This paper discusses our plan for an education program for data driven security for machine learning applications. We discuss the motivation for such a program, details of the program, various course modules to be developed as well as modules to be incorporated into our current courses in cyber security.
Recent digital transformation across industries has extended the demands for secure cross-domain transactions and data sharing. However, traditional Public Key Infrastructure (PKI)-based systems encounter challenges deriving from implementing centralized identity management setting, such as single-point failures, data silos, and insufficient privacy safeguards. Blockchain-based Decentralized Identity (DID) schemes have emerged as a promising solution to addressing cross-domain authentication challenges. In this work, we have systematically examined DID applications in cross-domain authentication, analyzing core mechanisms, e.g., privacy-preserving techniques, anonymous credentials, and lightweight verification protocols. We also investigate a few key supportive technologies, including Zero-Knowledge Proofs (ZKP) and cryptographic accumulators. Main findings of this work covers identifying consensus mechanism limitations and suggesting future potential research directions.
The welfare, productivity, and health of livestock are being impacted steadily by climate change and extreme weather events. For instance, it is predicted that heat stress will cause dairy cows in Southeast England to lose more than 170 kg of milk a year; in the hottest UK regions, this loss could increase to 1,300 kg per cow (18.6% of annual yield) by the 2090s. A solid foundation for evaluating environmental stress is presented in this paper. It is based on agro-environmental indices that are obtained from temperature and humidity data. To improve predictive capabilities, the proposed Virtualized Farming Analytics for Smart Decision-Making (V-Farm) system uses state-of-the-art synthetic data generation techniques, such as TCGAN and polynomial fitting. A novel approach to lessen the effects of thermal stress in precision livestock farming is to incorporate these indices into automated ventilation systems.
Blockchain technology, providing security, trust, and transparency for a variety of applications, has become a revolutionary force in decentralized systems. However, scalability continues to be a major obstacle, restricting its use in large-scale and high-throughput settings. To assess the trade-offs between decentralization, security, and computing efficiency, our survey provides a thorough examination of both on-chain and off-chain scalability options, such as sharding, adaptive block sizing, rollups, state channels, and sidechains. Layered protocols (Layer 1 and Layer 2) are also discussed in terms of scalability and interoperability. By combining on-chain, off-chain, and modular blockchain designs, this work investigates new hybrid models, multi-layer architectures, and cross-chain scalability mechanisms in addition to conventional solutions. By identifying critical gaps and challenges in existing approaches, this survey provides a roadmap for future research in scalable, secure, and interoperable blockchain infrastructures, with applications in decentralized finance, IoT, and cross-border transactions.
The significance of privacy and security in online social networks is to protect sensitive information, prevent unauthorized access, and build trust among users while ensuring a secure online environment. This paper proposes a friendship matching system based on the Parlier encryption algorithm, aiming at addressing existing privacy breaches and data misuse issues on current friendship platforms, significantly improving matching accuracy. First, filter the data items that users care about more in the database so as to make the appropriate friends. Then, according to the homomorphic encryption of paillier algorithm, in the case of ciphertext, the weight of each data item is calculated to the selected users. Finally, the Euclidean distance is used to calculate the similarity between users and select the appropriate dating users. Experimental results demonstrate that the proposed system exhibits strong performance and feasibility in preserving user privacy and data security.
