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1. Privacy preservation in a continuous-time static average consensus algorithm over directed graphs

   Rezazadeh, Navid; Kia, Solmaz S. (June 2018, Proceedings of the American Control Conference)

   In this paper, we study the problem of privacy preservation of the continuous-time Laplacian static average consensus algorithm using additive perturbation signals. We consider this problem over a strongly connected and weight-balanced digraph. Starting from a local reference value, in static average consensus algorithm each agent constantly communicates with its neighboring agents to update its local state to compute the average of the reference values across the network. Since every agent transmits its local reference value to its in-neighbors, the reference value of the agents are trivially disclosed. In this paper, we investigate the possibility of preserving the privacy of the reference value of the agents by adding admissible perturbation signals to the local dynamics and the transmitted out signals of the agents. Admissible additive perturbation signals are those signals that do not perturb the final convergence point of the algorithm from the average of the reference values of the agents. Our results show that if an adversarial agent has access to the output of another agent and all the input signals transmitted to that agent, the adversary can discover the private reference value of that agent, regardless of the perturbation signals. Otherwise, the privacy of the agent can be preserved. We demonstrate our results through a numerical example. 

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2. PrivOpt: an intrinsically private distributed optimization algorithm

   https://doi.org/10.23919/ACC53348.2022.9867222  

   Esteki, Amir-Salar; Kia, Solmaz S. (June 2022, 2022 American Control Conference (ACC))

   A critical factor for expanding the adoption of networked solutions is ensuring local data privacy of in-network agents implementing a distributed algorithm. In this paper, we consider privacy preservation in the distributed optimization problem in the sense that local cost parameters should not be revealed. Current approaches to privacy preservation normally propose methods that sacrifice exact convergence or increase communication overhead. We propose PrivOpt, an intrinsically private distributed optimization algorithm that converges exponentially fast without any convergence error or using extra communication channels. We show that when the number of the parameters of the local cost is greater than the dimension of the decision variable of the problem, no malicious agent, even if it has access to all transmitted-in and -out messages in the network, can obtain local cost parameters of other agents. As an application study, we show how our proposed PrivOpt algorithm can be used to solve an optimal resource allocation problem with the guarantees that the local cost parameters of all the agents stay private. 

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3. Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning

   https://doi.org/10.1109/CDC49753.2023.10384184  

   Gohari, Parham; Hale, Matthew; Topcu, Ufuk (December 2023, Proceedings of the 62nd IEEE Conference on Decision and Control (CDC))

   In cooperative multi-agent reinforcement learning (Co-MARL), a team of agents must jointly optimize the team's longterm rewards to learn a designated task. Optimizing rewards as a team often requires inter-agent communication and data sharing, leading to potential privacy implications. We assume privacy considerations prohibit the agents from sharing their environment interaction data. Accordingly, we propose Privacy-Engineered Value Decomposition Networks (PE-VDN), a Co-MARL algorithm that models multi-agent coordination while provably safeguarding the confidentiality of the agents' environment interaction data. We integrate three privacy-engineering techniques to redesign the data flows of the VDN algorithm-an existing Co-MARL algorithm that consolidates the agents' environment interaction data to train a central controller that models multi-agent coordination-and develop PE-VDN. In the first technique, we design a distributed computation scheme that eliminates Vanilla VDN's dependency on sharing environment interaction data. Then, we utilize a privacy-preserving multi-party computation protocol to guar-antee that the data flows of the distributed computation scheme do not pose new privacy risks. Finally, we enforce differential privacy to preempt inference threats against the agents' training data-past environment interactions-when they take actions based on their neural network predictions. We implement PE-VDN in StarCraft Multi-Agent Competition (SMAC) and show that it achieves 80% of Vanilla VDN's win rate while maintaining differential privacy levels that provide meaningful privacy guarantees. The results demonstrate that PE-VDN can safeguard the confidentiality of agents' environment interaction data without sacrificing multi-agent coordination. 

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4. Differentially Private Formation Control

   https://doi.org/10.1109/CDC42340.2020.9303817  

   Hawkins, Calvin; Hale, Matthew (December 2020, Proceedings of the 2020 59th IEEE Conference on Decision and Control (CDC))

   null (Ed.)

   As multi-agent systems proliferate, there is in-creasing demand for coordination protocols that protect agents’ sensitive information while allowing them to collaborate. To help address this need, this paper presents a differentially private formation control framework. Agents’ state trajectories are protected using differential privacy, which is a statistical notion of privacy that protects data by adding noise to it. We provide a private formation control implementation and analyze the impact of privacy upon the system. Specifically, we quantify tradeoffs between privacy level, system performance, and connectedness of the network’s communication topology. These tradeoffs are used to develop guidelines for calibrating privacy in terms of control theoretic quantities, such as steady-state error, without requiring in-depth knowledge of differential privacy. Additional guidelines are also developed for treating privacy levels and network topologies as design parameters to tune the network’s performance. Simulation results illustrate these tradeoffs and show that strict privacy is inherently compatible with strong system performance. 

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5. Finite-time Guarantees for Byzantine-resilient Distributed State Estimation with Noisy Measurements

   https://doi.org/10.1109/TAC.2019.2951686  

   Su, L.; Shahrampour, S. (January 2021, IEEE transactions on automatic control)

   null (Ed.)

   This article considers resilient cooperative state estimation in unreliable multiagent networks. A network of agents aim to collaboratively estimate the value of an unknown vector parameter, while an unknown subset of agents suffer Byzantine faults. We refer to the faulty agents as Byzantine agents. Byzantine agents malfunction arbitrarily and may send out highly unstructured messages to other agents in the network. As opposed to fault-free networks, reaching agreement in the presence of Byzantine agents is far from trivial. In this article, we propose a computationally efficient algorithm that is provably robust to Byzantine agents. At each iteration of the algorithm, a good agent performs a gradient descent update based on noisy local measurements, exchanges its update with other agents in its neighborhood, and robustly aggregates the received messages using coordinate-wise trimmed means. Under mild technical assumptions, we establish that good agents learn the true parameter asymptotically in almost sure sense. We further complement our analysis by proving (high probability) finite-time convergence rate, encapsulating network characteristics. 

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