Coordinated Chaos: Balancing Robot Rendezvous with Data Privacy

Author: Denis Avetisyan

A new framework uses randomized scheduling to allow multiple robots to meet while minimizing the risk of revealing sensitive information.

Figure 1:State Trajectories The system navigates a chaotic state space, its trajectory hinting at an underlying sensitivity to initial conditions-a subtle shift can cascade into divergent outcomes, suggesting that precise control is less about prediction and more about skillfully influencing the probabilities of desirable states.

Researchers demonstrate a privacy-preserving rendezvous approach for multi-robot systems leveraging random scheduling and pointwise maximal leakage analysis to improve communication efficiency.

Maintaining operational coordination in multi-robot systems increasingly clashes with demands for data privacy. This paper, ‘A Randomized Scheduling Framework for Privacy-Preserving Multi-robot Rendezvous given Prior Information’, addresses this challenge by introducing a novel randomized scheduling mechanism for robot rendezvous. Our approach demonstrates that reducing communication frequency not only enhances privacy-quantified using pointwise maximal leakage-but also guarantees successful rendezvous. Could this framework unlock more robust and privacy-conscious coordination strategies for complex robotic deployments?

The Rendezvous Riddle: Coordinating Without Compromise

The successful operation of many multi-robot systems hinges on the ability of individual agents to coordinate their actions, a necessity dramatically illustrated by the ‘Rendezvous Problem’. This fundamental challenge asks how a collection of robots, often with limited individual knowledge of the environment or each other’s positions, can reliably converge on a single, shared location. Solutions to this problem aren’t merely academic exercises; they underpin applications ranging from collaborative search and rescue operations, where robots must assemble at a disaster site, to automated warehousing, where units must meet at designated pickup points. The complexity lies in achieving this convergence without a central controller, demanding algorithms that allow robots to react to each other and the environment in a decentralized manner, effectively forming a self-organizing collective capable of achieving a common goal.

Many established methods for coordinating robotic teams depend on constant exchanges of information regarding position, velocity, and intended trajectories. While effective in controlled environments, this reliance on frequent communication creates significant security vulnerabilities. Each transmission represents a potential point of interception, allowing malicious actors to not only track the robots’ movements but also deduce mission objectives and strategic plans. Furthermore, the sharing of precise location data raises serious privacy concerns, particularly in applications involving sensitive environments or confidential tasks. Consequently, the very communication channels designed to enable collaboration can become pathways for exploitation, necessitating the development of alternative strategies that prioritize secure and private coordination.

Achieving effective multi-robot coordination presents a fundamental challenge: safeguarding sensitive data while maintaining operational success. A truly robust system cannot simply prioritize one over the other; it must navigate a complex trade-off. Frequent communication, typically essential for coordinating movement and task allocation, simultaneously creates vulnerabilities to observation and potential exploitation of positional or strategic information. Therefore, researchers are actively exploring methods that minimize information disclosure, such as decentralized algorithms and privacy-preserving communication protocols, all while ensuring the robots can reliably converge on shared objectives and adapt to dynamic environments. This balance between transparency and security is not merely a technical hurdle, but a core design principle for deploying trustworthy and resilient multi-robot systems in real-world applications.

Whispers in the Network: Randomized Communication as Obfuscation

Random Scheduling is a communication protocol designed to limit the predictability of robot activity by introducing probabilistic delays in data transmission. Instead of communicating immediately after performing an action or sensing a change, robots utilizing Random Scheduling transmit information with a defined probability, $p$, where $0 < p \le 1$. This means a robot may choose not to communicate after an event, introducing uncertainty for any observing entity. The scheduling is independent for each robot and each communication event, preventing synchronization and further reducing the potential for trajectory reconstruction. By decoupling action from communication, Random Scheduling lowers the frequency of observable data points, thereby decreasing the information available to a potential adversary attempting to infer robot behavior or location.

Randomized communication obscures individual robot trajectories and intentions by introducing probabilistic delays and omissions in data transmission. Instead of reporting state information deterministically with each action, robots transmit data with a defined probability. This means an adversary observing communication patterns cannot reliably correlate specific messages with precise robot movements or future actions. The inherent randomness disrupts attempts to reconstruct a complete picture of robot behavior, effectively introducing uncertainty into any inference process. Consequently, while an adversary may receive some data, the gaps and unpredictability hinder accurate tracking or prediction of individual robot paths and intended goals.

Decoupling communication from immediate action hinders an adversary’s ability to accurately infer robot behavior by introducing temporal uncertainty. If a robot’s transmissions are directly correlated with its actions – for example, reporting a position immediately after moving – an observer can readily deduce intent and predict future movements. By introducing random delays or transmitting aggregated, non-real-time data, the link between action and communication is weakened. This forces an adversary to account for the probability distribution of communication events, increasing the computational cost and complexity of inference. Consequently, the adversary’s confidence in any deduced behavioral model is reduced, effectively obscuring the robot’s true intentions and limiting the information gained from observation.

