Institute for Development and Research in Banking Technology

In the healthcare sector, the privacy of confidential patient data is a primary concern. Blockchain's decentralized management and cryptographic techniques enhance patient data security by discouraging unauthorized access and reducing the likelihood of data leaks. The use of blockchain technology in e-health is transforming healthcare by increasing data security, patient privacy, and interoperability. Various approaches such as homomorphic encryption, k-anonymity, and differential privacy have been utilized since long ago to ensure safe sharing, storage, and utilization of medical data. The application of blockchain technology in health care has arrived as an innovative force, addressing major challenges related to data security, privacy, and efficacy in e-health systems. Estonia's healthcare (MFFSIA) blockchain-based real use case is addressed in this chapter, which considers consent management and access control based on roles. Data-sharing mechanisms secured are also discussed, enabling cross-institutional collaboration while ensuring regulatory compliance. In totality, the chapter discusses blockchain's role in managing healthcare data, its advantages over traditional systems, and its diverse applications.

Public key encryption with keyword search (PEKS) is a method to search over encrypted data in the cloud. However, traditional PEKS schemes are based on classical mathematical problems, which are vulnerable to quantum attacks. To address the issues, several authors proposed lattice-based PEKS. Although lattice-based PEKS resist quantum attacks, they are inefficient and also suffer from certificate management problem (CMP) or key escrow problem (KEP). To address these issues, in this paper, we propose module lattice-based certificateless encryption with keyword search (ML-CLEKS) without trapdoor algorithms. The security of ML-CLEKS is proved against TYPE-I and TYPE-II adversaries based on Module Learning with Error (Module LWE) in random oracle model. Theoretical analysis and experimental results demonstrate that ML-CLEKS is efficient.

Digital lending platforms have been instrumental in processing loans faster and more efficiently by ensuring borrower verification through different channels. Thereby enhancing customer experience. The ecosystem is designed to collect applicants data from government data repositories, which is shared with untrusted third parties to assess the applicant's creditworthiness. This raises privacy concerns over borrowers data, consisting of bank statements, occupation information, and assets, which must be secure and private. Further, these platforms are designed with centralized control, which increases the risk of a single point of failure. Hence, there is a need to design a secure and efficient decentralized privacy-enhanced framework for the lending process. This paper proposes a blockchain-powered decentralized framework with functional encryption to ensure the privacy of data exchanged between borrowers/lenders and authority for verifying creditworthiness. The proposed solution is developed over the Ethereum platform. Further, computation analysis and performance analysis are discussed briefly.

Cross-chain asset exchange is crucial for blockchain interoperability. Existing solutions rely on trusted third parties and risk asset loss, or use decentralized alternatives like atomic swaps, which suffer from grief attacks. Griefing occurs when a party prematurely exits, locking the counterparty's assets until a timelock expires. Hedged Atomic Swaps mitigate griefing by introducing a penalty premium; however, they increase the number of transactions from four (as in Tier Nolan's swap) to six, which in turn introduces new griefing risks. Grief-Free (GF) Swap reduces this to five transactions by consolidating assets and premiums on a single chain. However, no existing protocol achieves grief-free asset exchange in just four transactions. This paper presents 4-Swap, the first cross-chain atomic swap protocol that is both grief-free and bribery-safe, while completing asset exchange in just four transactions. By combining the griefing premium and principal into a single transaction per chain, 4-Swap reduces on-chain transactions, leading to faster execution compared to previous grief-free solutions. It is fully compatible with Bitcoin and operates without the need for any new opcodes. A game-theoretic analysis shows that rational participants have no incentive to deviate from the protocol, ensuring robust compliance and security.

Facial expression recognition (FER) plays a crucial role in computer vision, driving advancements in gesture recognition, patient monitoring, and human-robot interaction. Despite its potential, traditional FER methods struggle with geometric variations in facial expression features within static images, often leading to an imbalance between model performance and network complexity. This results in increased computational demands, hindering their deployment in resource-constrained environments such as the Internet of Things (IoT) or Web of Things (WoT) and mobile. To address these challenges, we propose the Deformable Multi-Scale Net work (DMSNet), a lightweight and efficient model specifically designed to capture multi-scale geometric variations in spatial expression features dynamically using depthwise separable convolution, deformable convolution and Receptive Field Blocks (RFBs) while minimizing parameters. It is highly suitable for real-time applications with just 5.6 million parameters, 100.8 million floating point operations, and 30 frame-per-second (FPS) inference speeds. Extensive experiments on five benchmark datasets demonstrate superior performance compared to state-of-the-art (SOTA) models for FER.