Burak Cem Kara^1,2,*, Can Eyüpoğlu¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1515-1535, 2023, DOI:10.32604/cmc.2023.040274

Abstract Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring data utility remains an important goal to achieve. Because finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research area. When existing approaches are investigated, one of the most significant difficulties discovered is the presence of outlier data in the datasets. Outlier data has a negative impact on data utility. Furthermore, k-anonymity algorithms, which are commonly used in the literature, do not provide adequate protection against outlier data. In this study, a new data anonymization algorithm… More >

Sur Singh Rawat^1,*, Youseef Alotaibi², Nitima Malsa¹, Vimal Gupta¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1797-1815, 2023, DOI:10.32604/cmc.2023.039381

Abstract Unmanned aerial vehicles (UAVs), or drones, have revolutionized a wide range of industries, including monitoring, agriculture, surveillance, and supply chain. However, their widespread use also poses significant challenges, such as public safety, privacy, and cybersecurity. Cyberattacks, targeting UAVs have become more frequent, which highlights the need for robust security solutions. Blockchain technology, the foundation of cryptocurrencies has the potential to address these challenges. This study suggests a platform that utilizes blockchain technology to manage drone operations securely and confidentially. By incorporating blockchain technology, the proposed method aims to increase the security and privacy of drone data. The suggested platform stores… More >

Ali Altalbe^1,2,*, Abdul Rehman Javed³

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2187-2200, 2023, DOI:10.32604/csse.2023.040624

Abstract Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence (AI) algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy. Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s. Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients. The healthcare industry faces two significant challenges: security and privacy issues and the personalization of cloud-trained AI models. This paper proposes a Deep Neural Network (DNN) based approach embedded in a federated… More >

Arfat Ahmad Khan¹, Shtwai Alsubai², Chitapong Wechtaisong^3,*, Ahmad Almadhor⁴, Natalia Kryvinska^5,*, Abdullah Al Hejaili⁶, Uzma Ghulam Mohammad⁷

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1733-1750, 2023, DOI:10.32604/csse.2023.039296

Abstract A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over time. Automatic cataract prediction based on various imaging technologies has been addressed recently, such as smartphone apps used for remote health monitoring and eye treatment. In recent years, advances in diagnosis, prediction, and clinical decision support using Artificial Intelligence (AI) in medicine and ophthalmology have been exponential. Due to privacy concerns, a lack of data makes applying artificial intelligence models in the medical field challenging. To address this issue, a federated learning framework named CD-FL based on a VGG16… More >

Ahmad Almadhor^1,*, Arfat Ahmad Khan², Chitapong Wechtaisong^3,*, Iqra Yousaf⁴, Natalia Kryvinska⁵, Usman Tariq⁶, Haithem Ben Chikha¹

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.039007

Abstract Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse, causing air to enter the pleural cavity, the area close to the lungs and chest wall. The most persistent disease, as well as one that necessitates particular patient care and the privacy of their health records. The radiologists find it challenging to diagnose pneumothorax due to the variations in images. Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems. However, it is challenging to employ it in the medical field due to privacy issues and a lack of data. To address this issue, a… More >

Shoujian Yu¹, Zhenchi Jie¹, Guowen Wu¹, Hong Zhang¹, Shigen Shen^2,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1729-1751, 2023, DOI:10.32604/iasc.2023.039911

Abstract In recent years, the type and quantity of news are growing rapidly, and it is not easy for users to find the news they are interested in the massive amount of news. A news recommendation system can score and predict the candidate news, and finally recommend the news with high scores to users. However, existing user models usually only consider users’ long-term interests and ignore users’ recent interests, which affects users’ usage experience. Therefore, this paper introduces gated recurrent unit (GRU) sequence network to capture users’ short-term interests and combines users’ short-term interests and long-term interests to characterize users. While… More >

Dong Chen¹, Tanping Zhou^1,2,3,*, Xu An Wang^1,2, Zichao Song¹, Yujie Ding¹, Xiaoyuan Yang^1,2

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2477-2497, 2023, DOI:10.32604/iasc.2023.039238

Abstract Smart grid (SG) brings convenience to users while facing great challenges in protecting personal private data. Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value, preventing the leakage of personal data while ensuring its availability. Recently, a flexible subset data aggregation (FSDA) scheme based on the Paillier homomorphic encryption was first proposed by Zhang et al. Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset. In this paper, firstly, an efficient attack with both theorems proving and experimentative verification is… More >

Sultan Mesfer Aldossary^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2199-2212, 2023, DOI:10.32604/iasc.2023.038243

Abstract The challenge of encrypting sensitive information of a medical image in a healthcare system is still one that requires a high level of computing complexity, despite the ongoing development of cryptography. After looking through the previous research, it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field. Recently, neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images. This revelation came about as a result of current developments. Nevertheless, such an implementation is a technique that is… More >

Mohammad S. Al-kahtani¹, Zahid Mehmood^2,3,*, Tariq Sadad⁴, Islam Zada⁵, Gauhar Ali⁶, Mohammed ElAffendi⁶

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2279-2290, 2023, DOI:10.32604/iasc.2023.037673

Abstract Cybersecurity threats are increasing rapidly as hackers use advanced techniques. As a result, cybersecurity has now a significant factor in protecting organizational limits. Intrusion detection systems (IDSs) are used in networks to flag serious issues during network management, including identifying malicious traffic, which is a challenge. It remains an open contest over how to learn features in IDS since current approaches use deep learning methods. Hybrid learning, which combines swarm intelligence and evolution, is gaining attention for further improvement against cyber threats. In this study, we employed a PSO-GA (fusion of particle swarm optimization (PSO) and genetic algorithm (GA)) for… More >

Chen Wang¹, Jian Xu^1,*, Jiarun Li¹, Yan Dong¹, Nitin Naik²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1421-1436, 2023, DOI:10.32604/iasc.2023.034123

Abstract Outsourcing the k-Nearest Neighbor (kNN) classifier to the cloud is useful, yet it will lead to serious privacy leakage due to sensitive outsourced data and models. In this paper, we design, implement and evaluate a new system employing an outsourced privacy-preserving kNN Classifier Model based on Multi-Key Homomorphic Encryption (kNNCM-MKHE). We firstly propose a security protocol based on Multi-key Brakerski-Gentry-Vaikuntanathan (BGV) for collaborative evaluation of the kNN classifier provided by multiple model owners. Analyze the operations of kNN and extract basic operations, such as addition, multiplication, and comparison. It supports the computation of encrypted data with different public keys. At… More >