A Uniform and Open-source Publish System for Graph Data Anonymization and De-anonymization.Download Version 2.0
When using ShareSafe, if you find any bugs, please let us know. If you want to contribute some new algorithm or updated implementation of existing algorithms, please also let us know.
Anonymization Module (AM)
This module can anonymize raw graph data and generate anonymized data. In this module, we implement 11 state-of-the-art graph data anonymization schemes, including Edge Editing based algorithms, k-anonymity based algorithms and its variants, aggregation/class/cluster based algorithms, differential privacy based algorithms, and the random walk based algorithm.
Utility Module (UM)
This module can evaluate raw/anonymized data’s utility with respect to the 12 graph utility metrics and 7 application utility metrics. With the UM, we can determine whether the data to be published/shared (e.g., the anonymized data) satisfies required utility requirements. We can also evaluate how an anonymization algorithm preserves data utility.
De-Anonymization Module (DM)
This module offers 15 structural based de-anonymization algorithms (SDA) (all the existing SDA algorithms, to the best of our knowledge). In this module, the security of data to be published/shared can be evaluated with real-world powerful SDA attacks. More importantly, the effectiveness of an anonymization algorithm can be examined by this module, i.e., whether the anonymized data of an anonymization algorithm is resistant to modern SDA attacks.
Recommondation Module (RM)
This module recommends optimum anonymization algorithms that meet users' utility and security requirements. RM shows the detail quantifications about anonymized graph data's utility metric and de-anonymization attacks' results. RM integrates 11 world top graph data anonymization techniques, 21 graph utilities, and 13 SDA attacks. These algorithms ensures the accuracy of recommendation results and provide a solid foundations of our report.