Valid Blockchain-Based E-Voting Using Elliptic Curve and Homomorphic Encryption

Salman, Saba Abdulbaqi; Al-Janabi, Sufyan; Sagheer, Ali Makki

doi:10.3991/ijim.v16i20.33173

Cited by 6 publications

(4 citation statements)

References 18 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, complex implementation, cost, and data overload may pose challenges. Organizations should weigh the benefits and drawbacks before implementing a business intelligence system for cyber security [44].…”

Section: B Discussionmentioning

confidence: 99%

A Survey on Business Intelligence Approach Based on Deep Learning

2023

IJCCCE

View full text Add to dashboard Cite

The article provides an overview of two recent developments in technology: Business Intelligence (BI) and Deep Learning (DL). In order to support decision-making processes, BI entails gathering, integrating, and analyzing data from various sources, while DL uses artificial neural networks to learn and generate predictions from complicated datasets. This paper introduces the concepts and principles and highlights recent developments and applications in different domains of research: education, organizations, stock market, forecasting, decision-making in real-time, and security. However, the fundamental problem with the business intelligence approach is that there is no learning involved. Other limitations and challenges include the capacity that affects the data analysis process, the variety of data in results, and the need for a complete presentation of results in the form of dashboards, scorecards, reports, and portals. The approach choice hinges on the problem's context and requirements and the nature and characteristics of the data. Although BI and DL are widespread, alternative methods may suit well too, such as machine learning, data mining, and statistical analysis. Justifying the selection based on precise needs and goals is crucial. Recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent units (GRU), and Business intelligence tools are used in the research problem to address these limitations and explore the potential advantages and difficulties of integrating BI and DL to achieve an advantage in a given sector. Index Terms— Business Intelligence Tools; decision making; deep learning algorithms.

show abstract

Section: B Discussionmentioning

confidence: 99%

A Survey on Business Intelligence Approach Based on Deep Learning

2023

IJCCCE

View full text Add to dashboard Cite

show abstract

“…Homomorphic Encryption (HE) is essential for enabling private inference. HE is a cryptographic technique that enables computations to be executed on encrypted data, producing an encrypted output corresponding to the result of operations performed on unencrypted data [11], [12]. This attribute facilitates the execution of computations on encrypted data by multiple entities without necessitating access to the unprocessed, unencrypted data.…”

Section: Legal Obligationsmentioning

confidence: 99%

Secure Data Computation Using Deep Learning and Homomorphic Encryption: A Survey

Al-Janabi

Al‐Khateeb

2023

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Deep learning and its variant techniques have surpassed classical machine algorithms due to their high performance gaining remarkable results and are used in a broad range of applications. However, adopting deep learning models over the cloud introduces privacy and security issues for data owners and model owners, including computational inefficiency, expansion in ciphertext, error accumulation, security and usability trade-offs, and deep learning model attacks. With homomorphic encryption, computations on encrypted data can be performed without disclosing its content. This research examines the basic concepts of homomorphic encryption limitations, benefits, weaknesses, possible applications, and development tools concentrating on neural networks. Additionally, we looked at systems that integrate neural networks with homomorphic encryption in order to maintain privacy. Furthermore, we classify modifications made on neural network models and architectures that make them computable via homomorphic encryption and the effect of these changes on performance. This paper introduces a thorough review focusing on the privacy of homomorphic cryptosystems targeting neural network models and identifies existing solutions, analyzes potential weaknesses, and makes recommendations for further research.

show abstract

“…Those "parts" may be corner and spatiotemporal interest point trajectories or flow vectors. Even though part-based techniques are promising [11], they still struggle with erroneous background clutter and motion-induced background detection and tracking of important sections, which precludes a clear and useful representation [12].…”

Section: Related Workmentioning

confidence: 99%

Localization of Strangeness for Real Time Video in Crowd Activity Using Optical Flow and Entropy

Abid Hussan Altalbi,

Hameed Shaker,

Ezaldeen Ali

2023

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Anomaly detection, which is also referred to as novelty detection or outlier detection, is process of identifying unusual occurrences, observations, or events which considerably differ from the bulk of data and do not fit a predetermined definition of typical behavior. Medicine, cybersecurity, statistics, machine vision, law enforcement, neurology, and financial fraud are just a handful of the industries where anomaly detection is used. In the presented study, an online tool is utilized to identify crowd distortions, which could be brought on by panic. An activity map is produced with the use of numerous frames to show the continuity regarding the flow over time following the global optical flow has been calculated in the quickest time and with the highest precision possible utilizing the Farneback approach to calculate the magnitudes. Utilizing a specific threshold, the oddity in the video will be picked up by the activity map's generation of an entropy. The results indicate that the maximum entropy level for indoor video is <0.16 and the maximum entropy level for outdoor video is >0.45. A threshold of 0.04 is used to determine whether a frame is abnormal or normal.

show abstract

Valid Blockchain-Based E-Voting Using Elliptic Curve and Homomorphic Encryption

Cited by 6 publications

References 18 publications

A Survey on Business Intelligence Approach Based on Deep Learning

A Survey on Business Intelligence Approach Based on Deep Learning

Secure Data Computation Using Deep Learning and Homomorphic Encryption: A Survey

Localization of Strangeness for Real Time Video in Crowd Activity Using Optical Flow and Entropy

Contact Info

Product

Resources

About