Privacy-Preserving Statistical Analysis Method for Real-World Data

Abstract

We propose a method for obtaining statistical results such as averages, variances, and correlations without leaking any raw data values from data-holders by using multiple pseudonyms. At present, to obtain statistical results using a large amount of data, we need to collect all data in the same storage device. However, gathering real-world data that was generated by different people is not easy because they often contain private information. Thus, our method solves the problem and protects data-holders from data-user's malicious attacks. Finally, we evaluate the suitability of our method through implementation and experimentation.