Abstract: Many potential and valuable rules can be derived from data via clustering mining in an effective and accurate way, which can lead to security threats such as the disclosure of user privacy. Many privacypreserving researches on clustering mining have been conducted, especially on multiplicative perturbation (MP) that is a highly secure and accurate method. Research finds known knowledge independent component analysis (KKICA) can greatly reduce the privacy security of existing MP. It can approximately estimate private data from MP data. To solve the problem, partial rotation perturbation (PRP) is proposed. The analysis of accuracy shows that PRP has zeroloss accuracy. The analysis of security proves that PRP can defend attack from the KKICA availably and is more secure. PRP is applied in clustering mining. The results are very similar to unpreserved clustering mining results, which shows that MP is practicable. The existence of PRP solves the problem with the security vulnerability of existing MP effectively, making the application of clustering more secure.

Key words: clustering mining; privacypreserving; multiplicative perturbation; partial rotation perturbation