https://publikasi.dinus.ac.id/jcta/issue/feed 2026-05-31T00:00:00+00:00 JTCA Editorial editorial.jcta@dinus.id Open Journal Systems <div style="border: 3px #086338 Dashed; padding: 10px; background-color: #ffffff; text-align: left;"> <ol> <li><strong>Journal Title </strong>: Journal of Computing Theories and Applications</li> <li><strong>Online ISSN </strong>: <a href="https://portal.issn.org/resource/ISSN/3024-9104">3024-9104</a> </li> <li><strong>Frequency </strong>: Quarterly (February, May, August, and November) </li> <li><strong>DOI Prefix</strong>: 10.62411/jcta</li> <li><strong>Publisher </strong>: Universitas Dian Nuswantoro</li> </ol> </div> <div id="focusAndScope"> <p><strong data-start="133" data-end="190">Journal of Computing Theories and Applications (JCTA)</strong> is a peer-reviewed international journal that covers all aspects of foundations, theories, and practical applications in computer science. All accepted articles are published online, assigned a <strong data-start="527" data-end="547">DOI via Crossref</strong>, and made <strong data-start="558" data-end="593" data-is-only-node="">freely accessible (Open Access)</strong>. The journal follows a <strong>rapid peer-review</strong> process, with the first decision typically provided within two to four weeks. JCTA welcomes original research papers in, but not limited to:</p> <p>Artificial Intelligence<br />Big Data<br />Bioinformatics<br />Biometrics<br />Cloud Computing<br />Computer Graphics<br />Computer Vision<br />Cryptography<br />Data Mining<br />Fuzzy Systems<br />Game Technology<br />Image Processing<br />Information Security<br />Internet of Things<br />Intelligent Systems<br />Machine Learning<br />Mobile Computing<br />Multimedia Technology<br />Natural Language Processing<br />Network Security<br />Pattern Recognition<br />Quantum Informatics<br />Signal Processing<br />Soft Computing<br />Speech Processing</p> <p><br />Special emphasis is given to recent trends related to cutting-edge research within the domain.</p> </div> <div id="peerReviewProcess"> </div> <div id="sponsors"> <p> </p> </div> https://publikasi.dinus.ac.id/jcta/article/view/15508 2026-01-11T14:43:54+00:00 Tabitha Chukwudi Aghaunor tabitha.aghaunor@gmail.com Eferhire Valentine Ugbotu eferhire.ugbotu@gmail.com Emeke Ugboh ugboh1972@gmail.com Paul Avwerosuoghene Onoma kenbridge14@gmail.com Frances Uchechukwu Emordi emordi.frances@dou.edu.ng Arnold Adimabua Ojugo ojugo.arnold@fupre.edu.ng Victor Ochuko Geteloma geteloma.victor@fupre.edu.ng Rebecca Okeoghene Idama idama-ro@dsust.edu.ng Peace Oguguo Ezzeh peace.ezzeh@fcetasaba.edu.ng

<p>The proliferation of cloud infrastructures has intensified concerns regarding data security, integrity, identity and access management, and user privacy. Despite recent advances, existing solutions often lack comprehensive integration of privacy-preserving mechanisms, dynamic trust management, and cross-provider interoperability. This study proposes an AI-enabled, zero-trust, blockchain-fused identity management framework for secure, privacy-preserving multi-cloud environments. The framework integrates homomorphic encryption with differential privacy for aggregate-level protection and secure multi-party computation for collaborative data processing. The proposed system was validated in a simulated multi-cloud environment using CloudSim, Ethereum blockchain, and AWS EC2. Experimental results indicate homomorphic encryption latency of approximately 450ms per operation and statistically significant security improvements (t(128) = 12.47, p &lt; 0.001), privacy (t(95) = 8.93, p &lt; 0.001), and throughput (t(156) = 15.21, p &lt; 0.001). The framework achieved differential privacy with ε = 0.1 while retaining 99.2% data utility, and demonstrated a 34% improvement in processing speed over conventional differential privacy approaches. In addition, the implementation was observed to be 2.3× faster than BGV-based configurations, with 45% lower memory consumption than CKKS and a 67% reduction in ciphertext size relative to baseline implementations. From an operational perspective, the framework shows a 23% reduction in security management costs, a 31% improvement in resource utilization efficiency, and an 18% decrease in compliance audit expenses. The model further indicates a 27% reduction in total cost of ownership (TCO) compared with multi-vendor security solutions, a projected return on investment (ROI) within 14 months, and an 89% reduction in security incident response costs under the evaluated conditions.</p>

2026-02-24T00:00:00+00:00 Copyright (c) 2026 Tabitha Chukwudi Aghaunor, Eferhire Valentine Ugbotu, Emeke Ugboh, Paul Avwerosuoghene Onoma, Frances Uchechukwu Emordi, Arnold Adimabua Ojugo, Victor Ochuko Geteloma, Rebecca Okeoghene Idama, Peace Oguguo Ezzeh