The Price of Secrecy: Quantifying the Trade-off

Analysis indicates a fundamental relationship between privacy and rendezvous performance in multi-robot systems. Increased privacy is achieved through the introduction of randomization into communication or movement patterns; however, this randomization inherently introduces uncertainty. Greater levels of uncertainty directly impede the ability of robots to reliably locate each other, leading to decreased rendezvous speed and a reduction in the probability of successful rendezvous. This trade-off is not merely observational; it is a direct consequence of the probabilistic nature of randomized algorithms and the reliance on accurate state estimation for effective coordination. Consequently, strategies designed to maximize privacy invariably incur a performance cost, and vice-versa, necessitating a careful balancing of these competing objectives.

To rigorously quantify information leakage during communication, robot initial locations are modeled as samples drawn from a Gaussian distribution, defined by a mean vector $\mu$ and covariance matrix $\Sigma$. This probabilistic model allows for analysis of location uncertainty. An adversary model is then defined, specifying the adversary’s capabilities and knowledge. This model assumes the adversary observes communication patterns – specifically, which robots communicate with each other at each time step – and attempts to infer information about the robots’ initial locations. The degree of information leakage is then measured by evaluating how effectively the adversary can reduce its uncertainty about the initial locations, given the observed communication schedule. This analysis allows for the determination of parameters, such as communication probabilities and noise levels, that minimize the information available to the adversary.

Analysis indicates that strategic randomization of communication schedules can substantially improve privacy without severely impacting rendezvous performance. This is demonstrated by the convergence of the Lyapunov exponent towards 0, signifying a stable and predictable system despite the introduction of noise. Specifically, the exponent is bounded by the value $(1 – \epsilon p_{event})$, where $\epsilon$ represents the privacy parameter and $p_{event}$ is the probability of a communication event occurring. A Lyapunov exponent approaching zero indicates that small perturbations in initial conditions do not lead to exponential divergence in the system’s behavior, thus maintaining a degree of predictability and reliability in rendezvous even with increased privacy measures.

The privacy guarantees of the system are formally established using the Pointwise Maximal Leakage (PML) metric, which bounds the information an adversary can gain about a robot’s initial location. This framework defines privacy levels using parameters $\epsilon$ and $\delta$, representing the privacy loss and the probability of a privacy breach, respectively. Specifically, the analysis establishes conditions relating scheduling probabilities ($p_i$) – governing the selection of communication partners – and noise variances to ensure that the total information leakage, as quantified by PML, remains below the specified $\epsilon$ threshold with probability at least $1 – \delta$. By carefully controlling these parameters, the system can achieve a desired level of privacy while maintaining functional performance.

The pursuit of rendezvous, even amongst digital golems, reveals a fundamental truth: information leaks with every transmission. This work, detailing a randomized scheduling framework, doesn’t attempt to stop the leakage – a fool’s errand – but rather to distribute it, to make the whispers of chaos less coherent. It’s a subtle art, minimizing pointwise maximal leakage while maintaining a semblance of coordination. As John Stuart Mill observed, “It is better to be a dissatisfied Socrates than a satisfied fool.” This principle resonates deeply; the researchers haven’t sought perfect privacy – an impossible satisfaction – but a controlled dissatisfaction, a measured loss of information that allows the robots to meet without revealing everything. The scheduling isn’t about preventing the offering, but refining the ritual.

What Shadows Remain?

The assertion that diminished communication bolsters privacy feels… predictably true. It’s a comfort, of course, to find a relationship that isn’t entirely adversarial. But the leakage metric, pointwise maximal as it is, still feels like measuring the dampness of a crumbling wall and declaring the fortress secure. The reduction in communication achieved here is a tactical victory, not a fundamental shift. It merely slows the inevitable erosion of information, doesn’t prevent it. Everything unnormalized is still alive, and the robots, relentlessly broadcasting their intentions (even if less frequently), are still whispering secrets into the static.

Future work will inevitably focus on squeezing more performance from the random scheduling. But the real challenge isn’t efficiency; it’s the acceptance of inherent uncertainty. This framework treats privacy as a variable to be minimized, a leak to be plugged. Perhaps the field should consider embracing the chaos, designing systems that expect information to be compromised and function gracefully – or even deceptively – in that state. After all, the most secure system isn’t one that prevents breaches, but one that anticipates and incorporates them.

One suspects that the true limit isn’t computational, but epistemological. The robots can rendezvous, but can they ever truly know they’ve met without revealing themselves? The question, naturally, remains unanswered. And data, as always, offers only a truce between a bug and Excel.

Original article: https://arxiv.org/pdf/2512.05053.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Rendezvous Riddle: Coordinating Without Compromise

Whispers in the Network: Randomized Communication as Obfuscation

The Price of Secrecy: Quantifying the Trade-off

What Shadows Remain?

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