论文信息 - Securing Sensitive Data Information Through Multiplicative Perturbation Approach Based on UML Modeling

Securing Sensitive Data Information Through Multiplicative Perturbation Approach Based on UML Modeling

Mining mainly aims toward extracting hidden useful patterns from the large amount of data which is exploited by various organizations for their business planning. The organizations generally used to store sensitive data, during their mining processes, which they do not want to disclose either due to legal constraints or competition among themselves. Privacy preserving data mining is focused on hiding this sensitive information while still getting accurate mining results. This paper deals with UML modeling of multiplicative perturbation-based approach applied in privacy preserving data mining system. Multiplicative perturbation is the most popular technique nowadays, which mainly aimed to preserve multidimensional information while performing different mining operations. Authors have modeled the entire approach by using IBM Rational Software Architect tool. This standard model will facilitate researchers and organizations to design more specific and aligned business solutions in order to secure sensitive data by proposed UML models based on multiplicative perturbation approaches.

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