Preserving Statistical Privacy in Distributed Optimization
Preserving Statistical Privacy in Distributed Optimization
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DOI：
10.1109/lcsys.2020.3005766
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发表时间：
2020-04
影响因子：
3
通讯作者：
Nirupam Gupta;Shripad Gade;N. Chopra;N. Vaidya
中科院分区：
文献类型：
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作者：
Nirupam Gupta;Shripad Gade;N. Chopra;N. Vaidya
我们提出了一种分布式优化协议，该协议可以保护代理的本地成本函数的统计隐私，以对抗破坏网络中某些代理的被动对手。该协议由分布式“零和”混淆协议和标准的非私有分布式优化方法组成，该协议混淆了代理的局部成本函数。我们表明，只要通信网络具有（$t+1$）顶点连接，我们的协议就可以保护代理的本地成本函数的统计隐私，防止被动对手破坏多达$t$任意代理。“零和”混淆协议保留了代理的局部代价函数的和，因此确保了计算解的准确性。
We present a distributed optimization protocol that preserves statistical privacy of agents’ local cost functions against a passive adversary that corrupts some agents in the network. The protocol is a composition of a distributed “zero-sum” obfuscation protocol that obfuscates the agents’ local cost functions, and a standard non-private distributed optimization method. We show that our protocol protects the statistical privacy of the agents’ local cost functions against a passive adversary that corrupts up to $t$ arbitrary agents as long as the communication network has ( $t+1$ )-vertex connectivity. The “zero-sum” obfuscation protocol preserves the sum of the agents’ local cost functions and therefore ensures accuracy of the computed solution.