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

Publication ,  Conference
Wang, C; Bater, J; Nayak, K; MacHanavajjhala, A
Published in: Proceedings of the ACM SIGMOD International Conference on Management of Data
June 10, 2022

In this paper, we consider secure outsourced growing databases (SOGDB) that support view-based query answering. These databases allow untrusted servers to privately maintain a materialized view. This allows servers to use only the materialized view for query processing instead of accessing the original data from which the view was derived. To tackle this, we devise a novel view-based SOGDB 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 experiments 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, and offers at least a 7,800x performance advantage over standard SOGDB that do not support view-based query paradigm.

Duke Scholars

Published In

Proceedings of the ACM SIGMOD International Conference on Management of Data

DOI

ISSN

0730-8078

Publication Date

June 10, 2022

Start / End Page

818 / 832
 

Citation

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Wang, C., Bater, J., Nayak, K., & MacHanavajjhala, A. (2022). IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 818–832). https://doi.org/10.1145/3514221.3526151
Wang, C., J. Bater, K. Nayak, and A. MacHanavajjhala. “IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy.” In Proceedings of the ACM SIGMOD International Conference on Management of Data, 818–32, 2022. https://doi.org/10.1145/3514221.3526151.
Wang C, Bater J, Nayak K, MacHanavajjhala A. IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2022. p. 818–32.
Wang, C., et al. “IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy.” Proceedings of the ACM SIGMOD International Conference on Management of Data, 2022, pp. 818–32. Scopus, doi:10.1145/3514221.3526151.
Wang C, Bater J, Nayak K, MacHanavajjhala A. IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy. Proceedings of the ACM SIGMOD International Conference on Management of Data. 2022. p. 818–832.

Published In

Proceedings of the ACM SIGMOD International Conference on Management of Data

DOI

ISSN

0730-8078

Publication Date

June 10, 2022

Start / End Page

818 / 832