Current estimates suggest that a little less than one million bitcoins have been stolen since the emergence of the cryptocurrency. As of today, there are less than 17 million total bitcoins in circulation. The currency is presently limited to 21 million ever to be produced.
Some estimate that as much as 25% of the currency has simply been lost due to negligence or data loss.
This leaves us with a familiar set of problems to fiat currencies with some interesting new twists.
First, the inventory of a stable currency needs to be managed. In the world of fiat currencies, a central bank of some type would govern the creation and decommissioning of currency notes. This can serve two purposes. Paper currency needs maintenance so old notes can be replaced with new ones. The average US currency note stays in circulation for a short 20 months.
Second, it provides a monetary control as to when new currency should be added to the system. Aside from controversial topics like monetary easing and inflation, this system provides the government with an actionable method for controlling the influx of new currency.
In the cryptoeconomics world, this problem is, while not simplified, ignored for a course engineering solution. Bitcoins specifically uses the ‘proof of work’ algorithm to reward miners for propagating the network by releasing new currency on a programmatic basis. This serves to introduce new currency into the market.
Given the caps on how much currency will be released, this gives the bitcoin community a runway to either release more currency or move to a ‘proof at stake’ or alternative algorithm to propagate the network after this threshold has been reached, or reduced mining profitability reduces the number of miners.
The larger problem is when currency goes bad. That is, it is stolen or used for illicit purposes. As an example, there have been studies that show 80%+ of US currency shows traces of cocaine. That is not to say that these bills have all been used in illegal or illicit transactions, but that money which has is put back into the system and is laundered with other money. If all money is a little dirty, no money is too dirty. While not exactly true, the point is that there is no clear way to devalue currency once it has been used for an illegal transaction. Even the banks try to do this with dye packs. As the bank robber is running out the door, these packs explode tainting the currency, and the perpetrator with an indelible dye.
So, what is the cryptocurrency equivalent of solving this problem?
On December 7, NiceHash was hacked. If you’re unfamiliar, NiceHash was the one of the largest marketplaces where currency miners could pair up with coins or buyers wanting to buy hash power. They had a hot wallet where they temporarily stored the payouts of their miners. In all, the wallet had about 4700 bitcoins in it. Around 1 a.m., a hacker used a employee’s credentials to log into their VPN and transferred the contents of their wallet to another account.
For days, the stolen funds sat there, untouched. As the crypto community started monitoring their lost wages, quite a few people started paying attention. Recently, the money is moving again.
So, here is the question. Is this stolen currency any less valuable? Should it be inferred that anyone using this currency was involved in the hack?
A few weeks ago, I wrote a post on Risk Anchors. The concept is that there are knowable things about pieces of data in the cryptocurrency world, as well as on the open internet. Risk Anchors presents the idea that risk can be inferred by relationships that one has to these objects.
As a simple example, the NiceHash hack released thousands of bitcoins into a very specific bitcoin account, that is very traceable. If someone asked you to trade goods or services, in essence something more fungible than stolen bitcoins, and they were using the bitcoin account with the stolen coins to pay for it, would you be at least curious if you were receiving stolen funds?
In essence, the goal of a Risk Anchor is to extend existing Know Your Customer laws by inference into a distributed ledger world and create standardized risk models for computing overall risk.
To bring maturity and stability to the cryptoeconomic world, there needs to be an increased level of predictability and measured risk. Knowing the reputation of your customer, and where their money is coming from is one of many potential steps.