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

结构化数据的一个例子是描述疾病在特定人群中传播的数据。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，加快一些最重要疾病的治愈，并使研究人员能够进行可重复的实验。这在一定程度上是由于共享数据集的隐私问题。这个项目将开发一个开放源码系统(ShareSafe)，允许数据所有者在发布之前评估其匿名化数据集的安全性(如对去匿名化攻击的抵抗力)和效用，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，即ShareSafe，它(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(抵御现代去匿名化攻击)和效用；以及(2)为结构化数据安全/隐私研究人员提供统一的平台，以全面研究、评估和比较现有/新开发的结构化数据效用和隐私技术。ShareSafe是一个全面的、用户友好的框架，具有以下功能：(1)使用所有最先进的匿名技术将数据所有者匿名；(2)使用最先进的效用测量技术衡量匿名数据集的效用；(3)通过使他们的数据集遭受最先进的反匿名化攻击来评估他们数据集的实际安全性；以及(4)通过对他们的数据集进行最先进的去匿名化量化(去匿名性分析)技术来评估他们的数据集的理论安全性。最后，上述技术将作为开源软件以统一的方式实施，使图形数据安全/隐私研究人员能够全面研究、评估和比较现有/新开发的图形数据效用和隐私技术。

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.