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比特币赌博地址列表

[英]List of addresses for gambling in Bitcoin

I'd like to analyze the gambling activities in Bitcoin. 我想分析比特币中的赌博活动。

Does anyone has a list of addresses for gambling services such as SatoshiDICE and LuckyBit? 有没有人提供赌博服务的地址列表,例如SatoshiDICE和LuckyBit? For example, I found addresses of SatoshiDICE here. 例如,我在这里找到SatoshiDICE的地址。 https://www.satoshidice.com/Bets.php https://www.satoshidice.com/Bets.php

My suggestion would be to go and look for a list of popular addresses, ie, addresses that received and/or sent a lot of transactions. 我的建议是去查找常用地址列表,即接收和/或发送大量交易的地址。 Most gambling sites will use vanity addresses that include part of the site's name in the address, so you might also just search in the addresses for similar patterns. 大多数赌博网站都会使用虚荣地址,该地址中包含网站名称的一部分,因此您也可以只在地址中搜索类似的图案。

It's rather easy to build such a list using Rusty Russell's bitcoin-iterate if you have a synced full node: 如果您有一个已同步的完整节点,使用Rusty Russell的bitcoin-iterate构建这样的列表相当容易:

bitcoin-iterate --output "%os" -q > outputscripts.csv

This will get you a list of all output scripts in confirmed transactions in the blockchain. 这将为您提供区块链中已确认交易中所有输出脚本的列表。 The output scripts include the pubkey hash that is also encoded in the address. 输出脚本包括也已编码在地址中的pubkey哈希。 Let's keep only the P2PKH scripts of the form 76a914<pubkey-hash>88ac 我们只保留形式为76a914<pubkey-hash>88ac的P2PKH脚本

grep -E '^76a914.*88ac$' outputscripts.csv > p2pkhoutputs.csv

Just for reference, the 90.03% (484715631/538368714) of outputs are to P2PKH scripts, so we should be getting pretty accurate results. 仅供参考,P2PKH脚本的输出占90.03%(484715631/538368714),因此我们应该得到相当准确的结果。 So let's get a count for each outputscript and count its occurence: 因此,让我们对每个输出脚本进行计数并计算其出现次数:

sort p2pkhoutputs.csv | uniq -c | sort -g > uniqoutputscripts.csv

And finally let's convert the scripts to the addresses. 最后,让我们将脚本转换为地址。 We'll need to do the base58 encoding, and I chose the python base58 library: 我们需要进行base58编码,我选择了python base58库:

from base58 import b58encode_check 从base58导入b58encode_check

def script2address(s):
    h = s.decode('hex')[3:23]
    h = chr(0) + h
    return b58encode_check(h)

For details on how addresses are generated please refer to the Bitcoin wiki . 有关如何生成地址的详细信息,请参考Bitcoin Wiki And here we have the top 10 addresses sorted by incoming transactions: 这是我们按照传入交易排序的前10个地址:

1880739, 1NxaBCFQwejSZbQfWcYNwgqML5wWoE3rK4
1601154, 1dice8EMZmqKvrGE4Qc9bUFf9PX3xaYDp
1194169, 1LuckyR1fFHEsXYyx5QK4UFzv3PEAepPMK
1105378, 1dice97ECuByXAvqXpaYzSaQuPVvrtmz6
595846, 1dice9wcMu5hLF4g81u8nioL5mmSHTApw
437631, 1dice7fUkz5h4z2wPc1wLMPWgB5mDwKDx
405960, 1MPxhNkSzeTNTHSZAibMaS8HS1esmUL1ne
395661, 1dice7W2AicHosf5EL3GFDUVga7TgtPFn
383849, 1LuckyY9fRzcJre7aou7ZhWVXktxjjBb9S

As you can see SatishiDice and LuckyBit are very much present in the set. 正如您所看到的,SatishiDice和LuckyBit在集合中非常多。 Grepping for the vanity addresses unearths a lot of addresses too. 抢夺虚荣地址也发掘了很多地址。

I would suggest using the usual chain analysis approach: send money to these services and note the addresses. 我建议使用通常的连锁分析方法:汇款至这些服务并记下地址。 Then perform transitive, symmetric etc closures on the same in the blockchain transaction graph to get all addresses in their wallet. 然后在区块链交易图中的同一对象上执行传递,对称等闭包,以获取其钱包中的所有地址。

No technique can determine addresses in a wallet of the user is intelligent enough to mix properly. 没有任何一种技术可以确定用户钱包中的地址是否足够智能以进行正确混合。

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