Local differential privacy for social network publishing

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Social networks often contain sensitive information. Releasing social network data could possibly seriously jeopardize individual privacy. Therefore, we need to protect privacy when publish social network data. However, the current differential privacy for social network data publishing seriously influences the structure of the social network. We propose a local differential privacy model for social network publishing that preserves community structure information. The model generates the synthetic social network data as published versions under the structural constraints of the edge probability reconstruction. We theoretically prove that the local differential privacy model satisfies the definition of differential privacy. We evaluate the efficacy of the proposed method using three real-life social network datasets and show that our method effectively preserves network structural properties, while ensuring a strong degree of privacy.