Abstract:

Data Marketplaces allow a wide range of public and private data providers on the one hand, and data-consuming applications on the other, to interact. They can be used to exchange valuable data relevant to a community, such as data relevant to traffic, road conditions, parking, air quality and other urban internet of things (IoT) applications, in a scalable manner.

Traditionally, online marketplaces use ratings by buyers to help potential consumers identify good quality products; however such rating systems are often easy for sellers to game by paying for flattering ratings and reviews. These problems are even more challenging in data marketplaces due to the possibility of sellers launching Sybil attacks (taking on multiple fake identities) to rate their own products.

Our contribution:

We propose a novel decentralized crypto-economic system to ensure the credibility of reviews. We analyze the incentive mechanism through game-theoretical modeling and show conditions under which the Nash equilibrium policy is for all reviewers to perform the work needed for the review (without guessing at the answer).

The key idea of our proposed system, which can be implemented using decentralized smart contracts, is to have sellers apply for their products to be reviewed, followed by an allocation of products to a select subset of reviewers with credibility.

The reviewer allocation process is randomized and double-blinded to minimize the possibility of collusion with the seller. The reviewers are incentivized through a mechanism that not only provides a reward for reviewing products posted by sellers but also an additional reward for reviewing test products posted by the marketplace.

We also show how the staking mechanism in conjunction with high quality reviews incentivizes sellers to post higher-quality products.