TC: Small: Robust Anonymization on Social Networks

TC：小：社交网络上强大的匿名化

• 批准号: 1115234
• 负责人: Philip Yu
• 金额: $ 49.59万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1115234&HistoricalAwards=false
• 关键词: TC; Small; Robust; Anonymization; Social

Massive graphs arise in many social media applications, such as social networks, E-commerce recommendation systems, e-mail communication patterns, and other collaborative applications. Such data is often sensitive from a privacy point of view. Recently there are many privacy preserving schemes being proposed to protect the release of network data. The question is how effective these schemes are on preventing the re-identification of nodes, i.e., preserving the identity anonymization of the network nodes.This project will raise the issue of the inadequacy of the current network anonymization schemes for massive and sparse graphs. It is important to understand the theoretical properties which make them susceptible to re-identification attacks. By a systematic study of the re-identification risks of the existing approaches, and development of new principles for anonymization of network data, we will deepen our understanding of the problems and be better able to protect the data privacy. By designing a new type of attack algorithms and raising the issue on the privacy exposure of the current network anonymization schemes, the work can lead to fundamentally different thinking on how to perform privacy preserving data publishing on network data. It provides new insights on how to devise anonymization schemes to protect the privacy of social network data.One of the biggest obstacles on sharing information is the privacy concern. This project has the potential to make fundamental, disruptive advances in protecting the privacy of network data. It provides new insights on the inadequacy of the current anonymization schemes. Many researchers need access to sensitive data, e.g., social network data, e-mail and communication patterns, etc. By advancing the knowledge on privacy preserving data publishing, the barrier of sharing data will come down to facilitate scientific research activities.

大量图出现在许多社交媒体应用中，诸如社交网络、电子商务推荐系统、电子邮件通信模式和其他协作应用。从隐私的角度来看，这些数据通常是敏感的。近年来，有许多隐私保护方案被提出来保护网络数据的发布。 问题是这些方案在防止节点重新识别方面的有效性如何，即，保持网络节点的身份匿名化。这个项目将提出当前网络匿名化方案对于大规模和稀疏图的不足的问题。重要的是要了解使它们容易受到重新识别攻击的理论属性。通过系统研究现有方法的再识别风险，制定网络数据匿名化的新原则，将加深对问题的认识，更好地保护数据隐私。通过设计一种新型的攻击算法，并提出了当前网络匿名方案的隐私暴露问题，这项工作可以导致对如何在网络数据上执行隐私保护数据发布的根本不同的思考。它为如何设计匿名化方案以保护社交网络数据的隐私提供了新的见解。该项目有可能在保护网络数据隐私方面取得根本性的、颠覆性的进展。它为当前匿名化计划的不足提供了新的见解。许多研究人员需要访问敏感数据，例如，社交网络数据、电子邮件和通信模式等。通过提高隐私保护数据发布的知识，共享数据的障碍将降低，以促进科学研究活动。