Now That’s What I Call Crypto: 10 Years of The Best of Bitcoin

On January 3rd, 2009, the Genesis Block was created. This was the first entry on the Bitcoin blockchain. Because of the nature of Bitcoin, all transactions lead back to this block. This is where Bitcoin began, almost exactly ten years ago.

The Genesis Block was created by Satoshi, a person or persons we know nothing about. In the decade since, we’ve seen the astonishing rise and meteoric descent of Bitcoin, and then it happened again after the bubble was re-inflated.

Due to the nature of Bitcoins, blockchains, and ledgers, the entire history of Bitcoin has been recorded. Every coin spent and every satoshi scrupled has been recorded for all to see. It’s time for a retrospective, and not just because I wanted to see some art based on the covers of Now That’s What I Call Music albums. No, ten years is a lot of stories to tell.

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BitCluster Brings a New Way to Snoop Through BitCoin Transactions

Mining the wealth of information in the BitCoin blockchain is nothing new, but BitCluster goes a long way to make sense of the information you’ll find there. The tool was released by Mathieu Lavoie and David Decary-Hetu, PH.D. on Friday following their talk at HOPE XI.

I greatly enjoyed sitting in on the talk which began with some BitCoin basics. The cryptocurrency uses user generated “wallets” which are essentially addresses that identify transactions. Each is established using key pairs and there are roughly 146 million of these wallets in existence now

If you’re a thrifty person you might think you can get one wallet and use it for years. That might be true of the sweaty alligator-skin nightmare you’ve had in your back pocket for a decade now. It’s not true when it comes to digital bits —  they’re cheap (some would say free). People who don’t generate a new wallet for every transaction weaken their BitCoin anonymity and this weakness is the core of BitCluster’s approach.

Every time you transfer BitCoin (BTC) you send the network the address of the transaction when you acquired the BTCs and sign it with your key to validate the data. If you reuse the same wallet address on subsequent transactions — maybe because you didn’t spend all of the wallet’s coins in one transaction or you overpaid and have the change routed back to your wallet. The uniqueness of that signed address can be tracked across those multiple transactions. This alone won’t dox you, but does allow a clever piece of software to build a database of nodes by associating transactions together.

Mathieu’s description of first attempts at mapping the blockchain were amusing. The demonstration showed a Python script called from the command line which started off analyzing a little more than a block a second but by the fourth or fifth blocks hit the process had slowed to a standstill that would never progress. This reminds me of some of the puzzles from Project Euler.

bitcluster-how-it-worksAfter a rabbit hole of optimizations the problem has been solved. All you need to recreate the work is a pair of machines (one for Python one for mondoDB) with the fastest processors you can afford, a 500 GB SSD, 32 GB of RAM (but would be 64 better), Python 64-bit, and at least a week of time. The good news is that you don’t have to recreate this. The 200GB database is available for download through a torrent and the code to navigate it is up on GitHub. Like I said, this type of blockchain sleuthing isn’t new but a powerful open source tool like this is.

Both Ransomware and illicit markets can be observed using this technique. Successful, yet not-so-cautious ransomers sometimes use the same BitCoin address for all payments. For example, research into a 2014 data sample turned up a ransomware instance that pulled in $611k (averaging $10k per day but actually pulling in most of the money during one three-week period). If you’re paying attention you know using the same wallet address is a bad move and this ransomware was eventually shut down.

Illicit markets like Silk Road are another application for BitCluster. Prior research methods relied on mining comments left by customers to estimate revenue. Imagine if you had to guess at how well Amazon was doing reading customer reviews and hoping they mentioned the price? The ability to observe BTC payment nodes is a much more powerful method.

A good illicit market won’t use just one wallet address. But to protect customers they use escrow address and these do get reused making cluster analysis possible. Silk Road was doing about $800k per month in revenue at its height. The bulk of purchases were for less than $500 with only a tiny percentage above $1000. But those large purchases were likely to be drug purchases of a kilo or more. That small sliver of total transactions actually added up to about a third of the total revenue.

bitcluster-logoIt’s fascinating to peer into transactions in this manner. And the good news is that there’s plenty of interesting stuff just waiting to be discovered. After all, the blockchain is a historical record so the data isn’t going anywhere. BitCluster is intriguing and worth playing with. Currently you can search for a BTC address and see total BTC in and out, then sift through income and expense sorted by date, amount, etc. But the tool can be truly great with more development. On the top of the wishlist are automated database updates, labeling of nodes (so you can search “Silk Road” instead of a numerical address), visual graphs of flows, and a hosted version of the query tool (but computing power becomes prohibitive.)

Ask Hackaday: A Robot’s Black Market Shopping Spree

It was bad when kids first started running up cell phone bills with excessive text messaging. Now we’re living in an age where our robots can go off and binge shop on the Silk Road with our hard earned bitcoins. What’s this world coming to? (_sarcasm;)

For their project ‘Random Darknet Shopper’, Swiss artists [Carmen Weisskopf] and [Domagoj Smoljo] developed a computer program that was given 100 dollars in bitcoins and granted permission to lurk on the dark inter-ether and make purchases at its own digression. Once a week, the AI would carrying out a transaction and have the spoils sent back home to its parents in Switzerland. As the random items trickled in, they were photographed and put on display as part of their exhibition, ‘The Darknet. From Memes to Onionland’ at Kunst Halle St. Gallen. The trove of random purchases they received aren’t all illegal, but they will all most definitely get you thinking… which is the point of course. They include everything from a benign Lord of the Rings audio book collection to a knock-off Hungarian passport, as well as the things you’d expect from the black market, like baggies of ecstasy and a stolen Visa credit card. The project is meant to question current sanctions on trade and investigate the world’s reaction to those limitations. In spite of dabbling in a world of questionable ethics and hazy legitimacy, the artists note that of all the purchases made, not a single one of them turned out to be a scam.

Though [Weisskopf] and [Smoljo] aren’t worried about being persecuted for illegal activity, as Swiss law protects their right to freely express ideas publicly through art, the implications behind their exhibition did raise some questions along those lines. If your robot goes out and buys a bounty of crack on its own accord and then gives it to its owner, who is liable for having purchased the crack?

If a collection of code (we’ll loosely use the term AI here) is autonomous, acting independent of its creator’s control, should the creator still be held accountable for their creation’s intent? If the answer is ‘no’ and the AI is responsible for the repercussions, then we’re entering a time when its necessary to address AI as separate liable entities. However, if you can blame something on an AI, this suggests that it in some way has rights…

Before I get ahead of myself though, this whole notion circulates around the idea of intent. Can we assign an artificial form of life with the capacity to have intent?