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

TL;DR

Incshrink is a novel framework for secure, efficient, view-based outsourced databases that uses incremental MPC and differential privacy to protect data during updates, offering significant performance improvements.

Contribution

It introduces Incshrink, a new view-based secure outsourced database system combining incremental MPC and differential privacy, with no prior comparable solutions.

Findings

Achieves at least 7,800x performance improvement over traditional secure databases.

Provides a flexible privacy-accuracy-efficiency trade-off.

Demonstrates practical feasibility with real-world datasets and benchmarks.

Abstract

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.