@inproceedings{c9dbdcdce3c2403287bfed049f592e67,
title = "Gossip Algorithms that Preserve Privacy for Distributed Computation Part II: Performance Against Eavesdroppers",
abstract = "We propose gossip algorithms that preserve the sum of network values (and therefore the average), and in the meantime fully protect node privacy even against eavesdroppers possessing the entire information flow and network knowledge. We have shown in Part I of the paper that this type of privacy-preserving gossiping algorithms can be used as a simple encryption step in distributed optimization and computation algorithms. In this Part II, we investigate the underlying network dynamics of the proposed algorithms and present three categories of eavesdroppers. To show the Global Privacy Preservation property of the presented algorithms, we establish some concrete privacy-preservation performance analysis characterized by proving impossibilities for the reconstruction of the node initial values.",
author = "Yang Liu and Junfeng Wu and Manchester, {Ian R.} and Guodong Shi",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 57th IEEE Conference on Decision and Control, CDC 2018 ; Conference date: 17-12-2018 Through 19-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/CDC.2018.8619065",
language = "English",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5346--5351",
booktitle = "2018 IEEE Conference on Decision and Control, CDC 2018",
address = "United States",
}