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Cryptographic techniques have the potential to enable distrusting parties to collaborate in fundamentally new ways, but their practical implementation poses numerous challenges. An important class of such cryptographic techniques is known as secure multi-party computation (MPC). In an effort to provide an ecosystem for building secure MPC applications using higher degrees of automation, we present the HACCLE (High Assurance Compositional Cryptography: Languages and Environments) toolchain. The HACCLE toolchain contains an embedded domain-specific language (Harpoon) for software developers without cryptographic expertise to write MPC-based programs. Harpoon programs are compiled into acyclic circuits represented in HACCLEs Intermediate Representation (HIR) that serves as an abstraction for implementing a computation using different cryptographic protocols such as secret sharing, homomorphic encryption, or garbled circuits. Implementations of different cryptographic protocols serve as different backends of our toolchain. The extensible design of HIR allows cryptographic experts to plug in new primitives and protocols to realize computations.We have implemented HACCLE, and used it to program interesting algorithms and applications (e.g., secure auction, matrix-vector multiplication, and merge sort). We show that the performance is improved by using our optimization strategies and heuristics.
The purpose of Secure Multi-Party Computation is to enable protocol participants to compute a public function of their private inputs while keeping their inputs secret, without resorting to any trusted third party. However, opening the public output
Elaborate protocols in Secure Multi-party Computation enable several participants to compute a public function of their own private inputs while ensuring that no undesired information leaks about the private inputs, and without resorting to any trust
Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machine
This paper investigates the problem of Secure Multi-party Batch Matrix Multiplication (SMBMM), where a user aims to compute the pairwise products $mathbf{A}divideontimesmathbf{B}triangleq(mathbf{A}^{(1)}mathbf{B}^{(1)},ldots,mathbf{A}^{(M)}mathbf{B}^
We consider the task of secure multi-party distributed quantum computation on a quantum network. We propose a protocol based on quantum error correction which reduces the number of necessary qubits. That is, each of the $n$ nodes in our protocol requ