Energy Consumption Cost Analysis of Mobile Data Encryption and Decryption

2016 ◽  
Author(s):  
Joseph C. Pry ◽  
Richard K. Lomotey
Keyword(s):  
Energy Consumption ◽  
Cost Analysis ◽  
Data Encryption ◽  
Mobile Data ◽  
Encryption And Decryption ◽  
Consumption Cost

scholarly journals A proposal of privacy preserving reinforcement learning for secure multiparty computation

2017 ◽  
Vol 6 (2) ◽  
pp. 57 ◽  
Author(s):  
Hirofumi Miyajima ◽  
Noritaka Shigei ◽  
Syunki Makino ◽  
Hiromi Miyajima ◽  
Yohtaro Miyanishi ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Distributed Processing ◽  
Data Encryption ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Learning Methods ◽  
Q Learning ◽  
Encryption And Decryption ◽  
Maze Problem ◽  
Learning Data

Many studies have been done with the security of cloud computing. Though data encryption is a typical approach, high computing complexity for encryption and decryption of data is needed. Therefore, safe system for distributed processing with secure data attracts attention, and a lot of studies have been done. Secure multiparty computation (SMC) is one of these methods. Specifically, two learning methods for machine learning (ML) with SMC are known. One is to divide learning data into several subsets and perform learning. The other is to divide each item of learning data and perform learning. So far, most of works for ML with SMC are ones with supervised and unsupervised learning such as BP and K-means methods. It seems that there does not exist any studies for reinforcement learning (RL) with SMC. This paper proposes learning methods with SMC for Q-learning which is one of typical methods for RL. The effectiveness of proposed methods is shown by numerical simulation for the maze problem.

Application of the Tabu Search Algorithm on Solving the Optimum Heating Institution of Reheating Furnace

2011 ◽  
Vol 130-134 ◽  
pp. 1725-1729
Author(s):  
Ye Jian Yang ◽  
Ze Yi Jiang ◽  
Xin Xin Zhang ◽  
Peng Jin
Keyword(s):  
Mathematical Model ◽  
Energy Consumption ◽  
Tabu Search ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Reheating Furnace ◽  
Consumption Cost ◽  
Optimum Heat ◽  
Heating Model ◽  
Oxidation Loss

With the aim of minimizing the energy consumption cost and taking the billet heating quality as the constraint condition, a mathematical model was established based on the billet heating model to solve the optimum heat institution of the reheating furnace. The tabu search algorithm was employed to solve the model. The results of the industrial production data optimization showed that the proposed mathematical model and the solving method could fully meet the practical production demand of the reheating furnace. In addition to satisfying the heating quality requirements, the energy consumption is reduced and the oxidation loss is decreased. Compared with the original one, the heating institution obtained from the mentioned model and algorithm had a better performance on energy efficiency.

scholarly journals Least Cost Analysis of Energy Efficiency Upgrades for Ontario Using Trnsys

2021 ◽  
Author(s):  
Amir Fereidouni Kondri
Keyword(s):  
Life Cycle ◽  
Energy Consumption ◽  
Cost Analysis ◽  
Energy Efficient ◽  
Life Cycle Cost ◽  
Net Present Value ◽  
Hot Water ◽  
Life Cycle Costs ◽  
Residential Housing ◽  
Domestic Hot Water

This report presents the methodology for determining least cost energy efficient upgrade solutions in new residential housing using brute force sequential search (BFSS) method for integration into the reference house to reduce energy consumption while minimizing the net present value (NPV) of life cycle costs. The results showed that, based on the life cycle cost analysis of 30 years, the optimal upgrades resulted in the average of 19.25% (case 1), 31% (case 2a), and 21% (case 2b) reduction in annual energy consumption. Economic conditions affect the sequencing of the upgrades. In this respect the preferred upgrades to be performed in order are; domestic hot water heating, above grade wall insulation, cooling systems, ceiling insulation, floor insulation, heat recovery ventilator, basement slab insulation and below grade wall insulation. When the gas commodity pricing becomes high, the more energy efficient upgrades for domestic hot water (DHW) get selected at a cost premium.

Implementasi Algoritma Merkle-Hellman Knapsack dalam Penyandian Record Database

2020 ◽  
pp. 162-165
Author(s):  
Reni Rahmadani ◽  
Harvei Desmon Hutahaean ◽  
Ressy Dwitias Sari
Keyword(s):  
Data Security ◽  
Public Key Cryptography ◽  
Data Encryption ◽  
Key Generation ◽  
Software Developers ◽  
Data Owner ◽  
Encryption And Decryption ◽  
User Data ◽  
Np Complete ◽  
Knapsack Algorithm

A lot of data is misused without the data owner being aware of it. Software developers must ensure the security user data on their system. Due to the size of the market that houses data, the security of record databases must be of great concern. Cryptographic systems or data encryption can be used for data security. The Merkle-Hellman Knapsack algorithm is included in public-key cryptography because it uses different keys for the encryption and decryption processes. This algorithm belongs to the NP-complete algorithm which cannot be solved in polynomial order time. This algorithm has stages of key generation, encryption, and decryption. The results of this study secure database records from theft by storing records in the form of ciphertext/password. Ciphertext generated by algorithmic encryption has a larger size than plaintext.

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