Abstract
Online social networks (OSNs) contain sensitive information about individuals, so it’s important to anonymize network data before releasing it. Recently, researchers introduced differential privacy to give a strict privacy guarantee. Graph abstraction models are essential to transform graph structural information into numerical type data, and the choice of models may influence the utility preservation of the published graph. In this paper, we propose a comprehensive differentially private graph model which 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 series data makes the regeneration process more executable and effective, while high dimensional data preserves additional utility of the graph. As the higher dimensional data is more sensitive to the noise, we carefully design the executing sequence and add three levels of rewiring algorithms to further preserve the structural information. The final released graph increases the graph utility under differential privacy. We also experimentally evaluate our approach on real-world OSNs and show that our scheme produces ready-to-be-shared graphs that are closely matched with the originals, while achieving differential privacy.
Highlights
Studying online social networks (OSNs) through graph analysis could produce knowledge of human social relationships, help feed advertisements to recommendation targets, and evaluate the effectiveness of applications
Since the differential privacy is applied on the query result, typically the numerical type data, the dK graph model is chosen as the graph abstraction model to transform the graph structures into a set of structural statistics
Our work focuses on minimizing the error between the dK series in the published graph and the target dK series calculated under differential privacy
Summary
Studying online social networks (OSNs) through graph analysis could produce knowledge of human social relationships, help feed advertisements to recommendation targets, and evaluate the effectiveness of applications. After studying the differences of abilities between dK-1, dK-2, and dK-3 series, we find that low dimensional models, e.g., dK-1, are less sensitive to noise and can regenerate a graph. The CAT scheme uses all three kinds of dK information in the regeneration phase It aims to reduce the errors of the dK-2 and dK-3 series. The major technical contributions are the following: (1) We are the first to build the systematic regeneration algorithm for VOLUME 8, 2020 embedding dK-3 information in graph anonymization, which helps to preserve more utility than existing dK models. (4) We use three levels of rewiring algorithms to actively reduce the errors between the desired dK series and the published graph. (5) We reveal the insights and challenges of using different levels of dK abstraction models jointly to enhance the utility under differential privacy The major technical contributions are the following: (1) We are the first to build the systematic regeneration algorithm for VOLUME 8, 2020 embedding dK-3 information in graph anonymization, which helps to preserve more utility than existing dK models. (2) We combine the dK-3 model with both dK-1 and dK-2 models in sampling and graph regeneration, which mitigates the high sensitivity and complexity in the dK-3 model and makes the design practical. (3) We design two different routes, CAT and LTH, to generate the graph efficiently and effectively, even under the impact of noise. (4) We use three levels of rewiring algorithms to actively reduce the errors between the desired dK series and the published graph. (5) We reveal the insights and challenges of using different levels of dK abstraction models jointly to enhance the utility under differential privacy
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