论文标题

Incshrink:使用增量MPC和差异隐私来架构有效的外包数据库

IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy

论文作者

Wang, Chenghong, Bater, Johes, Nayak, Kartik, Machanavajjhala, Ashwin

论文摘要

在本文中,我们考虑了支持基于视图的查询答案的安全外包生长数据库。这些数据库允许不受信任的服务器私下维护实现的视图,以便它们只能使用实现的视图来处理查询请求,而不是访问从中得出该视图的原始数据。为了解决这个问题,我们设计了一种新颖的基于视图的安全外包生长数据库框架Incshrink。该解决方案的关键功能是:(i)IncShrink使用增量MPC操作员维护视图,从而消除了对可信赖的第三方的需求,并且(ii)为了确保高性能,Incshrink保证在更新的情况下泄漏满足DP。据我们所知,没有现有系统具有这些属性。我们通过对现实世界数据集和TPC-DS基准进行广泛的经验评估,在效率和准确性方面展示了Incshrink的实际可行性。评估结果表明,Incshrink在隐私,准确性和效率保证方面提供了三路权衡,并且至少比不支持基于视图的查询范式的标准安全外包数据库提供了至少7,800倍的性能优势。

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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