Centre for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.
Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.
Sci Rep. 2024 Jul 9;14(1):15763. doi: 10.1038/s41598-024-66603-y.
The timely identification of autism spectrum disorder (ASD) in children is imperative to prevent potential challenges as they grow. When sharing data related to autism for an accurate diagnosis, safeguarding its security and privacy is a paramount concern to fend off unauthorized access, modification, or theft during transmission. Researchers have devised diverse security and privacy models or frameworks, most of which often leverage proprietary algorithms or adapt existing ones to address data leakage. However, conventional anonymization methods, although effective in the sanitization process, proved inadequate for the restoration process. Furthermore, despite numerous scholarly contributions aimed at refining the restoration process, the accuracy of restoration remains notably deficient. Based on the problems identified above, this paper presents a novel approach to data restoration for sanitized sensitive autism datasets with improved performance. In the prior study, we constructed an optimal key for the sanitization process utilizing the proposed Enhanced Combined PSO-GWO framework. This key was implemented to conceal sensitive autism data in the database, thus avoiding information leakage. In this research, the same key was employed during the data restoration process to enhance the accuracy of the original data recovery. Therefore, the study enhanced the restoration process for ASD data's security and privacy by utilizing an optimal key produced via the Enhanced Combined PSO-GWO framework. When compared to existing meta-heuristic algorithms, the simulation results from the autism data restoration experiments demonstrated highly competitive accuracies with 99.90%, 99.60%, 99.50%, 99.25%, and 99.70%, respectively. Among the four types of datasets used, this method outperforms other existing methods on the 30-month autism children dataset, mostly.
及时识别儿童自闭症谱系障碍（ASD）对于防止其成长过程中的潜在挑战至关重要。在分享与自闭症相关的数据以进行准确诊断时，保护数据的安全性和隐私性是首要关注的问题，以防止在传输过程中未经授权的访问、修改或盗窃。研究人员已经设计了各种安全和隐私模型或框架，其中大多数通常利用专有算法或适应现有的算法来解决数据泄露问题。然而，传统的匿名化方法虽然在净化过程中有效，但在恢复过程中证明是不够的。此外，尽管有许多旨在改进恢复过程的学术贡献，但恢复的准确性仍然明显不足。基于上述问题，本文提出了一种新的方法来恢复经过净化的敏感自闭症数据集，以提高性能。在之前的研究中，我们利用提出的增强型组合 PSO-GWO 框架为净化过程构建了最优密钥。该密钥用于在数据库中隐藏敏感的自闭症数据，从而避免信息泄露。在本研究中，在数据恢复过程中使用相同的密钥来提高原始数据恢复的准确性。因此，通过利用增强型组合 PSO-GWO 框架生成的最优密钥，本研究增强了 ASD 数据的安全性和隐私性的恢复过程。与现有的元启发式算法相比，自闭症数据恢复实验的模拟结果显示出具有竞争力的准确性，分别为 99.90%、99.60%、99.50%、99.25%和 99.70%。在所使用的四种类型的数据集，该方法在 30 个月的自闭症儿童数据集上的性能优于其他现有的方法。
