Characterizing ’s Mining Power Decentralization at a Deeper Level

Liyi Zeng∗§, Yang Chen†§, Shuo Chen†, Xian Zhang†, Zhongxin Guo†, Wei Xu∗, Thomas Moscibroda‡ ∗Institute for Interdisciplinary Information Sciences, Tsinghua University †Microsoft Research ‡Microsoft Azure §Contacts: [email protected], [email protected]

Abstract—For proof-of-work such as Ethereum, than 50% of the total power has grown from several the mining power decentralization is an important discussion hundred to several thousand. Overall, the power is more point in the community. Previous studies mostly focus on the decentralized at the participant level than 4 years ago. aggregated power of the mining pools, neglecting the pool participants who are the source of the pools’ power. In this paper, However, we also find that this number varied signif- we present the first large-scale study of the pool participants icantly over time, which means it requires continuous in Ethereum’s mining pools. Pool participants are not directly tracking. Additionally, as our current data and method- observable because they communicate with their pools via private ology cannot de-anonymize the participants, it’s possible channels. However, they leave “footprints” on chain as they that some participants split themselves into many smaller use Ethereum accounts to anonymously receive rewards from mining pools. For this study, we combine several data sources ones for various reasons, which could make our estima- to identify 62,358,646 pool reward transactions sent by 47 tion inaccurate if not completely off the target. Further pools to their participants over Ethereum’s entire near 5-year study to improve the estimation accuracy is important. history. Our analyses about these transactions reveal interesting • Insight 2: Our study about multi-pool miners, who have insights about three aspects of pool participants: the power decentralization at the participant level, their pool-switching switched pools or participated in multiple pools, shows behavior, and why they participate in pools. Our results provide that they control a significant amount of the total mining a complementary and more balanced view about Ethereum’s power. This suggests that a large share of the mining mining power decentralization at a deeper level. power is not owned by their pools or not loyal to them. Index Terms—, Ethereum, mining power decentral- Even if the top pools could collude to launch a 51% ization, participant. attack (which is a public noticeable event, as we explain in Section II), they might have to factor in the heavy price I.INTRODUCTION to pay if their participants switch to other honest pools. Major blockchains like [1] and Ethereum [2] are • Insight 3: Concerned about the pools’ power, multiple based on Proof-of-Work mining [3]. The decentralization of research groups have developed and published solu- the mining power is a major concern of the community. tions [9]–[11], such as FruitChain [11], to disincentivize Previous studies show that mining pools aggregate most of miners from joining mining pools. They attempt to reduce the mining power [4]–[8]. For example, the combined power the pools’ power concentration by removing a financial of the top 2∼4 pools for Ethereum and Bitcoin, which are benefit, i.e., by making it possible for small-power miners two of the biggest public blockchains, has already exceeded to solo mine and enjoy a frequent and stable stream of the 50% threshold! However, the pool participants, who are rewards. Our empirical evidence about solo-able miners the source of the pools’ power, are largely missing in these indicates that miners participate in pools even when they studies. are powerful enough to get stable rewards by solo mining, In this paper, we conduct the first large scale characteri- so the benefits of pool participation are more than the re- zation of pool participants. Our study focuses on Ethereum, ward stability. Therefore, any solution that disincentivizes which is one of the most popular public blockchains. The pool participation by offering reward stability alone may challenge is that pool participants are not directly observ- not be effective. Researchers need to identify and address able because they communicate with their pools via private other reasons for pool participation. channels. Fortunately, they leave “footprints” on chain as they use Ethereum accounts to anonymously receive rewards from We publish the data set collected for the study1. It contains mining pools. For this study, we combine several data sources 62,358,646 identified pool reward transactions, which are to identify pool reward transactions sent by pools to their sent by 47 pools to their participants over Ethereum’s entire participants. Although these transactions only carry indirect history since 2015. In the most recent 12 months2, the reward and partial information about pool participants, our analyses transactions identified on average cover more than 76% of reveal the following important insights: • Insight 1: Despite the power concentration at the pool 1https://github.com/yangsrc/pool-dataset level, the number of participants required to control more 2From 2019-04-01 to 2020-04-01. the total mining power. Since the data are from the public P1 P2 P3 Ethereum, our results can be independently verified. Mining Pool Tier-2 Participants In addition to releasing a new data set, our contribution includes 1) the first longitudinal and cross-pool analysis of

