No abstract

No abstract

With the increasing interest in Secure Multi-Party Computation protocols (MPC), there have been several works such as the SPDZ 1 protocol that tackled this problem under a malicious security with dishonest majority attack model. However, most of these MPC efforts assume that the nodes running the computations are also supplying the inputs, which is not a realistic assumption for many real-life applications. In this paper, we extend the SPDZ protocol to enable clients outsource data and computation to the clouds while ensuring the correctness of the results, in addition to integrity and confidentiality of the input and output. We guarantee that the computation among nodes is done correctly by verifying their output's Message Authentication Codes (MACs) at the end. Specifically, we delegate this task to an honest server. Our approach strives to minimize the burden on clients while enabling cheating detection even when assuming a malicious attack model with dishonest majority.

No abstract

Secure multi-party computation (SMPC) allows mutually distrusted parties to evaluate a function jointly without revealing their private inputs. This technique helps organizations collaborate on a common goal without disclosing confidential or protected data. Despite its suitability for privacy-preserving computation, SMPC suffers from network-based performance limitations. Specifically, the SMPC parties perform the techniques in rounds, where they execute a local computation and then share their round output with the other parties. This network interchange creates a bottleneck as parties need to wait until the data propagates before resuming the execution. To reduce the SMPC execution time, we propose a pipelining-like approach for each round's computation and communication by dividing the data and readjusting the execution order. Targeting deep learning applications, we propose strategies for the case of matrix multiplication, a core component of such applications. Our results on a distributed cloud deployment show a significant reduction in the SMPC execution time.