Privacy Preserving High-Dimensional Data Mashup
K Megala · 2014
The goal of this project is protecting privacy of online users in social networks. Mashup is integrating different service providers to expertise and to deliver highly customizable services to their customers. Data mashup is an application that aims at integrating data from multiple data providers based on the users request. However, integrating data from multiple sources brings about three challenges: 1. Simply joining multiple private data sets together would reveal the sensitive information to the other data providers. 2. 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. 3. The mash up data from multiple sources often contains many data attributes. When enforcing a traditional 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. This paper resolves a privacy problem in a real-life mashup application for the online advertising industry in social networks, and proposes a service-oriented architecture along with a privacy-preserving data mashup algorithm to address the aforementioned challenges. Index Terms —Privacy protection, anonymity, data mashup, data integration, service-oriented architecture, high dimensionality .