IncShrink：使用增量MPC和差分隐私构建高效的外包数据库
IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy
摘要：
在本文中，我们考虑安全外包增长的数据库，支持基于视图的查询回答。这些数据库允许不受信任的服务器私下维护物化视图，使得它们可以仅使用物化视图来处理查询请求，而不是访问从中导出视图的原始数据。为了解决这个问题，我们设计了一个新的基于视图的安全外包增长数据库框架，Incshrink。该解决方案的主要特点是：（i）Incshrink使用增量MPC操作器维护视图，无需预先使用可信的第三方，以及（ii）为了确保高性能，Incshrink保证在存在更新的情况下泄漏满足DP。据我们所知，没有现有的系统具有这些属性。我们展示了Incshrink的实际可行性，在效率和准确性方面，对真实世界的数据集和TPC-DS基准进行了广泛的实证评估。评估结果表明，Incshrink在隐私、准确性和效率保证方面提供了三方面的权衡，并且与不支持基于视图的查询范式的标准安全外包数据库相比，提供了至少7，800倍的性能优势。
英文摘要：
In this paper, we consider secure outsourced growing databases that support view-based query answering. These databases allow untrusted servers to privately maintain a materialized view, such that they can use only the materialized view to process query requests instead of accessing the original data from which the view was derived. To tackle this, we devise a novel view-based secure outsourced growing database framework, Incshrink. The key features of this solution are: (i) Incshrink maintains the view using incremental MPC operators which eliminates the need for a trusted third party upfront, and (ii) to ensure high performance, Incshrink guarantees that the leakage satisfies DP in the presence of updates. To the best of our knowledge, there are no existing systems that have these properties. We demonstrate Incshrink's practical feasibility in terms of efficiency and accuracy with extensive empirical evaluations on real-world datasets and the TPC-ds benchmark. The evaluation results show that Incshrink provides a 3-way trade-off in terms of privacy, accuracy, and efficiency guarantees, and offers at least a 7,800 times performance advantage over standard secure outsourced databases that do not support the view-based query paradigm.
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