Browsing by Subject "differential privacy"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    PHDP: Preserving Persistent Homology in Differentially Private Graph Publications
    (IEEE, 2019-04) Gao, Tianchong; Li, Feng; Computer Information and Graphics Technology, School of Engineering and Technology
    Online social networks (OSNs) routinely share and analyze user data. This requires protection of sensitive user information. Researchers have proposed several techniques to anonymize the data of OSNs. Some differential-privacy techniques claim to preserve graph utility under certain graph metrics, as well as guarantee strict privacy. However, each graph utility metric reveals the whole graph in specific aspects.We employ persistent homology to give a comprehensive description of the graph utility in OSNs. This paper proposes a novel anonymization scheme, called PHDP, which preserves persistent homology and satisfies differential privacy. To strengthen privacy protection, we add exponential noise to the adjacency matrix of the network and find the number of adding/deleting edges. To maintain persistent homology, we collect edges along persistent structures and avoid perturbation on these edges. Our regeneration algorithms balance persistent homology with differential privacy, publishing an anonymized graph with a guarantee of both. Evaluation result show that the PHDP-anonymized graph achieves high graph utility, both in graph metrics and application metrics.
  • Thumbnail Image
    Item
    Sharing Social Networks Using a Novel Differentially Private Graph Model
    (IEEE, 2019-01) Gao, Tianchong; Li, Feng; Computer Information and Graphics Technology, School of Engineering and Technology
    Online social networks (OSNs) often contain sensitive information about individuals. Therefore, anonymizing social network data before releasing it becomes an important issue. Recent research introduces several graph abstraction models to extract graph features and add sufficient noise to achieve differential privacy.In this paper, we design and analyze a comprehensive differentially private graph model that combines the dK-1, dK-2, and dK-3 series together. The dK-1 series stores the degree frequency, the dK-2 series adds the joint degree frequency, and the dK-3 series contains the linking information between edges. In our scheme, low dimensional data makes the regeneration process more executable and effective, while high dimensional data preserves additional utility of the graph. As the higher dimensional model is more sensitive to the noise, we carefully design the executing sequence. The final released graph increases the graph utility under differential privacy.