Privacy-Preserving Data Mashup
Authors
- Noman Mohammed (Concordia University, Canada)
- Benjamin C. M. Fung (Concordia University, Canada)
- Ke Wang (Simon Fraser University, Canada)
- Patrick C. K. Hung (University of Ontario Institute of Technology, Canada)
Abstract
Mashup is a web technology that combines information from more than one source into a single web application. This technique provides a new platform for different data providers to flexibly integrate their expertise and deliver highly customizable services to their customers. Nonetheless, combining data from different sources could potentially reveal person-specific sensitive information. In this paper, we study and resolve a real-life privacy problem in a data mashup application for the financial industry in Sweden, and propose a privacy-preserving data mashup (PPMashup) algorithm to securely integrate private data from different data providers, whereas the integrated data still retains the essential information for supporting general data exploration or a specific data mining task, such as classification analysis. Experiments on real-life data suggest that our proposed method is effective for simultaneously preserving both privacy and information usefulness, and is scalable for handling large volume of data.
Session
EDBT Research Session 7: Privacy & Security (Tuesday, March 24, 16:00—17:30)