TC: Medium: Dissemination and Analysis of Private Network Data

TC：媒介：专网数据传播与分析

• 批准号: 0964094
• 负责人: Gerome Miklau
• 金额: $ 87.31万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964094&HistoricalAwards=false
• 关键词: TC; Medium; Dissemination; Analysis; Private

The goal of this research project is to enable statistical analysis and knowledge discovery on networks without violating the privacy of participating entities. Network data sets record the structure of computer, communication, social, or organizational networks, but they often contain highly sensitive information about individuals. The availability of network data is crucial for analyzing, modeling, and predicting the behavior of networks. The team's approach is based on model-based generation of synthetic data, in which a model of the network is released under strong privacy conditions and samples from that model are studied directly by analysts. Output perturbation techniques are used to privately compute the parameters of popular network models. The resulting "noisy" model parameters are released, satisfying a strong, quantifiable privacy guarantee, but still preserving key properties of the networks. Analysts can use the released models to sample individual networks or to reason about properties of the implied ensemble of networks. By synthesizing versions of networks that would otherwise remain hidden, this research can advance the study of topics such as disease transmission, network resiliency, and fraud detection. The project will result in publicly available privacy tools, a repository for derived models and sample networks, and contributions to workforce development in the field of information assurance. The experimental research is linked to educational efforts including undergraduate involvement in research through a Research Experience for Undergraduates site, as well as interdisciplinary seminars.For further information see the project web site at the URL:http://dbgroup.cs.umass.edu/private-network-data

该研究项目的目标是在不侵犯参与实体隐私的情况下，在网络上进行统计分析和知识发现。 网络数据集记录了计算机、通信、社交或组织网络的结构，但它们通常包含有关个人的高度敏感信息。 网络数据的可用性对于分析、建模和预测网络行为至关重要。 该团队的方法基于基于模型的合成数据生成，其中网络模型在强隐私条件下发布，分析师直接研究该模型的样本。 输出扰动技术被用来私下计算流行的网络模型的参数。 由此产生的“噪声”模型参数被释放，满足强大的，可量化的隐私保证，但仍然保留了网络的关键属性。 分析师可以使用发布的模型对单个网络进行采样，或者对隐含的网络集合的属性进行推理。 通过合成网络的版本，否则将保持隐藏，这项研究可以推进疾病传播，网络弹性和欺诈检测等主题的研究。 该项目将产生可公开使用的隐私工具、衍生模型和样本网络的储存库，并有助于信息保证领域的劳动力发展。 实验研究与教育工作相联系，包括通过本科生研究经验网站以及跨学科研讨会参与研究的本科生。欲了解更多信息，请访问该项目的网站：http://dbgroup.cs.umass.edu/private-network-data