Anonymizing Set-Valued Social Data

The increasing popularity of social networks has generated tremendous amount of data to be exploited for commercial, research and many other valuable applications. However, the release of these data has raised an issue that personal privacy may be breached. Current practices of simply removing all identifiable personal information (such as names and social security numbers) before releasing the data is insufficient. More effective anonymization techniques are required. In this work, we propose a k-anonymization-based technique on set-valued network node data. The proposed algorithm is based on the principle of minimizing the number of addition and deletion operations to achieve k-anonymity. Numerical experiments on real dataset show that it requires less number of operations than current suppression-based approach.