Computer
Safa S. Abdul-Jabbar; Alaa K. Farhan; Rana F. Ghani
Abstract
Blockchain technology relies on cryptographic techniques that provide various advantages, such as trustworthiness, collaboration, organization, identification, integrity, and transparency. Meanwhile, data analytics refers to the process of utilizing techniques to analyze big data and comprehend the relationships ...
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Blockchain technology relies on cryptographic techniques that provide various advantages, such as trustworthiness, collaboration, organization, identification, integrity, and transparency. Meanwhile, data analytics refers to the process of utilizing techniques to analyze big data and comprehend the relationships between data points to draw meaningful conclusions. The field of data analytics in Blockchain is relatively new, and few studies have been conducted to examine the challenges involved in Blockchain data analytics. This article presents a systematic analysis of how data analytics affects Blockchain performance, with the aim of investigating the current state of Blockchain-based data analytics techniques in research fields and exploring how specific features of this new technology may transform traditional business methods. The primary objectives of this study are to summarize the significant Blockchain techniques used thus far, identify current challenges and barriers in this field, determine the limitations of each paper that could be used for future development, and assess the extent to which Blockchain and data analytics have been effectively used to evaluate performance objectively. Moreover, we aim to identify potential future research paths and suggest new criteria in this burgeoning discipline through our review.
Computer
Emad M. Alsaedi; Alaa Kadhim Farhan
Abstract
Content-Based Image Retrieval (CBIR) is an actual application in computer vision, which retrieves similar images from a database. Deep Learning (DL) is essential in many applications, including image retrieval applications. However, encryption techniques are used to protect data privacy because these ...
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Content-Based Image Retrieval (CBIR) is an actual application in computer vision, which retrieves similar images from a database. Deep Learning (DL) is essential in many applications, including image retrieval applications. However, encryption techniques are used to protect data privacy because these data are vulnerable to being viewed by unauthorized parties while being transmitted over unsecured channels. This paper includes two parts for images retrieval. In the first part, features of all images of a Canadian Institute for Advanced Research CIFAR-10 dataset were extracted and stored on the Server-side. In the second part, the Brakerski/Fan-Vercauteren (BFV) homomorphic encryption scheme method for encrypting an image sent by the client-side. First, their decryption and image features are extracted depending on the trainer model when they arrive on the server-side. Then an extracted features are compared with stored features using the Cosine Distance method, and then the server encrypts the retrieved images and sends them to the client-side. Deep-learning results on plain images were 97% for classification and 96.7% for retriever images. At the same time, TheNational Institute of Standards and Technology (NIST ) test was used to check the security of BFV when applied to CIFAR-10 dataset.
Computer
Sameeh Abdulghafour Jassim; Alaa K. Farhan
Abstract
Recently, the Internet of Things (IoT) is begin used in many fields such as smart homes, healthcare systems, industrial applications, etc. Therefore, the use of the IoT led to a growth in the number of dangers especially in the areas of privacy and security for applications running on low- resource computers. ...
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Recently, the Internet of Things (IoT) is begin used in many fields such as smart homes, healthcare systems, industrial applications, etc. Therefore, the use of the IoT led to a growth in the number of dangers especially in the areas of privacy and security for applications running on low- resource computers. Consequently, the demand for lightweight encryption methods is growing. To safeguard sensing data, this study introduces a Lightweight Advanced Encryption Standard (LAES) depending on dynamic ShiftRows, initial permutation instead of MixColumns, and a dynamic number of rounds. It was created with the goal of reducing encryption/decryption time. The proposed approach was assessed by using various measurements such as lengths of the key used was 2128 and it is quite enough for security, key sensitivity values were 100%, Also, this study compared the encryption/decryption time, NIST statistical test, and security strength of the proposed architecture to those of XTEA, SIMON, Skinny, SPECK, and PRESENT. The encryption/decryption time of the proposed approach was had the shortest period (0.0169 S) while the SPECK algorithm was had the longest period (4.1249 S) among the comparative algorithms. Whereas, NIST statistical test values of the proposed approach were passed successfully and had higher values than the comparative algorithms. Moreover, the proposed approach utilized 1280, 1024, and 768 GE with 6, 8, or 10 rounds respectively. The average number of GE was approximately 1000 GE. These numbers of GE are considered highly efficient with the IoT environment.