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.
翻译:我们考虑一个网络编码问题,即目的地希望在所有源节点恢复信号的总和(Gausian随机变量或随机有限字段元素),但必须从能够窃听某一子边缘的窃听器中保守秘密。这种设置自然出现在传感器网络和联合学习中,可能希望对信号的总和(例如重量、梯度)保密。虽然有限字段的案例可以解决,但高西亚随机变量的案例却令人惊讶地困难重重。我们给出了一个简单的推测,根据这个假设,在必要和充分的条件下,可以对高西亚案件进行这种秘密计算,并在最多2个有线边缘的情况下证明推测。