Simple, Private, and Accurate Distributed Averaging
Simple, Private, and Accurate Distributed Averaging
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简单、私密且准确的分布式平均
DOI：
10.1109/allerton.2019.8919736
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
K. Lynch
中科院分区：
文献类型：
--
作者：
Israel L. Donato Ridgley;R. Freeman;K. Lynch
一些分布式优化应用需要保密，这意味着在联合优化过程中，节点本地的某些参数的值不应该泄露给网络中的其他节点。一种特殊情况是私有分布式平均问题，其中节点网络以分布式方式计算单个节点参考参数的全局平均值，同时保持每个参考的私密性。我们提出了简单的迭代方法，保证了高精度(即全局平均值的精确渐近计算)和高保密性(即任何节点都不能估计另一个节点的参考值到任何有意义的程度)。为了实现这一点，我们假设模拟节点之间通信的有向图满足一定的拓扑条件。文献中的其他相关方法也达到了较高的精度和隐私性，但在拓扑条件下比我们的方法更具限制性。此外，我们的方法更简单，因为它不需要任何初始加扰阶段，它不向分布式计算注入任何噪声或其他掩蔽信号，它不需要任何边权重的随机切换，它不依赖于同态加密。
Some distributed optimization applications require privacy, meaning that the values of certain parameters local to a node should not be revealed to other nodes in the network during the joint optimization process. A special case is the problem of private distributed averaging, in which a network of nodes computes the global average of individual node reference parameters in a distributed manner while preserving the privacy of each reference. We present simple iterative methods that guarantee high accuracy (i.e. the exact asymptotic computation of the global average) and high privacy (i.e. no node can estimate another node’s reference value to any meaningful degree). To achieve this, we assume that the digraph modeling the communication between nodes satisfies certain topological conditions. Other related methods in the literature also achieve high accuracy and privacy, but under topological conditions more restrictive than ours. Moreover, our method is simpler because it does not require any initial scrambling phase, it does not inject any noise or other masking signals into the distributed computation, it does not require any random switching of edge weights, and it does not rely on homomorphic encryption.
影响因子：
6.8
作者：
Yongqiang Wang
通讯作者：
Yongqiang Wang
有向图连续时间静态平均共识算法中的隐私保护
DOI：
--
发表时间：
2018
期刊：
Proceedings of the American Control Conference
影响因子：
--
作者：
Rezazadeh, Navid;Kia, Solmaz S.
通讯作者：
Kia, Solmaz S.