I scrolled through your whitepaper and i like the idea a lot! But i still have a few questions which didn't get awnsered by looking at your whitepaper.
Thanks! Let's dive in...
1) The credit ceiling of a seller can be decreased. But how does it exactly get decreased? With a reputation of <3 stars? Or does it need several bad reputations?
The exact mechanism is not made public to prevent manipulation. I can tell you that it would depend on several factors, the primary one being the magnitude of the difference between the expectation that was set with the buyer and the actual result. It also, as you noted, is dependent on our confidence in this assessment (based on historical data).
2) How do you secure yourself (or the buyer) from an exit scam of a seller with a high credit ceiling? Are personal information gathered somehow?
We perform Know Your Customer (KYC) checks on all sellers. This is required by law in many jurisdictions, and also helps prevent some obvious attack vectors (like a Sybil attack). We describe this in detail in the Section 4.3.2 Advance Payment:
An obvious risk against reputation systems is that of a Sybil attack; this is an attack that relies on forging identities in peer-to-peer networks and using them to gain a disproportionately large influence [17, 21, 22]. In the context of the Verify reputation protocol, this would entail a seller registering multiple accounts, performing many “fake transactions” in order to artificially boost his reputation and then, having accumulated a high-enough credit ceiling to make his pursuits worthwhile, withdraw this credit and depart from the platform. At this point, the entire process can be repeated, resulting in further credit theft, and so on.
A critical component of this attack is based on the attacker’s ability to create multiple accounts. An effective way to limit their ability to do so is to require Know Your Customer (or KYC) requirements from sellers -- collecting things like passport information of the principal, business registration and proof of address. Not only is it best-practice to request this information from sellers, but, in many jurisdictions, it is actually required by law to limit certain forms of financial crime like money laundering.
Another dimension to this solution is to make it difficult for an account to accumulate a large credit limit within a short period of time. A treatment of this solution is subtle; it is important to allow legitimate sellers access to credit, in some ways proportional to the transaction volume that they process, while also ensuring that the transactions themselves are legitimate business transactions. Our solution considers both of these aspects. The first facet of this solution is to prevent sellers from accumulating a high reputation in a short period of time through “fake” transactions. Here, we note various signature traits of a transaction (device fingerprint, IP address, source of funds and other patterns) to detect and reject repeated fraudulent transactions originating from a single buyer (or a network of illegitimate buyers). The mechanism used here is similar to the one described in the prior section on Buyer Protection abuse prevention. Further, the reputation calculation mechanism limits what proportion of one’s reputation can originate from a single party. The second facet includes management of the credit ceiling for sellers. Sellers are assigned low credit ceilings, and these are increased only once the seller has resolved any negative balance outstanding from previous credit issuances. This would mean that a seller will not be issued $20 credit if he has not successfully accepted and repaid a $10 credit.
3) In theory a seller could "sell" products to his own accouts to gain a good reputation / high credit ceiling. Did i miss something in the whitepaper here?
Another great question Bob! You're thinking of many different attack vectors that this solution might be susceptible to, and we've got this one covered too. Section 4.3.2 comes to the rescue once more:
The first facet of this solution is to prevent sellers from accumulating a high reputation in a short period of time through “fake” transactions. Here, we note various signature traits of a transaction (device fingerprint, IP address, source of funds and other patterns) to detect and reject repeated fraudulent transactions originating from a single buyer (or a network of illegitimate buyers). The mechanism used here is similar to the one described in the prior section on Buyer Protection abuse prevention. Further, the reputation calculation mechanism limits what proportion of one’s reputation can originate from a single party.
Thanks for taking the time to ask these great questions!
