Abstract: With the advancement of internet and data technologies, the information explosion can be seen everywhere in the industry. The huge corpus of statistical data are periodically generated, updated and maintained by various organizations. It has evolved not only as a speed of exponential growth of data but as an impressive tool to understand the deep insights of any business future planning and market strategy. Various web repositories are being employed to sustain this data for further findings of different business patterns which could be very important and private for a particular industry. This sensitive data when exposed to legitimate but untrustworthy owners may possess the grave risks to the owners privacy. Secure access of organization database during mining process has been a major challenge of the database designers which consequences in evolution the new research disciple privacy preserving data mining. It deals with preventing the confidential information of an individual or entity from inferring by the malicious data miners during the mining process. In this study, researchers have presented an object oriented modeling of the proposed system by applying inference control techniques based on unified modeling language. Different UML diagrams like, class, sequence and activity diagram have been designed in this research work for modeling proposed PPDM system. Its generalized UML model will act as a prototype for the software engineers to conceive and develop the protocol embeds in the complicated privacy preserving data mining systems. This object oriented UML modeling will ensure successful consummation of any large and complex PPDM software projects in both optimal time and cost efficient manner.
Anurag , Deepak Arora and Upendra Kumar, 2017. UML Modeling of Securing Sensitive Data by Inference Control Method. International Journal of Soft Computing, 12: 112-119.