Preprocessing-Based Verification of Multiparty Protocols with Honest Majority

This paper presents a generic “GMW-style” method for turning passively secure protocols into protocols secure against covert attacks, adding relatively cheap offline preprocessing and post-execution verification phases. In the preprocessing phase, each party generates and shares a sufficient amount of verified multiplication triples that will be later used to assist that party’s proof. The execution phase, after which the computed result is already available to the parties, has only negligible overhead that comes from signatures on sent messages. In the postprocessing phase, the verifiers repeat the computation of the prover in secret-shared manner, checking that they obtain the same messages that the prover sent out during execution. The verification preserves the privacy guarantees of the original protocol. It is applicable to protocols doing computations over finite rings, even if the same protocol performs its computation over several distinct rings. We apply our verification method to the Sharemind platform for secure multiparty computations (SMC), evaluate its performance and compare it to other existing SMC platforms offering security against stronger than passive attackers

Conclave: secure multi-party computation on big data (extended TR)

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.Comment: Extended technical report for EuroSys 2019 pape

Programmeerimiskeel privaatsust säiliavate rakenduste loomiseks

https://www.ester.ee/record=b5433159*es

SecreC: a privacy-aware programming language with applications in data mining

https://www.ester.ee/record=b5412660*es