scholarly journals A Novel Method to Solve Real Time Security Issues in Software Industry Using Advanced Cryptographic Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
B. Gobinathan ◽  
M. A. Mukunthan ◽  
S. Surendran ◽  
K. Somasundaram ◽  
Syed Abdul Moeed ◽  
...  
Keyword(s):  
Data Privacy ◽  
Privacy Preservation ◽  
Smart Manufacturing ◽  
Trade Off ◽  
Security Issues ◽  
Novel Technique ◽  
Proposed Model ◽  
Privacy Model ◽  
Novel Method ◽  
Cryptographic Techniques

In recent times, the utility and privacy are trade-off factors with the performance of one factor tends to sacrifice the other. Therefore, the dataset cannot be published without privacy. It is henceforth crucial to maintain an equilibrium between the utility and privacy of data. In this paper, a novel technique on trade-off between the utility and privacy is developed, where the former is developed with a metaheuristic algorithm and the latter is developed using a cryptographic model. The utility is carried out with the process of clustering, and the privacy model encrypts and decrypts the model. At first, the input datasets are clustered, and after clustering, the privacy of data is maintained. The simulation is conducted on the manufacturing datasets over various existing models. The results show that the proposed model shows improved clustering accuracy and data privacy than the existing models. The evaluation with the proposed model shows a trade-off privacy preservation and utility clustering in smart manufacturing datasets.

scholarly journals Upgrade-Data Security in Cloud by Machine Learning and Cryptography Techniques

2019 ◽  
Vol 8 (6) ◽  
pp. 2728-2732
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Data Privacy ◽  
Business Models ◽  
Distributed Resources ◽  
Security Issues ◽  
New Business ◽  
Cryptographic Techniques ◽  
Privacy And Anonymity ◽  
Computer Resources

The term cloud computing is referred as the shared pool of customizable computer resources and high quantity services which can easily be provisioned with less management endeavours via internet. It transfigured the mode associations reach IT, which enables them to be more perceptive, launch new business models, and minimise the IT costs. These technologies are to be administrated in an interdisciplinary collection of architectures, characterized into various deployment and service models, and can synchronize with other related technologies. The widespread issues with cloud computing are security, reliability, data privacy and anonymity. Cloud computing provides a way to share distributed sources and services that are owned by different organizations or sites. Since it shares distributed resources via network in open environment that results in security issues. In this paper, our aim is to upgrade the security of data in the cloud and also to annihilate the difficulties related to the data security with encipher algorithm. In our proposed plan, some key services of security like authentication and cryptographic techniques are assigned in cloud computing environment.

scholarly journals Query Computation Time and Its Performance over Distributed Database Frameworks

2019 ◽  
Vol 8 (12) ◽  
pp. 5153-5160
Keyword(s):  
Data Privacy ◽  
Privacy Preservation ◽  
Clustering Algorithms ◽  
Distributed Databases ◽  
Computation Time ◽  
Distributed Database ◽  
Vital Role ◽  
Data Set ◽  
Security Issues ◽  
Distributed Database System

It is essential to maintain a relevant methodology for data fragmentation to employ resources, and thus, it needs to choose an accurate and efficient fragmentation methodology to improve authority of distributed database system. This leads the challenges on data reliability, stable storage space and costs, Communication costs, and security issues. In Distributed database framework, query computation and data privacy plays a vital role over portioned distributed databases such as vertical, horizontal and hybrid models, Privacy of any information is regarded as the essential issue in nowadays hence we show an approach by that we can use privacy preservation over the two parties which are actually distributing their data horizontally or vertically. In this chapter, I present an approach by which the concept of hierarchal clustering applied over the horizontally partitioned data set. We also explain the desired algorithm like hierarchal clustering, algorithms for finding the minimum closest cluster. Furthermore, it explores the performance of Query Computation over portioned databases with the analysis of Efficiency and Privacy.

Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Experimental Result ◽  
Map Reduce ◽  
Distributed Environment ◽  
Top Down ◽  
Two Phase ◽  
Data Anonymization ◽  
Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

A novel technique for mapping biopsy of bile duct cancer

Endoscopy ◽  
2020 ◽  
Author(s):  
Hirokazu Okada ◽  
Norimitsu Uza ◽  
Tomoaki Matsumori ◽  
Shimpei Matsumoto ◽  
Yuya Muramoto ◽  
...  
Keyword(s):  
Bile Duct ◽  
Intrahepatic Bile Duct ◽  
Preoperative Assessment ◽  
Median Number ◽  
Novel Technique ◽  
Minimal Trauma ◽  
Adequate Sampling ◽  
Novel Method ◽  
Longitudinal Extension ◽  
Mapping Biopsy

Abstract Background Accurate preoperative assessment of the longitudinal extension of perihilar cholangiocarcinoma (PHCC) is essential for treatment planning. Mapping biopsies for PHCC remain challenging owing to technical difficulties and insufficient sample amounts. The aim of this study was to investigate the usefulness of a novel technique for mapping biopsies of PHCC. Methods Our novel method focused on a biliary stent delivery system for mapping biopsies. Fifty patients with PHCC undergoing endoscopic transpapillary mapping biopsy using the novel method were reviewed from August 2015 to June 2019. Results The median number of biopsy samples was six (range 1 – 17), and the rate of adequate sampling was 91.4 % (266 /291). Biopsy from the intrahepatic bile duct was possible in 82.0 % of patients (41 /50), and negative margins were confirmed in the resected specimens from 34 /39 patients who underwent surgery (87.2 %). None of the patients had post-endoscopic retrograde cholangiopancreatography pancreatitis. Conclusions With our novel method, accurate assessment of the longitudinal extension of PHCC might be expected with minimal trauma to the duodenal papilla.

