Secure multi-party consensus gossip algorithms

Lazzeretti, Riccardo; Horn, Steven; Braca, Paolo; Willett, Peter

doi:10.1109/icassp.2014.6855039

Cited by 28 publications

(26 citation statements)

References 32 publications

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“…In this subsection, we consider the nonparametric case where the underlying sensor distributions are unknown. We perform simulations to provide insights into the performance of our proposed EPIC approach in (18).…”

Section: B Simulation Resultsmentioning

confidence: 99%

On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks

Sun

Tay

2020

IEEE Trans.Inform.Forensic Secur.

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In a decentralized Internet of Things (IoT) network, a fusion center receives information from multiple sensors to infer a public hypothesis of interest. To prevent the fusion center from abusing the sensor information, each sensor sanitizes its local observation using a local privacy mapping, which is designed to achieve both inference privacy of a private hypothesis and data privacy of the sensor raw observations. Various inference and data privacy metrics have been proposed in the literature. We introduce the concept of privacy implication (with vanishing budget) to study the relationships between these privacy metrics. We propose an optimization framework in which both local differential privacy (data privacy) and information privacy (inference privacy) metrics are incorporated. In the parametric case where sensor observations' distributions are known a priori, we propose a two-stage local privacy mapping at each sensor, and show that such an architecture is able to achieve information privacy and local differential privacy to within the predefined budgets. For the nonparametric case where sensor distributions are unknown, we adopt an empirical optimization approach. Simulation and experiment results demonstrate that our proposed approaches allow the fusion center to accurately infer the public hypothesis while protecting both inference and data privacy.

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Section: B Simulation Resultsmentioning

confidence: 99%

On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks

Sun

Tay

2020

IEEE Trans.Inform.Forensic Secur.

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“…Therefore this single connection configuration should always be avoided, which is also required by other noise-based privacy protocols, for instance in [6] and [9].…”

Section: Ea Ebmentioning

confidence: 99%

“…Different from existing noise-based privacy-preserving approaches which can only achieve average consensus in the statistic case, our approach can guarantee convergence to the exact average value in a deterministic manner. Unlike the existing cryptography based average consensus approach in [9], this approach allows every participating nodes to access the exact final value. Furthermore, the approach is completely decentralized and light-weight in computation, which makes it easily applicable to resource restricted systems.…”

Section: Introductionmentioning

confidence: 99%

Secure and Privacy-Preserving Average Consensus

Ruan

Ahmad

Wang

2017

Proceedings of the 2017 Workshop on Cyber-Physical Systems Security and PrivaCy

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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.

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“…If the agents' inputs are integers (bounded), privacy in distributed average consensus can be achieved by relying on (information-theoretic) distributed secure multi-party computation protocols [12] or homomorphic encryption-based average consensus [10], [13]. In this paper, we are interested in real-valued inputs with known bound, as several applications of distributed average consensus such as distributed Kalman filtering [4], formation control [14] and distributed learning [15]-deal with real-valued agents' inputs.…”

Section: Introductionmentioning

confidence: 99%

Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs

Gupta

Kat

Chopra

2019

2019 American Control Conference (ACC)

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This paper proposes a privacy protocol for distributed average consensus algorithms on bounded real-valued inputs that guarantees statistical privacy of honest agents' inputs against colluding (passive adversarial) agents, if the set of colluding agents is not a vertex cut in the underlying communication network. This implies that privacy of agents' inputs is preserved against t number of arbitrary colluding agents if the connectivity of the communication network is at least (t+1). A similar privacy protocol has been proposed for the case of bounded integral inputs in our previous paper [1]. However, many applications of distributed consensus concerning distributed control or state estimation deal with real-valued inputs. Thus, in this paper we propose an extension of the privacy protocol in [1], for bounded real-valued agents' inputs, where bounds are known apriori to all the agents.

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Secure multi-party consensus gossip algorithms

Cited by 28 publications

References 32 publications

On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks

On the Relationship Between Inference and Data Privacy in Decentralized IoT Networks

Secure and Privacy-Preserving Average Consensus

Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs

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