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Information networks, such as social media and email networks, often contain sensitive information. Releasing such network data could seriously jeopardize individual privacy. Therefore, we need to sanitize network data before the release. In this paper, we present a novel data sanitization solution that infers a network's structure in a differentially private manner. We observe that, by estimating the connection probabilities between vertices instead of considering the observed edges directly, the noise scale enforced by differential privacy can be greatly reduced. Our proposed method infers the network structure by using a statistical hierarchical random graph (HRG) model. The guarantee of differential privacy is achieved by sampling possible HRG structures in the model space via Markov chain Monte Carlo (MCMC). We theoretically prove that the sensitivity of such inference is only O(log n), where n is the number of vertices in a network. This bound implies less noise to be injected than those of existing works. We experimentally evaluate our approach on four real-life network datasets and show that our solution effectively preserves essential network structural properties like degree distribution, shortest path length distribution and influential nodes.

Regularly releasing the aggregate statistics about data streams in a privacy-preserving way not only serves valuable commercial and social purposes, but also protects the privacy of individuals. This problem has already been studied under differential privacy, but only for the case of a single continuous query that covers the entire time span, e.g., counting the number of tuples seen so far in the stream. However, most real-world applications are window-based, that is, they are interested in the statistical information about streaming data within a window, instead of the whole unbound stream. Furthermore, a Data Stream Management System (DSMS) may need to answer numerous correlated aggregated queries simultaneously, rather than a single one. To cope with these requirements, we study how to release differentially private answers for a set of sliding window aggregate queries. We propose two solutions, each consisting of query sampling and composition. We first selectively sample a subset of representative sliding window queries from the set of all the submitted ones. The representative queries are answered by adding Laplace noises in a way satisfying differential privacy. For each non-representative query, we compose its answer from the query results of those representatives. The experimental evaluation shows that our solutions are efficient and effective.

Background A high tendency of intention to leave has been noted for nurses in China. The nursing profession is currently unstable. Methods A sample of 51406 nurses from 311 hospitals in China who completed the self-administered questionnaire online was recruited via the China Nursing Association by email and phone using a simple random sampling method. The recruitment occurred between July 2016 and July 2017. Results The majority of the nurses had working experience ≤20 years and had to work on night shifts. A high percentage of nurses (71.8%) had insomnia, followed by 37.0% who developed varicose veins and 40.9% who experienced musculoskeletal-related disorders. The proportions of the nurses who developed gastrointestinal and urinary system diseases were 56.0% and 18.2%, respectively. Nearly half of the nurses did not have a clear goal for their future career development and intended to leave. Nurses with long working hours each week were positively associated with the development of occupational diseases. The prevalence of occupational diseases was independently associated with career development. Conclusions A high prevalence of occupational diseases was noted among nurses in China. The data indicated that 50% of the nurses were vague regarding their career planning. The data suggest that managers need to pay more attention and to prevent this problem. Appropriate interventions should also be provided.

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