SI2-SSE: ShareSafe: A Framework for Researchers and Data Owners to Help Facilitate Secure Graph Data Sharing

我们迫切需要信息/数据共享来解决一些最重要的学术和社会问题。这些数据的规模越来越大，变得越来越复杂；在许多情况下，它们可以被认为是结构化的。结构化数据的一个例子是描述特定人群中疾病传播的数据。数据的广泛共享可以帮助企业增加收入，帮助减少传染病的传播，加速某些重大疾病的治疗，并使研究人员能够进行可重复的实验。虽然共享数据有巨大的好处这一点几乎没有异议，但它仍然没有像它应该的那样广泛。这在一定程度上是由于共享数据集的隐私问题。该项目将开发一个开源系统（ShareSafe），允许数据所有者在发布之前评估其匿名数据集的安全性（例如抗去匿名化攻击）和实用性，这将有助于促进数据共享过程。该项目的总体目标是开发一个软件框架ShareSafe，该框架(1)帮助结构化数据所有者（例如，社交网络研究人员，流行病学家）在使用简单和最先进的匿名化技术时评估其数据集的安全性（针对现代去匿名化攻击）和实用性；(2)为结构化数据安全/隐私研究人员提供一个统一的平台，以全面研究、评估和比较现有/新开发的结构化数据实用和隐私技术。ShareSafe是一个全面的、用户友好的框架，具有以下功能：ShareSafe将使数据所有者能够：(1)使用所有最先进的匿名化技术对其数据集进行匿名化；(2)使用最先进的效用测量技术测量匿名数据集的效用；(3)通过最先进的去匿名化攻击来评估其数据集的实际安全性；(4)通过使用最先进的去匿名化量化（去匿名化分析）技术来评估其数据集的理论安全性。理解(2)-(4)的结果可以让数据所有者确定在共享数据集时哪种匿名化算法适合他们的需求。最后，上述技术将以统一的方式作为开源软件实现，使图数据安全/隐私研究人员能够全面研究、评估和比较现有/新开发的图数据实用和隐私技术。

There is a critical need for information/data sharing to solve some of our most significant academic and societal problems. These data are increasing in size and are becoming much more complex; in many cases, they can be considered structured. An example of structured data is data describing disease propagation in a specific population. Widespread sharing of data can, among many other things, help corporations increase their revenues, help reduce the spread of communicable diseases, accelerate the cure of some of the most significant diseases, and enable reproducible experiments amongst researchers. Although there is little disagreement that sharing data has tremendous benefits, it is still not as widespread as it should be. This is, in part, due to privacy concerns with sharing datasets. This project will develop an open source system (ShareSafe) that allows data owners to evaluate the security (such as resistance to de-anonymization attacks) and utility of their anonymized datasets before release, which will help facilitate the data sharing process.The overarching goals of this project are to develop a software framework, ShareSafe, that (1) helps structured data owners (e.g., social network researchers, epidemiologists) evaluate the security (against modern de-anonymization attacks) and utility of their datasets when using simple and state-of-the-art anonymization techniques; and (2) to provide structured data security/privacy researchers a uniform platform to comprehensively study, evaluate, and compare existing/newly developed techniques for structured data utility and privacy. ShareSafe is a comprehensive, user-friendly framework with the following capabilities: ShareSafe will enable data owners to: (1) anonymize their datasets with all of the state-of-the art anonymization techniques; (2) measure the utility of anonymized datasets using state-of-the-art utility measurement techniques; (3) evaluate the practical security of their datasets by subjecting them to state-of-the-art de-anonymization attacks; and (4) evaluate the theoretical security of their datasets by subjecting them to state-of-the-art de-anonymization quantification (de-anonymizability analysis) techniques. Understanding the results from (2)-(4) allows data owners to determine which anonymization algorithm suits their needs when sharing datasets. Finally, the aforementioned techniques will be implemented in a uniform manner as open source software, allowing graph data security/privacy researchers the ability to comprehensively study, evaluate, and compare existing/newly developed techniques for graph data utility and privacy.