Proof of Sampling (PoSP) Breakdown


This post originally appeared on the Hyperbolic Medium blog on May 9th, 2024

In the rapidly evolving world of blockchain technology, ensuring the integrity and security of decentralized systems remains a critical challenge. Traditional verification mechanisms often struggle to balance efficiency with robust security, especially in systems that handle complex transactions or data analyses. We introduce Proof of Sampling (PoSP), a sampling based verification protocol, a groundbreaking solution designed to address these challenges head-on.

PoSP introduces a novel approach to verification that leverages sampling techniques to ensure high security without compromising system performance. By utilizing a game-theoretic model known as pure strategy Nash Equilibrium, PoSP encourages all participants to act honestly, enhancing the network’s overall trustworthiness and reliability. This protocol is not just theoretical — it has practical applications ranging from decentralized AI inference to improving blockchain layer 2 solutions, and extending further to enhancing the design of Actively Validated Services (AVS) in EigenLayer, making it a significant advancement in blockchain technology.

Understanding PoSP Through Swiss Bus Ticket Checks

Have you ever wondered how public transport authorities ensure everyone pays their fare, even though ticket inspectors seem to appear so infrequently? Consider the Swiss bus system, where instead of checking every passenger’s ticket on every trip, inspectors conduct random checks. This might raise a question: wouldn’t people take chances if inspections are rare? Interestingly, the answer is no. Here’s why: the random nature of inspections, coupled with the fines for fare evasion, sets up a scenario where the most rational, or optimal, strategy for each passenger is simply to buy a ticket.

Let’s delve into the numbers for a moment. In Zurich, for a local zone trip, the price of a second class adult ticket for a half-hour validity is CHF 2.80. The penalty for being caught first time without a ticket is CHF 102.80. Suppose the probability of a ticket being checked on any given journey is 10%. Under these conditions, if a passenger decides to risk not buying a ticket, the expected cost (the penalty multiplied by the probability of getting caught) would be CHF 10.28 (10% of CHF 102.80), which is more than the ticket price. Therefore, economically and logically, buying a ticket is the most sensible option — the fines make evasion unattractive and compliance the best strategy.

In a similar vein, the PoSP harnesses these principles within decentralized blockchain networks. Instead of verifying every single transaction or data interaction — which would be tremendously taxing on the system — PoSP uses a sampling method. Transactions are randomly selected and verified, drastically reducing the workload and computational demand. Here’s the clever part: the design of the protocol ensures that acting honestly is the most beneficial strategy for all participants, thanks to a game-theoretical concept called the pure strategy Nash Equilibrium. Under this equilibrium, any deviation towards dishonesty becomes less attractive compared to the rewards of maintaining integrity.

So, how does this innovative approach reshape the security and efficiency of decentralized systems? As we dive deeper into the mechanics of PoSP, we’ll see how this protocol not only bolsters network integrity but also aligns seamlessly with the fundamental needs for scalability and efficiency in modern blockchain applications.

How PoSP works

In the ever-evolving world of decentralized system, imagine each node in the network as a participant in a high-stakes game show. Here, accuracy and honesty aren’t just valued; they’re required for the game to continue smoothly. Enter PoSP, a brilliant protocol that ensures every player adheres to the rules in a way that allows each participant to maximize their own utility, without the need for constant oversight from everyone else. It’s like having an invisible referee who occasionally steps in to make sure everything’s on track.

Initial Move. The game begins with one node, known as the asserter, who claims to have computed a given function that helps maintain the ledger’s integrity. But instead of shouting out the solution for all to hear, they seal it in a metaphorical envelope — a cryptographic commitment. This envelope proves they’ve done their homework but keeps the actual answers hidden. It’s then sent off to the orchestrator, who holds the central role of overseeing the fairness of the game. In real application, the orchestrator could be a Layer 1 blockchain.

Random Check. The orchestrator, equipped with a virtual coin, decides whether to trust the asserter outright or to verify the claim further. This decision is random but calculated, ensuring every play is fair but not overly burdensome. If luck is on the asserter’s side, they get rewarded right away for their efforts, and the game moves on.

Initiate Challenges. If the coin flip goes the other way, the orchestrator calls in a select few from the network, known as validators. These validators are like detectives, each working independently to solve the same puzzle as the asserter. They, too, seal their answers in their unique envelopes, which are then collected for examination.

Accept vs. Arbitration. As the orchestrator gathers these sealed answers, a moment of tension ensues. If all the envelopes match, it confirms that the puzzle was solved correctly by everyone, and the asserter, along with the validators, shares the reward. However, if there’s a mismatch, it triggers a detective-like review process where each solution is scrutinized. Honest nodes are rewarded for their accuracy, while any node that tries to cheat the system faces penalties.

This approach leverages the collective power of the network to self-validate its computations efficiently. Under specific conditions, this PoSP achieves what is known in game theory as a pure strategy Nash Equilibrium. It’s a state where all participants in the network are incentivized to act honestly because any other strategy would be less beneficial. In other words, when the system is set up correctly, with rewards and penalties balanced against the probability of being challenged, the most rational and profitable approach for all nodes is to compute the result accurately.

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