transparent AI for Secure Health Predictions on Blockchain
BCredS: Blockchain Leveraged Secure System for Credential Management in Emergency Scenarios
Implementation of Hyperledger Fabric in Different Deep Learning Models

Problem Description: In smart cities, post-disaster relief distribution demands robust cybersecurity, automation, and uninterrupted connectivity. However, traditional systems often face challenges like data tampering, misallocation of resources, and network disruptions. This research proposes a blockchain-enabled framework operating over wireless networks to ensure secure, transparent, and tamper-proof distribution of relief. Additionally, machine learning is integrated to automate beneficiary identification, demand prediction, and priority-based resource allocation—enhancing responsiveness and minimizing human error during crisis situations.

Focus of Research:

Ensuring secure, tamper-proof, and transparent relief distribution in smart cities using blockchain over wireless networks
Automating decision-making processes such as beneficiary identification, demand forecasting, and resource prioritization using machine learning
Designing a resilient and efficient post-disaster communication framework that maintains data integrity and public trust without relying on centralized control

Publications:

Nabanita Das, Souvik Basu and Sipra Das Bit, OlaRout: Optimal dropbox deployment based cluster routing for post disaster information exchange in a smart city”, Springer Peer-to-Peer Networking and Applications, 16, 876–899, 2023. https://doi.org/10.1007/s12083-022-01433-1
Nabanita Das, Souvik Basu and Sipra Das Bit, ReliefChain: A blockchain leveraged post-disaster relief allocation system over smartphone-based DTN, Springer Peer-to-Peer Networking and Applications, vol. 15, pp. 2603-2618, 2022. https://doi.org/10.1007/s12083-022-01366-9
Nabanita Das, Souvik Basu and Sipra Das Bit, Efficient DropBox Deployment towards Improving Post Disaster Information Exchange in a Smart City, ACM Transactions on Spatial Algorithms and Systems, vol. 6(2), pp. 1-18, 2020. https://doi.org/10.1145/3373645
N. Das and S. Das, BCredS: Blockchain Leveraged Secure System for Credential Management in Emergency Scenarios, 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT), Hooghly, India, 2024, pp. 1-6, https:// 10.1109/C3IT60531.2024.10829445
Nabanita Das, Souvik Basu and Sipra Das Bit, Hm2Sc: Human Movement Model for Post Disaster Scenario in Smart City, C-NetSys co-located with ACM MobiCom, pp. 1-6, 2018. https://doi.org/10.1145/3265997.3265998
Nabanita Das and Sipra Das Bit, ProDiP: PDF based dropbox deployment for improved performance of DTN placed for emergency situation handling in a Smart city, 11th IEEE International Conference on Advanced Networks and Telecommunication Systems (ANTS), pp. 1-6, 2017. https://doi.org/10.1109/ANTS.2017.8384170
Nabanita Das, Animesh Roy and Sipra Das Bit, LionBEAR: A location based energy aware routing scheme in DTNs, 3rd IEEE International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC), pp. 75-80, 2016. https:// 10.1109/DIPDMWC.2016.7529367

Book Chapters:

Nabanita Das, Souvik Basu and Sipra Das Bit, A blockchain leveraged postdisaster relief allocation system over an incentive-based DTN, in the book: “ The Role of Blockchain in Disaster Management”, Elsevier, 2024.

Student Projects:

Title: AI Shield: AI Driven Disaster Management and Emergency Health Services Mobile Application

Students: Kuntal Pal, Manish Sharma, Preetam Pal, Ritam Samanta, Santosh Maity

Supervisor: Dr. Nabanita Das (CSE Department)

Year: 2025

Problem Description: Accurate disease prediction using machine learning holds immense potential in healthcare, but it often comes at the cost of patient privacy and interpretability. Centralized systems risk data breaches, and black-box AI models lack transparency, making clinical adoption difficult. This research presents a privacy-preserving disease prediction framework that integrates machine learning with blockchain to ensure secure, decentralized, and tamper-proof data handling. Furthermore, Explainable AI (XAI) techniques are employed to provide transparent, interpretable predictions, enabling medical professionals to trust and understand AI-driven decisions while maintaining strict privacy standards.

Focus of Research:

Developing a secure and decentralized disease prediction framework using machine learning integrated with blockchain to ensure patient data privacy and integrity
Incorporating Explainable AI (XAI) techniques to provide transparent and interpretable predictions for clinical decision support
Automating the detection and diagnosis process while maintaining trust, accountability, and regulatory compliance in smart healthcare systems

Publications:

Nabanita Das, Souvik Basu and Sipra Das Bit, Incentive Minimization using Energy and Buffer Efficient Routing Protocol over Blockchain enabled DTN, PPNA, Springer, 17, 3239–3254, 2024. https://doi.org/10.1007/s12083-024-01737-4
S. Das, S. Banerjee, P. Banerjee and N. Das, Securing Comparative Study of Leaf Disease Identification with Different Deep Learning Models Using Hyperledger Fabric, 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT), Hooghly, India, 2024, pp. 1-6, https://10.1109/C3IT60531.2024.10829471

Student Projects:

Title: Smart and Secure Diagnosis of Cancer Using Machine Learning with Blockchain-Assisted Patient Privacy

Students: Ramkrishna Giri, Amirul Ali Mallick, Chandana Jana, Tripurari Sen, Soumyojit Dutta

Supervisor: Dr. Nabanita Das (CSE Department)

Year: 2025

Problem Description: In emergencies, rapid and secure identity verification and credential access are vital, especially for medical, professional, or governmental needs. Centralized systems often fall short, lacking reliability, speed, and tamper-proof mechanisms. A decentralized, secure solution is essential to ensure seamless access and trust in high-stakes, time-sensitive situations which is where this research work aims to plug the gap.

Focus of Research:

Design a Blockchain-based credential management system (BCredS) for decentralized, tamper-proof verification during emergencies.
Ensure data integrity, transparency, and auditability using smart contracts.
Leverage permissioned blockchain frameworks for privacy and role-based access.

Problem Description: Accurate identification of plant leaf diseases using deep learning plays a crucial role in precision agriculture, enabling timely interventions and improved crop yield. However, these AI models and their predictions are vulnerable to manipulation, adversarial attacks, or unauthorized access, potentially leading to misinformation, compromised decisions, and disruptions in agricultural supply chains.

Focus of Research:

Design a Blockchain-based credential management system (BCredS) for decentralized, tamper-proof verification during emergencies.
Ensure data integrity, transparency, and auditability using smart contracts.
Leverage permissioned blockchain frameworks for privacy and role-based access.