Multi-level Data Processing Research Articles

ESMAC: Efficient and Secure Multi-Owner Access Control With TEE in Multi-Level Data Processing

Traditional data access control schemes only prevent unauthorized access to private data with a single owner. They are not suitable for application in a Multi-Level Data Processing (MLDP) scenario, where data are processed by a series of parties who also insert new data. Hence, the accumulated dataset should be protected through access control handled by hierarchically-structured parties who are at least partial data owners in MLDP. Existing multi-owner access control schemes mainly focus on controlling access to co-owned data of multiple entities with the equal ownership, but seldom investigates how to apply access control in MLDP. In this paper, we base the off-the-shelf Trusted Execution Environment (TEE), Intel SGX, to propose an Efficient and Secure Multi-owner Access Control scheme (ESMAC) for access authorization in MLDP. Moreover, to prevent unauthorized data disclosure by non-root data owners aiming to gain extra profits, we further introduce undercover polices to supervise their behaviors. Specifically, we design a data protection scheme based on game theory to decide the payoffs and punishments of honest and dishonest data owners, which motivates data owners to behave honestly when claiming ownership over data. Through comprehensive security analysis and performance evaluation, we demonstrate ESMAC's security and effectiveness.

Multilevel Data Processing Using Parallel Algorithms for Analyzing Big Data in High-Performance Computing

The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where High-Performance Computing solution has become a key issue and has attracted attention in recent years. However, these systems are either memoryless or computational inefficient. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that enhances the working of traditional MapReduce by incorporating parallel processing algorithm. Moreover, complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed parallel processing algorithm. The proposed system architecture both read and writes operations that enhance the efficiency of the Input/Output operation. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.