scholarly journals A fully homomorphism encryption scheme based on LWR

2021 ◽  
Vol 2131 (2) ◽  
pp. 022104
Author(s):  
Qixin Zhang

Abstract We believe that isomorphic encryption technology can provide strong technical support for users’ privacy protection in a distributed computing environment. There are three types of quasi-homomorphism encryption methods: partial homomorphism encryption, shallow homomorphism encryption, and full homomorphism encryption. homomorphism encryption methods have important applications for ciphertext data computing in distributed computing environments, such as secure cloud computing, fee computing, and remote file storage ciphertext retrieval. It is pointed out that the construction of the homomorphism encryption method is still in the theoretical stage and cannot be used for real high-density data calculation problems. How to design (natural) isomorphic encryption schemes according to algebraic systems is still a challenging research. This question discusses the problem of Learning With Rounding (LWR). Based on the difficulty of LWR, multiple IDs, and attribute categories, a fully homomorphism encryption method corresponding to an ID is proposed. In this paper, in order to reflect the effectiveness of the proposed method, we propose a homomorphism encryption technology based on the password search attribute.

2019 ◽  
Author(s):  
A.G. Feoktistov ◽  
I.A. Sidorov ◽  
R.O. Kostromin ◽  
G.A. Oparin ◽  
O.Yu. Basharina

The paper addresses the relevant issue of ensuring the reliability of solving large scientific and applied problems in computing environments that integrate Grid and cloud computing. The main reliability parameter is the probability of successful problem-solving in a computing environment with the following specified quality criteria: efficiency of the allocated resources use, and time, deadline or cost of executing jobs. We propose a new technology for testing and evaluating the reliability of functioning of problem-oriented heterogeneous distributed computing environments. It integrates models, representing different layers of knowledge about the environments, and special tools that automate a study of these environments. Applying such technology provides an increase in the reliability and efficiency of heterogeneous distributed computing environments by parametric adjusting of local resources managers installed in the environment nodes. Their adjustment is implemented on the base of the results of testing and evaluating obtained with the use of complex (conceptual, simulation, and semi-natural) modeling and meta-monitoring of computational resources.


Author(s):  
Jahwan Koo ◽  
Nawab Muhammad Faseeh Qureshi ◽  
Isma Farah Siddiqui ◽  
Asad Abbas ◽  
Ali Kashif Bashir

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.


2013 ◽  
Vol 765-767 ◽  
pp. 1087-1091
Author(s):  
Hong Lin ◽  
Shou Gang Chen ◽  
Bao Hui Wang

Recently, with the development of Internet and the coming of new application modes, data storage has some new characters and new requirements. In this paper, a Distributed Computing Framework Mass Small File storage System (For short:Dnet FS) based on Windows Communication Foundation in .Net platform is presented, which is lightweight, good-expansibility, running in cheap hardware platform, supporting Large-scale concurrent access, and having certain fault-tolerance. The framework of this system is analyzed and the performance of this system is tested and compared. All of these prove this system meet requirements.


2015 ◽  
Vol 32 (5) ◽  
pp. 798-800 ◽  
Author(s):  
Brandon R. Thomas ◽  
Lily A. Chylek ◽  
Joshua Colvin ◽  
Suman Sirimulla ◽  
Andrew H.A. Clayton ◽  
...  

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