多维敏感属性隐私保护数据发布方法

Privacy-preserving data publishing method for dataset with multi-dimensional sensitive attributes

在匿名数据发布中,当敏感属性为多维时,攻击者有可能能够获取一维或几维敏感属性信息,并且结合准标识符信息对其他敏感属性进行推理攻击。针对此问题提出(Dou-l)-匿名模型,更好地保护了敏感信息。基于多维桶和分解思想,提出(Dou-l)-匿名算法,使得即便攻击者掌握了部分敏感数据,仍然能较好地保护其他敏感属性数据的隐私安全性。实际数据实验证明,算法可以较好地均衡发布数据的安全性和可用性。

When publishing data with multiple sensitive attributes, an adversary may be able to get some sensitive attribute information, attack other sensitive attribute information through a combination of this background knowledge with quasi-identifier information. To avoid this problem, a formal multiple sensitive attributes data publication model is defined, named （Dou-l）-anonymity. The corresponding （Dou-l）-anonymity implementation algorithm is proposed based on the idea of multi-sensitive bucketization and lossy join. The findings are verified by experiments with real data.