98 research outputs found
Secure and Privacy-Preserving Average Consensus
Average consensus is fundamental for distributed systems since it underpins
key functionalities of such systems ranging from distributed information
fusion, decision-making, to decentralized control. In order to reach an
agreement, existing average consensus algorithms require each agent to exchange
explicit state information with its neighbors. This leads to the disclosure of
private state information, which is undesirable in cases where privacy is of
concern. In this paper, we propose a novel approach that enables secure and
privacy-preserving average consensus in a decentralized architecture in the
absence of any trusted third-parties. By leveraging homomorphic cryptography,
our approach can guarantee consensus to the exact value in a deterministic
manner. The proposed approach is light-weight in computation and communication,
and applicable to time-varying interaction topology cases. A hardware
implementation is presented to demonstrate the capability of our approach.Comment: 7 pages, 4 figures, paper is accepted to CPS-SPC'1
Vector-valued Privacy-Preserving Average Consensus
Achieving average consensus without disclosing sensitive information can be a
critical concern for multi-agent coordination. This paper examines
privacy-preserving average consensus (PPAC) for vector-valued multi-agent
networks. In particular, a set of agents with vector-valued states aim to
collaboratively reach an exact average consensus of their initial states, while
each agent's initial state cannot be disclosed to other agents. We show that
the vector-valued PPAC problem can be solved via associated matrix-weighted
networks with the higher-dimensional agent state. Specifically, a novel
distributed vector-valued PPAC algorithm is proposed by lifting the agent-state
to higher-dimensional space and designing the associated matrix-weighted
network with dynamic, low-rank, positive semi-definite coupling matrices to
both conceal the vector-valued agent state and guarantee that the multi-agent
network asymptotically converges to the average consensus. Essentially, the
convergence analysis can be transformed into the average consensus problem on
switching matrix-weighted networks. We show that the exact average consensus
can be guaranteed and the initial agents' states can be kept private if each
agent has at least one "legitimate" neighbor. The algorithm, involving only
basic matrix operations, is computationally more efficient than
cryptography-based approaches and can be implemented in a fully distributed
manner without relying on a third party. Numerical simulation is provided to
illustrate the effectiveness of the proposed algorithm
A dynamical approach to privacy preserving average consensus
In this paper we propose a novel method for achieving average consensus in a
continuous-time multiagent network while avoiding to disclose the initial
states of the individual agents. In order to achieve privacy protection of the
state variables, we introduce maps, called output masks, which alter the value
of the states before transmitting them. These output masks are local (i.e.,
implemented independently by each agent), deterministic, time-varying and
converging asymptotically to the true state. The resulting masked system is
also time-varying and has the original (unmasked) system as its limit system.
It is shown in the paper that the masked system has the original average
consensus value as a global attractor. However, in order to preserve privacy,
it cannot share an equilibrium point with the unmasked system, meaning that in
the masked system the global attractor cannot be also stable.Comment: 19 pages, 2 figures (minor changes w.r.t. previous version
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