论文标题
秘密总和计算的算术网络编码
Arithmetic Network Coding for Secret Sum Computation
论文作者
论文摘要
我们考虑一个网络编码问题，目的地想要在所有源节点上恢复信号总和（高斯随机变量或随机有限字段元素）的总和，但是必须将窃听器从窃听器中保密，该窃听器可以在边缘子集中窃听。这种设置自然出现在传感器网络和联合学习中，其中可能需要信号之和（例如权重，梯度）的保密。尽管可以解决有限场的情况，但高斯随机变量的情况令人惊讶地困难。我们对高斯案例可以进行这种秘密计算的必要条件进行了简单的猜想，并在最多2个窃听的边缘数量时证明了猜想。
We consider a network coding problem where the destination wants to recover the sum of the signals (Gaussian random variables or random finite field elements) at all the source nodes, but the sum must be kept secret from an eavesdropper that can wiretap on a subset of edges. This setting arises naturally in sensor networks and federated learning, where the secrecy of the sum of the signals (e.g. weights, gradients) may be desired. While the case for finite field can be solved, the case for Gaussian random variables is surprisingly difficult. We give a simple conjecture on the necessary and sufficient condition under which such secret computation is possible for the Gaussian case, and prove the conjecture when the number of wiretapped edges is at most 2.