scholarly journals A Model for Working Environment Monitoring in Smart Manufacturing

2021 ◽  
Vol 11 (6) ◽  
pp. 2850
Author(s):  
Dalibor Dobrilovic ◽  
Vladimir Brtka ◽  
Zeljko Stojanov ◽  
Gordana Jotanovic ◽  
Dragan Perakovic ◽  
...  
Keyword(s):  
Open Source ◽  
Student Teaching ◽  
Manufacturing Systems ◽  
Working Environment ◽  
Smart Manufacturing ◽  
Environment Monitoring ◽  
Application Development ◽  
Testing Platform ◽  
Proposed Model ◽  
Smart Manufacturing Systems

The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.

scholarly journals A Secure Ciphertext Retrieval Scheme against Insider KGAs for Mobile Devices in Cloud Storage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Run Xie ◽  
Chanlian He ◽  
Dongqing Xie ◽  
Chongzhi Gao ◽  
Xiaojun Zhang
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Privacy ◽  
Security Analysis ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Security Issues ◽  
Efficiency Comparison ◽  
Cloud Servers

With the advent of cloud computing, data privacy has become one of critical security issues and attracted much attention as more and more mobile devices are relying on the services in cloud. To protect data privacy, users usually encrypt their sensitive data before uploading to cloud servers, which renders the data utilization to be difficult. The ciphertext retrieval is able to realize utilization over encrypted data and searchable public key encryption is an effective way in the construction of encrypted data retrieval. However, the previous related works have not paid much attention to the design of ciphertext retrieval schemes that are secure against inside keyword-guessing attacks (KGAs). In this paper, we first construct a new architecture to resist inside KGAs. Moreover we present an efficient ciphertext retrieval instance with a designated tester (dCRKS) based on the architecture. This instance is secure under the inside KGAs. Finally, security analysis and efficiency comparison show that the proposal is effective for the retrieval of encrypted data in cloud computing.

scholarly journals A Proposed Heuristic Optimization Algorithm for Detecting Network Attacks

2019 ◽  
Vol 2 (4) ◽  
pp. 530
Author(s):  
Amr Hassan Yassin ◽  
Hany Hamdy Hussien
Keyword(s):  
Neural Network ◽  
Heuristic Algorithms ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Network Attacks ◽  
Security Issues ◽  
Proposed Model ◽  
Network Intrusions ◽  
Attack Patterns ◽  
Selection Of

Due to the exponential growth of E-Business and computing capabilities over the web for a pay-for-use groundwork, the risk factors regarding security issues also increase rapidly. As the usage increases, it becomes very difficult to identify malicious attacks since the attack patterns change. Therefore, host machines in the network must continually be monitored for intrusions since they are the final endpoint of any network. The purpose of this work is to introduce a generalized neural network model that has the ability to detect network intrusions. Two recent heuristic algorithms inspired by the behavior of natural phenomena, namely, the particle swarm optimization (PSO) and gravitational search (GSA) algorithms are introduced. These algorithms are combined together to train a feed forward neural network (FNN) for the purpose of utilizing the effectiveness of these algorithms to reduce the problems of getting stuck in local minima and the time-consuming convergence rate. Dimension reduction focuses on using information obtained from NSL-KDD Cup 99 data set for the selection of some features to discover the type of attacks. Detecting the network attacks and the performance of the proposed model are evaluated under different patterns of network data.

scholarly journals A New Secure Model for Data Protection over Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Amr M. Sauber ◽  
Passent M. El-Kafrawy ◽  
Amr F. Shawish ◽  
Mohamed A. Amin ◽  
Ismail M. Hagag
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Protection ◽  
Cloud Services ◽  
Security And Privacy ◽  
Storage Model ◽  
Cloud Data ◽  
Security Issues ◽  
Data Owner ◽  
Proposed Model

The main goal of any data storage model on the cloud is accessing data in an easy way without risking its security. A security consideration is a major aspect in any cloud data storage model to provide safety and efficiency. In this paper, we propose a secure data protection model over the cloud. The proposed model presents a solution to some security issues of cloud such as data protection from any violations and protection from a fake authorized identity user, which adversely affects the security of the cloud. This paper includes multiple issues and challenges with cloud computing that impairs security and privacy of data. It presents the threats and attacks that affect data residing in the cloud. Our proposed model provides the benefits and effectiveness of security in cloud computing such as enhancement of the encryption of data in the cloud. It provides security and scalability of data sharing for users on the cloud computing. Our model achieves the security functions over cloud computing such as identification and authentication, authorization, and encryption. Also, this model protects the system from any fake data owner who enters malicious information that may destroy the main goal of cloud services. We develop the one-time password (OTP) as a logging technique and uploading technique to protect users and data owners from any fake unauthorized access to the cloud. We implement our model using a simulation of the model called Next Generation Secure Cloud Server (NG-Cloud). These results increase the security protection techniques for end user and data owner from fake user and fake data owner in the cloud.

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