International Journal of Emerging Science and Engineering (IJESE)
Mash up is integrating different service providers to expertise and to deliver highly customizable services to their customers. Simply joining multiple private data sets together would reveal the sensitive information to the other data providers. The integrated (mash up) data could potentially sharpen the identification of persons and therefore, expose their person-specific sensitive information that was not available before the mash up. The mash up data from multiple sources often contains many data attributes. When enforcing an established privacy model such as k-anonymity, the high-dimensional data would assist from the problem known as the curse of high dimensionality, resulting in ineffective data for further data analysis.