Developing realistic attack models for privacy preserving record linkage and algorithms to prevent such attacks

分析大规模数据集（大数据）在社会科学和官方统计中越来越重要。对这些数据的分析通常需要将多个数据集连接起来。在许多国家，这种联系必须在没有唯一的个人识别号码的情况下进行。这个过程在统计学和计算机科学中被称为记录链接。在欧洲法律和德国联邦和非中央数据保护组织的特殊限制下，记录链接需要特殊技术（隐私保护记录链接）。以前的方法不是基于现实的攻击模型。这个项目的工作计划就是开发这样的攻击模型和算法来防止这些攻击。该项目将定义隐私链接的质量要求，并开发正式的安全模型。在分析有关正式安全模式的现有建议之后，将拟订新的保障措施。该提案描述了三种以前未发表的方法。现有的和新开发的程序将进行数学研究，模拟和现实世界的数据。该项目的目标是为大型数据集开发加密安全隐私保护记录链接技术。

Analyzing large scale data sets (big data) is gaining importance in the Social Sciences and Official Statistics. The analysis of such data often requires linking multiple data sets. In many countries, this linkage has to be done without a unique personal identification number. This process is called record linkage in statistics and computer science. Record linkage under the special restrictions given by European law and the federal and non-central organisation of data protection in Germany requires special techniques (privacy preserving record linkage). Previous approaches are not based on realistic attack models. The work program of this project is the development of such attack models and algorithms to prevent these attacks. The project will define quality requirements for privacy linkage and develop a formal security model. The analysis of existing proposals with regard to the formal security model will be followed by the development of new safeguards. The proposal describes three previously unpublished methods. Existing and newly developed procedures will be studied mathematically, with simulated and with real world data. The goal of the project is the development of cryptographically secure privacy preserving record linkage techniques for large data sets.