PolyDot Coded Privacy Preserving Multi-Party Computation at the Edge
PolyDot Coded Privacy Preserving Multi-Party Computation at the Edge
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DOI：
10.1109/spawc51304.2022.9833997
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Elahe Vedadi;Yasaman Keshtkarjahromi;H. Seferoglu
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文献类型：
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作者：
Elahe Vedadi;Yasaman Keshtkarjahromi;H. Seferoglu
我们使用多方计算（MPC）研究边缘网络中隐私保护分布式矩阵乘法的问题。编码多方计算（CMPC）是一种新兴方法，通过采用编码计算来减少 MPC 所需的工作人员数量。现有的 CMPC 方法通常将专为高效矩阵乘法而设计的编码计算算法与 MPC 结合起来。我们证明这种方法效率不高。我们设计了一种新颖的 CMPC 算法；使用 PolyDot 代码进行 PolyDot 编码 MPC (PolyDot-CMPC)。我们利用在 PolyDot-CMPC 设计中构造多项式时自然出现的“垃圾项”来减少隐私保护计算所需的工作人员数量。我们表明，纠缠多项式代码在编码计算设置中始终优于 PolyDot 代码，但在 MPC 设置中不一定优于 PolyDot-CMPC。
We investigate the problem of privacy preserving distributed matrix multiplication in edge networks using multi-party computation (MPC). Coded multi-party computation (CMPC) is an emerging approach to reduce the required number of workers in MPC by employing coded computation. Existing CMPC approaches usually combine coded computation algorithms designed for efficient matrix multiplication with MPC. We show that this approach is not efficient. We design a novel CMPC algorithm; PolyDot coded MPC (PolyDot-CMPC) by using PolyDot codes. We exploit "garbage terms" that naturally arise when polynomials are constructed in the design of PolyDot-CMPC to reduce the number of workers needed for privacy-preserving computation. We show that entangled polynomial codes, which are consistently better than PolyDot codes in coded computation setup, are not necessarily better than PolyDot-CMPC in MPC setting.