Turning Lemons into Peaches using Secure Computation

TL;DR

This paper addresses the challenge of verifying goods' quality when testing is computationally intensive, proposing secure computation techniques to prevent over-fitting and ensure honest assessments.

Contribution

It introduces a novel approach using secure computation to hide quality tests from sellers, preventing over-fitting and improving market transparency.

Findings

Secure computation can effectively hide tests from sellers.

Preventing over-fitting improves the reliability of quality assessments.

The method enhances market fairness by ensuring truthful quality reporting.

Abstract

In many cases, assessing the quality of goods is hard. For example, when purchasing a car, it is hard to measure how pollutant the car is since there are infinitely many driving conditions to be tested. Typically, these situations are considered under the umbrella of information asymmetry and as Akelrof showed may lead to a market of lemons. However, we argue that in many of these situations, the problem is not the missing information but the computational challenge of obtaining it. In a nut-shell, if verifying the value of goods requires a large amount of computation or even infinite amounts of computation, the buyer is forced to use a finite test that samples, in some sense, the quality of the goods. However, if the seller knows the test, then the seller can over-fit the test and create goods that pass the quality test despite not having the desired quality. We show different solutions to this situation including a novel approach that uses secure computation to hide the test from the seller to prevent over-fitting.