Theseus Data Synthesis Approach: A Privacy-Preserving Online Data Sharing Service

Abstract

With the vigorously developed services of cloud computing, it is relatively easier and more convenient for organizations or enterprises to open data on clouds. However, as personal information in electronic data becomes more massive and detailed, how to balance data opening and personal privacy has become a critical issue. In this paper, we propose the Theseus Data Synthesis Approach (TDSA), which generates synthetic data by replacing partial records until no record from the original dataset remains. Unlike other data anonymization works such as k-anonymity and differential privacy, which encountered limitations and challenges when applying to real-world scenarios. In our work, Since there are no real data, personal privacy is definitely preserved. We also analyze the quality and utility of the synthetic dataset and make comparisons with related works. We conclude that with our scheme, opening useful data on clouds and preserving personal privacy can be simultaneously achieved.

Keywords

• Data anonymization
• data synthesis
• privacy-preserving data sharing