Efficient Big Data-based Access Control Mechanism for IoT Cloud Environments

Background/Objectives: Recently, the data using in the internet had processed through the network every day, and cloud services related to IoT are increasing rapidly. In particular, the cloud service related to IoT has been transformed into an era in which data is generated and processed by an individual centered around the enterprise in the past. However, the use of mobile phones and IoT technology had diversified, and the demand for computational cost and accuracy has increased. Methods/Statistical analysis: we propose access control method based on big data that can process various attributes of data in block in IoT cloud environment. The proposed scheme aims to minimize the service latency of users by extracting the security parameters of each data by attribute and converting them into pairs with polynomials. In the performance evaluation, we had fined that the data processing time was 7.2% higher than previous scheme and the data processing rate was 9.7% higher than previous scheme. The accuracy according to the type and size of the different data improved by 18.1%. IoT cloud server and user communication delay was 8.5% higher than previous scheme. Finally, the server overhead reduced by 5.8%. Findings: We propose a method that can access verified data without delaying the data by constructing data into subnets and then applying the security parameter of the data constituting each subnet to -bit and applying it to the polynomial coefficients. Improvements/Applications: In future research, proposed scheme can be applied to various services related to large-scale data access in the cloud environment.