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 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 for undirected networks, which can enable secure and privacy-preserving average consensus in a decentralized architecture in the absence of an aggregator or third party. By leveraging partial homomorphic cryptography to embed secrecy in pairwise interaction dynamics, our approach can guarantee convergence to the consensus value (subject to a quantization error) in a deterministic manner without disclosing a node's state to its neighbors. We provide a new privacy definition for dynamical systems, and give a new framework to rigorously prove that a node's privacy can be protected as long as it has at least one legitimate neighbor, which follows the consensus protocol faithfully without attempts to infer other nodes’ states. In addition to enabling resilience to passive attackers aiming to steal state information, the approach also allows easy incorporation of defending mechanisms against active attackers who try to alter the content of exchanged messages. Furthermore, in contrast to existing noise-injection-based privacy-preserving mechanisms that have to reconfigure the entire network when the topology or number of nodes varies, our approach is applicable to dynamic environments with time-varying coupling topologies. This secure and privacy-preserving approach is also applicable to weighted average consensus as well as maximum/minimum consensus under a new update rule. Numerical simulations and comparison with existing approaches confirm the theoretical results. Experimental results on a Raspberry-Pi board based microcontroller network are also presented to verify the effectiveness and efficiency of the approach.

Secure and Privacy-Preserving Consensus

Privacy preservation is addressed for decentralized optimization, where $N$ agents cooperatively minimize the sum of $N$ convex functions private to these individual agents. In most existing decentralized optimization approaches, participating agents exchange and disclose states explicitly, which may not be desirable when the states contain sensitive information of individual agents. The problem is more acute when adversaries exist which try to steal information from other participating agents. To address this issue, we propose a privacy-preserving decentralized optimization approach based on alternating direction method of multipliers (ADMM) and partially homomorphic cryptography. To the best of our knowledge, this is the first time that cryptographic techniques are incorporated in a fully decentralized setting to enable privacy preservation in decentralized optimization in the absence of any third party or aggregator. To facilitate the incorporation of encryption in a fully decentralized manner, we introduce a new ADMM, which allows time-varying penalty matrices and rigorously prove that it has a convergence rate of $O(1/t)$ . Numerical and experimental results confirm the effectiveness and low-computational complexity of the proposed approach.

ADMM Based Privacy-Preserving Decentralized Optimization

Detection of Stealthy False Data Injection Attacks Against Networked Control Systems via Active Data Modification

This article considers the resilient consensus problems of discrete-time complex cyber-physical networks under $F$ -local deception attacks. A resilient consensus algorithm, where extreme values received are removed by each node, is first introduced. By utilizing the presented algorithm, a necessary and sufficient condition to ensure resilient consensus in the absence of trusted edges is then provided by means of network robustness. We further generalize the notion of network robustness and present the necessary and sufficient condition for the achievement of resilient consensus in the presence of trusted edges. In addition, we show that through appropriately assigning the trusted edges, the resilient consensus can be reached under arbitrary communication network. Finally, the validity of the theoretical findings is demonstrated by simulation examples.

Resilient Consensus of Discrete-Time Complex Cyber-Physical Networks Under Deception Attacks

Average consensus underpins key functionalities of distributed systems ranging from distributed information fusion, decision-making, distributed optimization, to load balancing and decentralized control. Existing distributed average consensus algorithms require each node to exchange and disclose state information to its neighbors, which is undesirable in cases where the state is private or contains sensitive information. In this paper, we propose a novel approach that avoids disclosing individual state information in average consensus by letting each node decompose its state into 2 substates. For each node, one of the two substates involves in computation and internode interactions as if it were the original state, while the other substate interacts only with the first substate of the same node, being completely invisible to other nodes. The initial values of the two substates are chosen randomly but with their mean fixed to the initial value of the original state, which is key to guarantee convergence to the desired consensus value. In direct contrast to differential-privacy based privacy-preserving average-consensus approaches, which enable privacy by compromising accuracy in the consensus value, the proposed approach can guarantee convergence to the exact desired value without any error. Not only is the proposed approach able to prevent the disclosure of a node's initial state to honest-but-curious neighbors, it can also provide protection against inference by external eavesdroppers able to wiretap communication links. Numerical simulations demonstrate the effectiveness of the approach and its advantages over state-of-the-art counterparts.

