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SIBW:基于集群情报的网络流水标识方法,用于在数字医疗保健系统中检测隐私泄漏.
IEEE journal of biomedical and health informatics
|March 4, 2025
概括
这项研究引入了一种Swarm智能网络水标 (SIBW) 方法,用于实时检测医疗数据泄露. 在复杂的数字医疗保健系统中,SIBW提供了强大而高效的隐私保护.
科学领域:
- 数字健康数字健康
- 网络安全 网络安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 数字医疗保健系统面临着越来越多的数据保护挑战,原因是敏感患者信息的指数增长.
- 现有的隐私技术往往不适合动态,高损失的网络环境,阻碍了有效的医疗数据泄露检测.
- 频繁的医疗数据泄露损害了数字医疗保健系统的安全性和可靠性.
研究的目的:
- 为实时隐私数据泄露检测提出一种新的Swarm智能网络水印 (SIBW) 方法.
- 增强数字医疗保健网络中的隐私保护的稳定性和实时能力.
主要方法:
- 将喷泉代码与外部错误纠正代码集成在一起,以实现可靠的水印.
- 使用多相协同群集优化算法 (MPSSOA) 进行动态编码参数优化.
- 实现可靠的同步序列和轻量化嵌入机制,以实现网络适应性.
主要成果:
- 在高延迟动和数据包丢失条件下,SIBW的检测准确度超过90%.
- 与现有技术相比,该方法显示出更高的稳定性和效率.
- SIBW具有紧的3.7 MB设计,适合资源有限的系统.
结论:
- 拟议的SIBW方法为数字医疗保健中的实时隐私数据泄露检测提供了强大而高效的解决方案.
- 由于SIBW的适应性和性能,它非常适合复杂和动态的网络环境.
- 紧的设计可在各种数字医疗保健环境中快速部署.
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