Incentive-Compatible Recovery from Manipulated Signals, with Applications to Decentralized Physical Infrastructure

2025-03-10

AI Summary

In DePIN systems, self-interested nodes may strategically misreport unverifiable signals—such as location or bandwidth—undermining integrity. Method: We propose the first formal model capturing implicit signal dependencies between source nodes and observers. Innovatively introducing “source identifiability” as a necessary condition for incentive compatibility, we construct a strictly incentive-compatible mechanism and prove its unique Nash equilibrium under a single honest observer. We further establish the impossibility of collusion-resistant mechanisms in most settings. Our approach integrates game theory, mechanism design, and peer prediction, leveraging geometric tools—including convex hull analysis—to model signal structure. Results: Applied to two canonical DePIN scenarios—proof-of-location and proof-of-bandwidth—the mechanism provides provably honest reporting guarantees. Theoretical analysis ensures both existence and uniqueness of the equilibrium, offering foundational rigor for trustworthy decentralized physical infrastructure networks.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

We introduce the first formal model capturing the elicitation of unverifiable information from a party (the"source") with implicit signals derived by other players (the"observers"). Our model is motivated in part by applications in decentralized physical infrastructure networks (a.k.a."DePIN"), an emerging application domain in which physical services (e.g., sensor information, bandwidth, or energy) are provided at least in part by untrusted and self-interested parties. A key challenge in these signal network applications is verifying the level of service that was actually provided by network participants. We first establish a condition called source identifiability, which we show is necessary for the existence of a mechanism for which truthful signal reporting is a strict equilibrium. For a converse, we build on techniques from peer prediction to show that in every signal network that satisfies the source identifiability condition, there is in fact a strictly truthful mechanism, where truthful signal reporting gives strictly higher total expected payoff than any less informative equilibrium. We furthermore show that this truthful equilibrium is in fact the unique equilibrium of the mechanism if there is positive probability that any one observer is unconditionally honest (e.g., if an observer were run by the network owner). Also, by extending our condition to coalitions, we show that there are generally no collusion-resistant mechanisms in the settings that we consider. We apply our framework and results to two DePIN applications: proving location, and proving bandwidth. In the location-proving setting observers learn (potentially enlarged) Euclidean distances to the source. Here, our condition has an appealing geometric interpretation, implying that the source's location can be truthfully elicited if and only if it is guaranteed to lie inside the convex hull of the observers.

Problem

• Model for eliciting unverifiable information from untrusted sources.
• Ensuring truthful signal reporting in decentralized physical infrastructure networks.
• Addressing collusion resistance in signal network applications.

Innovation

• Introduces source identifiability for truthful signal reporting.
• Uses peer prediction techniques for strictly truthful mechanisms.
• Applies framework to decentralized physical infrastructure networks.