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Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint computations without revealing private data. Current MPC algorithms scale poorly with data size, which makes MPC on "big data" prohibitively slow and inhibits its practical use. Many relational analytics queries can maintain MPC's end-to-end security guarantee without using cryptographic MPC techniques for all operations. Conclave is a query compiler that accelerates such queries by transforming them into a combination of data-parallel, local cleartext processing and small MPC steps. When parties trust others with specific subsets of the data, Conclave applies new hybrid MPC-cleartext protocols to run additional steps outside of MPC and improve scalability further. Our Conclave prototype generates code for cleartext processing in Python and Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave scales to data sets between three and six orders of magnitude larger than state-of-the-art MPC frameworks support on their own. Thanks to its hybrid protocols, Conclave also substantially outperforms SMCQL, the most similar existing system.
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Brief Announcement: Federated Code Auditing and Delivery for MPC
Secure multi-party computation (MPC) is a cryptographic primitive that enables several parties to compute jointly over their collective private data sets. MPC’s objective is to federate trust over several computing entities such that a large threshold (e.g., a majority) must collude before sensitive or private input data can be breached. Over the past decade, several general and special-purpose software frameworks have been developed that provide data contributors with control over deciding whom to trust to perform the calculation and (separately) to receive the output. However, one crucial component remains centralized within all existing MPC frameworks: the distribution of the MPC software application itself. For desktop applications, trust in the code must be determined once at download time. For web-based JavaScript applications subject to trust on every use, all data contributors across several invocations of MPC must maintain centralized trust in a single code delivery service. In this work, we design and implement a federated code delivery mechanism for web-based MPC such that data contributors only execute code that has been accredited by several trusted auditors (the contributor aborts if consensus is not reached). Our client-side Chrome browser extension is independent of any MPC scheme and has a trusted computing base of fewer than 100 lines of code.
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- PAR ID:
- 10067690
- Date Published:
- Journal Name:
- SSS 2017: Stabilization, Safety, and Security of Distributed Systems
- Volume:
- 10616
- Page Range / eLocation ID:
- 298--302
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-319-69084-1_20
