Cloud Security in Crypt Database Server Using Fine Grained Access Control

Information sharing in the cloud, powered by good patterns in cloud technology, is rising as a guaranteeing procedure for permitting users to advantageously access information. However, the growing number of enterprises and customers who stores their information in cloud servers is progressively challenging users’ privacy and the security of information. This paper concentrates on providing a dependable and secure cloud information sharing services that permits users dynamic access to their information. In order to achieve this, propose an effective, adaptable and flexible privacy preserving information policy with semantic security, by using Cipher text Policy Element Based Encryption (CP-EBE) consolidated with Character Based Encryption (CBE) systems. To ensure strong information sharing security, the policy succeeds in protecting the privacy of cloud users and supports efficient and secure dynamic operations, but not constrained to, file creation, user revocation. Security analysis demonstrates that the proposed policy is secure under the generic bi- linear group model in the random oracle model and enforces fine-grained access control, full collusion resistance and retrogressive secrecy. Furthermore, performance analysis and experimental results demonstrate that the overheads are as light as possible

Future Food Production Prediction Using AROA Based Hybrid Deep Learning Model in Agri-Sector

Abstract Policymaking and administration of national tactics of action for food security rely heavily on advances in models for accurate estimation of food output. In several fields, including food science and engineering, machine learning (ML) has been established to be an effective tool for data investigation and modelling. There has been a rise in recent years in the application of ML models to the tracking and forecasting of food safety. In our analysis, we focused on two sources of food production: livestock production and agricultural production. Livestock production was measured in terms of yield, number of animals, and sum of animals slaughtered; crop output was measured in terms of yields and losses. An innovative hybrid deep learning model is proposed in this paper by fusing a Dense Convolutional Network (DenseNet) with a Long Short-Term Memory (LSTM) to do production analysis. The hybridised algorithm, or A-ROA for short, combines the Arithmetic Optimisation Algorithm (AOA) and the Rider Optimisation Algorithm (ROA) to determine the ideal weight of the LSTM. The current investigation focuses on Iran as a case study. Therefore, we have collected FAOSTAT time series data on livestock and farming outputs in Iran from 1961 to 2017. Findings from this study can help policymakers plan for future generations' food safety and supply by providing a model to anticipate the upcoming food construction