安全的隐私保护记录链接系统免受重新识别攻击
Sejong Lee1, Yushin Kim1, Yongseok Kwon1
1Department of Computer Science and Engineering, Major in Bio-Artificial Intelligence, Hanyang University, Ansan-si, Gyeonggi-do, South Korea.
本研究引入了一种零关系编码方案,以保护保护隐私的记录链接 (PPRL) 系统免受基于图形的重新识别攻击. 新方法显著降低了重新识别风险,增强了数据安全和隐私保护.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 保护隐私的记录链接 (PPRL) 对于跨数据集的数据分析至关重要.
- PPRL系统容易受到复杂的基于图形的重新识别攻击.
- 这种攻击带来了包括身份盗窃和金融欺诈在内的风险.
研究的目的:
- 提出一个新的零关系编码方案.
- 加强PPRL系统的安全性和隐私性,以防止重新识别威胁.
- 为了最大限度地减少源和编码记录之间的联系.
主要方法:
- 开发一个零关系编码方案.
- 基于模拟的验证使用泰坦尼克号和北卡罗来纳州选民记录 (NCVR) 数据集.
- 安全分析,以评估对基于图形的重新识别攻击的抵抗力.
主要成果:
- 在经过测试的数据集中显示了重新识别率的大幅降低.
- 验证了零关系编码方案的有效性.
- 确认了PPRL系统的隐私保护能力的改进.
结论:
- 拟议的零关系编码方案有效地加强了PPRL系统对抗基于图形的重新识别攻击.
- 这种方法在记录链接应用程序中显著提高了数据隐私和安全性.
- 该方法为安全的数据共享和分析提供了一个有前途的解决方案.
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