Data privacy is a major concern in data mining. Privacy-preserving data mining algorithms have been used for preserving privacy in data mining. However, privacy-preserving data mining on high dimensional continuous data leads to high data loss, information loss and identifying clusters are very difficult. In this paper, a novel technique Anonymized Noise Addition in Subspaces (ANAS) is proposed, which reduces data loss, information loss and enhances identification of clusters and privacy. Anonymization using aggregation is performed in dense and non-dense subspaces considering Euclidean distances to reduce data loss and enhance privacy. Random noise within the subspace limits is then applied to anonymized subspaces to enhance identification of clusters and reduce data loss. ANAS is run on benchmark datasets, and results show that ANAS can identify 80% of the original dataset clusters on sparse datasets, whereas the existing techniques do not identify any clusters. ANAS reduces data loss by 50%, information loss by 20% and enhances privacy by 40%.

数据隐私是数据挖掘中的主要问题。隐私保护数据挖掘算法已用于保护数据挖掘中的隐私。但是，在高维连续数据上进行隐私保护的数据挖掘会导致高数据丢失，信息丢失和识别群集非常困难。本文提出了一种新的子空间匿名噪声加法（ANAS）技术，可以减少数据丢失，信息丢失并增强对集群和隐私的识别。考虑到欧几里得距离以减少数据丢失并增强隐私性，在密集和非密集子空间中执行使用聚合的匿名化。然后将子空间限制内的随机噪声应用于匿名子空间，以增强对群集的标识并减少数据丢失。ANAS在基准数据集上运行，结果表明，ANAS可以识别稀疏数据集上80％的原始数据集聚类，而现有技术无法识别任何聚类。ANAS可以将数据丢失减少50％，将信息丢失减少20％，并将隐私提高40％。