Privacy-Preserving Average Consensus via State Decomposition

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.

https://dl.acm.org/doi/pdf/10.1145/3140241.3140243

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.

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 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 an aggregator or third-party. By leveraging partial homomorphic cryptography to embed secrecy in pairwise interaction dynamics, our approach can guarantee consensus to the exact value in a deterministic manner without disclosing a node's state to its neighbors. In addition to enabling resilience to passive attackers aiming to steal state information, the approach also allows easy incorporation of defending mechanisms against active attackers which try to alter the content of exchanged messages. Furthermore, in contrast to existing noise-injection based privacy-preserving mechanisms which have to reconfigure the entire network when the topology or number of nodes varies, our approach is applicable to dynamic environments with time-varying coupling topologies. This secure and privacy-preservation approach is also applicable to weighted average consensus as well as maximum/minimum consensus under a new update rule. The approach is light-weight in computation and communication. Implementation details and numerical examples are provided to demonstrate the capability of our approach.

e16123 Background: Transcatheter arterial chemoembolization (TACE) has been combined with immune checkpoint inhibitor (ICI)-based systematic therapies for unresectable HCC (uHCC) with promising efficacy. However, whether the addition of TACE to the combination of ICI and tyrosine kinase inhibitor (TKI) (ICI+TKI+TACE) is superior to ICI+TKI combination therapy is still not clear. Thus, this study compares the efficacy of ICI+TKI+TACE triple therapy and ICI+TKI doublet therapy in patients with uHCC. Methods: uHCC patients treated with either ICI+TKI+TACE triple therapy or ICI+TKI doublet therapy were retrospectively recruited between January 2016 and December 2021 at Eastern Hepatobiliary Surgery Hospital. The patients from ICI+TKI+TACE group and ICI+TKI group were further subjected to propensity score matching based on the baseline characteristics. The primary outcome was progression-free survival (PFS) in matched patients. The secondary outcomes were overall survival (OS) and objective response rate (ORR). Post-progression survival (PPS) as well as treatment-related adverse events (TRAEs) were also assessed. Results: A total of 120 patients were matched. The median PFS was 8.4 months in ICI+TKI+TACE triple therapy group versus 6.6 months in ICI+TKI doublet therapy group (HR 0.72, 95%CI 0.48-1.08; p=0.115). Similar results were obtained in term of OS (26.9 versus 24.2 months, HR 0.88, 95% CI 0.51-1.52; p=0.670). The ORR in the triple therapy group was comparable with that in the doublet therapy group (16.6% versus 21.6%, p=0.487). Further subgroup analysis for PFS illustrated that patients without previous locoregional treatment (preLRT) (10.5 versus 3.7 months, HR 0.35 [0.16-0.76]; p=0.009), without previous treatment (10.5 versus 3.5 months, HR 0.34 [0.14-0.81]; p=0.015) or treated with lenvatinib (14.8 versus 6.9 months, HR 0.52 [0.31-0.87]; p=0.013) can significantly benefit from triple therapy compared with doublet therapy. A remarkable interaction between treatment and preLRT ( p=0.049) or TKIs-combined ( p=0.005) was also detected in term of PFS. Kaplan-Meier analysis of PPS revealed that patients with post progression treatment displayed significant improved survival in both ICI+TKI+TACE and ICI+TKI groups. The incidence of TRAEs was comparable between two groups. Conclusions: In selected uHCC patients (without preLRT or treated with lenvatinib as combination), the addition of TACE to ICI+TKI combination therapy can remarkably improve PFS.

Minghao Ruan

Papers

Secure and Privacy-Preserving Consensus

Secure and Privacy-Preserving Average Consensus

Secure and Privacy-Preserving Average Consensus

Secure and Privacy-Preserving Consensus

Immune checkpoint inhibitor plus tyrosine kinase inhibitor with or without transarterial chemoembolization for unresectable hepatocellular carcinoma.