With the advances of graph analytics, preserving privacy in publishing graph data becomes an important task. However, graph data is highly sensitive to structural changes. Perturbing graph data for achieving differential privacy inevitably leads to inject a large amount of noise and the utility of anonymized graphs is severely limited. In this paper, we propose a microaggregation-based framework for graph anonymization which meets the following requirements: (1) The topological structures of an original graph can be preserved at different levels of granularity;(2) ε-differential privacy is guaranteed for an original graph through adding controlled perturbation to its edges (i.e., edge privacy);(3) The utility of graph data is enhanced by reducing the magnitude of noise needed to achieve ε-differential privacy. Within the proposed framework, we further develop a simple yet effective microaggregation algorithm under a distance constraint. We have empirically verified the noise reduction and privacy guarantee of our proposed algorithm on three realworld graph datasets. The experiments show that our proposed framework can significantly reduce noise added to achieve ε-differential privacy over graph data, and thus enhance the utility of anonymized graphs.

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The behavioral problems are increasing day by day among students acquiring any sort of education. Hence, the study was done to identify behavioral problems in school and madrassa students. A random sampling technique was used for data collection. The sample was comprised of 140 participants, ranging from 14 to 17 years of age, including 70 girls and 70 boys. Child behavior checklist (CBCL), interpersonal reactivity index (IRI) and cognitive emotion regulation scale (CERS) were used for the data collection. Different statistical analyses were conducted to analyze the data. The results of the study showed that the students going to madrassa (M=65.64, SD=31.69) have more behavioral problems as compared to students going to school (M=22.47, SD=6.94). Moreover, the behavioral problems were found more in boys (M=56.88, SD=39.38) as compared to the girls (M=31.10, SD=10.63). Furthermore, Regression analysis showed that behavioral problems was significantly predicted by empathy (β=-.61, p<.01) and emotion regulation (β=.72, p<.01). The present study will be helpful for the parents, educational administration and counselor to identify and manage behavioral problems in students seeking education.