TY - GEN
T1 - Gossip Algorithms that Preserve Privacy for Distributed Computation Part I
T2 - 57th IEEE Conference on Decision and Control, CDC 2018
AU - Liu, Yang
AU - Wu, Junfeng
AU - Manchester, Ian R.
AU - Shi, Guodong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Gossip protocols play an important role in disseminating information and solving global tasks over networks in a distributed fashion. In this paper, we propose gossip algorithms that preserve the sum of network states (and therefore the average), while fully protecting node privacy even against eavesdroppers possessing the entire information flow and network knowledge. At each time step, a node is selected to interact with one of its neighbors via deterministic or random gossiping. The selected node generates a random number to replace its current state, and sends to the neighbor the difference between the current state and the random number. On receiving the data from the selected node, the neighbor sets its new state as the sum of its current state and the difference. The algorithms can be used as a simple encryption step in distributed optimization and computation algorithms. In this Part I, we study the output statistics of the proposed algorithms with deterministic edge sequence selection, in addition to the convergence limits and encryption time of their randomized version.
AB - Gossip protocols play an important role in disseminating information and solving global tasks over networks in a distributed fashion. In this paper, we propose gossip algorithms that preserve the sum of network states (and therefore the average), while fully protecting node privacy even against eavesdroppers possessing the entire information flow and network knowledge. At each time step, a node is selected to interact with one of its neighbors via deterministic or random gossiping. The selected node generates a random number to replace its current state, and sends to the neighbor the difference between the current state and the random number. On receiving the data from the selected node, the neighbor sets its new state as the sum of its current state and the difference. The algorithms can be used as a simple encryption step in distributed optimization and computation algorithms. In this Part I, we study the output statistics of the proposed algorithms with deterministic edge sequence selection, in addition to the convergence limits and encryption time of their randomized version.
UR - http://www.scopus.com/inward/record.url?scp=85062190592&partnerID=8YFLogxK
U2 - 10.1109/CDC.2018.8619783
DO - 10.1109/CDC.2018.8619783
M3 - Conference contribution
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 4499
EP - 4504
BT - 2018 IEEE Conference on Decision and Control, CDC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 December 2018 through 19 December 2018
ER -