Privacy-aware Management of Relational Data

该建议研究了使用星型模式对关系数据进行具有隐私意识的高效管理。特别地，我们考虑包含个人数据的非规范化、非聚合数据库。此外，我们假设一个数据库模型，其中许多维度表通过代理键与一个大型事实表链接。通常，这类数据库非常频繁地处理读取操作，插入和删除操作必须不时执行，但对现有元组的更新操作很少处理。我们关注这个场景有三个原因：首先，星型模式是一种经典的数据库模型，最近许多存储个人数据的应用程序都使用它，例如，用于数据仓库和挖掘，在ERP系统中管理客户的交易历史，或者使用像Folksonomies这样的社交软件方法来注释数字对象。其次，现在为使用星型模式的关系数据库开发隐私方法尤为重要，因为通过使用云计算或数据库即服务等技术，将此类数据库转移到第三方托管的趋势非常强烈。第三，如何保护这些数据库所涉及的个人隐私尚不清楚。目前存在针对统计数据库、地理空间轨迹、集值数据库或图形数据库的隐私保护方法，例如，像差分隐私这样的匿名化方法或像希波克拉底数据库这样的架构。然而，对于使用星型模式的关系数据库，这种方法不容易部署。我们的目标是为关系数据开发一个具有隐私意识的数据库管理系统，该系统采用星型模式（RDSS），可以有效地处理查询、插入和删除操作。因此，每个用户都应该能够知道并确定谁能够访问与用户相关的哪些个人值。为此，我们希望为RDSS创建隐私方法，以便(1)隐藏数据与相关个人的关系，或(2)限制对敏感数据的访问。在这种情况下，我们要设计系统架构、算法、数据库模型和攻击者模型。因此，我们努力寻求一种全面的方法，考虑到在RDSS中存储、传输和处理个人数据的许多方面可能会出现隐私挑战。

This proposal investigates privacy-aware, efficient management of relational data with a star schema. In particular, we consider denormalized, non-aggregated databases containing personal data. Further, we assume a database model where many dimension tables are linked with a large fact table via surrogate keys. Typically, such databases process read operations very frequently, insert- and delete operations must be executed from time to time, but update operations on existing tuples are infrequently processed. We focus on this scenario for three reasons: Firstly, the star schema is a classical database model that is used by numerous recent applications storing personal data, e.g., for data warehousing and mining, to manage transaction histories of customers in an ERP system, or to annotate digital objects with social software approaches like Folksonomies. Secondly, it is particularly important to develop privacy approaches for relational databases with a star schema now, because there is a strong tendency to move such databases into the custody of third parties by using techniques like cloud computing or database-as-a-service. Thirdly, it is unclear how to protect the privacy of the individuals concerned by such databases. There exist privacy approaches for statistical databases, geospatial trajectories, set-valued databases or graph databases, e.g., anonymization approaches like Differential Privacy or architectures like Hippocratic Databases. Nevertheless, such approaches cannot be deployed readily for relational databases with a star schema. Our objective is to develop a privacy-aware database management system for relational data with a star schema (RDSS) that handles queries, insert- and delete operations efficiently. Thus, every user should be able to know and to determine who is able to access which personal values relating to the user. For this purpose, we want to create privacy methods for RDSS that (1) conceal the relation of the data to the individual concerned, or (2) restrict the access to sensitive data. In this context, we want to design system architectures, algorithms, database models and attacker models. Thus, we strive for a holistic approach that takes into account that privacy challenges might arise from many aspects of storing, transferring and processing personal data in RDSS.