KloakDB: A Data Federation for Analyzing Sensitive Data with K -anonymous Query Processing
KloakDB: A Data Federation for Analyzing Sensitive Data with K -anonymous Query Processing
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KloakDB：使用 K 匿名查询处理分析敏感数据的数据联合
DOI：
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发表时间：
2019
期刊：
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通讯作者：
Jennie Rogers
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文献类型：
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作者：
Madhav Suresh;William Wallace;Adel Lahlou;Jennie Rogers
私有数据联合使数据所有者能够在不向彼此公开其秘密元组的情况下汇集其信息以进行查询。在这里，客户端查询所有数据所有者的记录的并集。数据所有者一起工作，使用隐私保护算法来回答查询，以防止他们学习有关其对等方输入的未经授权的信息。只有客户机和联合协调器才知道查询的输出。KloakDB是一个私有数据联合，它使用可信硬件来处理两方或多方输入的SQL查询。目前，私有数据联合会完全遗忘地计算其查询，通过观察查询的指令跟踪和存储器访问模式来保证没有关于数据所有者的敏感输入的信息被泄露给其对等体。与明文执行相比，不经意查询几乎总是在查询运行时降低几个数量级，这使得它对许多应用程序来说不切实际。KloakDB提供了一个半遗忘计算框架，k-匿名查询处理。我们使查询的可观察成绩单k-匿名，因为它是一个流行的标准，在许多领域，包括医学，教育研究和政府数据的数据发布。KloakDB的查询运行使得每个数据所有者可以推断出关于其对等数据中不少于k个人的信息。此外，匿名持有者设置k，在隐私和性能之间创建了一个新的交易。我们的研究结果表明，KloakDB在使用k-匿名查询处理时，在完全不经意评估时，速度提高了117倍。
A private data federation enables data owners to pool their information for querying without disclosing their secret tuples to one another. Here, a client queries the union of the records of all data owners. The data owners work together to answer the query using privacy-preserving algorithms that prevent them from learning unauthorized information about the inputs of their peers. Only the client, and a federation coordinator, learn the query’s output. KloakDB is a private data federation that uses trusted hardware to process SQL queries over the inputs of two or more parties. Currently private data federations compute their queries fully-obliviously, guaranteeing that no information is revealed about the sensitive inputs of a data owner to their peers by observing the query’s instruction traces and memory access patterns. Oblivious querying almost always exacts multiple orders of magnitude slowdown in query runtimes compared to plaintext execution, making it impractical for many applications. KloakDB offers a semi-oblivious computing framework, k -anonymous query processing . We make the query’s observable transcript k - anonymous because it is a popular standard for data release in many domains including medicine, educational research, and government data. KloakDB’s queries run such that each data owner may deduce information about no fewer than k individuals in the data of their peers. In addition, stake-holders set k , creating a novel trade-off between privacy and performance. Our results show that KloakDB enjoys speedups of up to 117X using k -anonymous query processing over full-oblivious evaluation.