Ethereum’s pool participants; 2) the first to reveal and doc- O1 O2 S1 ument that mining power decentralization at the participant Tier-1 Mining Pool Solo Operators Miner level might be significantly different from the situation at the pool level; 3) the first attempt to quantify the multi-pool Blockchain and solo-able mining power, which adds to a more complete Miner: O1 Miner: O2 Miner: S1 Miner: O2’ understanding of the fear and solutions about mining pools. TX: O TX: O … 1→P1 TX: … 2→P2 … TX: O1→P1 …

We review the important concepts and background in TX: O1→P2 … TX: O2→P3 TX: O1→P2 Section II. A summary of our methodology and data set … … … Block N Block N+1 Block N+2 Block N+k is presented in Section III. We present our characterization results and findings in Section IV. Section V presents related Fig. 1. Ecosystem of Ethereum mining. Miners (pools or solo miners) benefit from block rewards (“Miner:...”). Pool operators further allocate the block work. Section VI concludes the paper. rewards to pool participants via on-chain transactions (TXs, “TX:...”).

II.BACKGROUND Miners and mining pools. Starting with Bitcoin [1], many operators use the combined mining power of the mining pool popular (Ethereum [2], [12] and participants (denoted as P1, P2 and P3). As shown in the others [13]–[15]) employ proof-of-work (PoW) consensus graph, one participant can join several pools. Pool operators algorithms to maintain a decentralized among peers in a send pool reward transactions (denoted as TX : O1 → P1, P2P network who do not need to trust each other. These PoW TX : O2 → P3 and etc.) to split and distribute the block algorithms incentivize miners, who are peers in the network, rewards to participants based on their estimated mining power to continuously grow a global chain of blocks. Miners are contribution. Pool operators often charge participants a fixed required to compete for each new block by solving a unique fee and have craftily designed reward allocation and payment and computation-heavy puzzle, which is called block mining. policies [17]. In return, miners are allowed to record their account addresses A 51% attack is very noticeable when it is launched. in the field of a block to receive newly generated A 51% attack by top pools can be detected as they have to crypto coins as reward3. The block puzzles are designed in divert more than 51% of the total mining power off the honest such a way that a miner, whose mining power accounts for chain to build a forked chain that grows faster than the honest X% of the combined total in the network, has X% chance to one. After they release the malicious to replace the honest win the competition for each new block. For example, a miner one, both forks of the chain are automatically recorded by the with 1% of the total power is expected to win and receive nodes in the network, which could be independently examined a block reward once every 100 blocks. If 10 such miners after the fact. Even if the victims of the attack do not cry out combine their power to mine collaboratively, they together, loudly for their loss, a significant drop of mining power on with 10% of the total power, can expect to win once every 10 the replaced honest chain can be easily observed. blocks. By splitting the reward for each block, each of them can receive rewards 10 times more frequently (each time one III.METHODOLOGYAND DATA SET tenth of the reward). This practice can also reduce the variance of the interval between two consecutive rewards received by A. Methodology the miners. In the real world, many mining pools are created We study mining pools and pool participants by observing to coordinate such collaborative mining. The miners who the amount, the source and the time they receive block re- join pools are called mining pool participants. People often wards4 and pool rewards respectively. The amount of rewards believe that small-power miners can especially benefit from received is used to estimate the mining power of pools and pool participation [16]. Otherwise, they might have to operate participants [19]. We are interested in the relative mining without an income for a long and uncertain amount of time. power a pool or participant has, which is calculated as a Figure 1 depicts Ethereum’s mining ecosystem, in which percentage of the combined total power in the network. More mining pools play an important role. The operators of mining precisely, given a time interval (e.g., a week, month, year), we pools (denoted as O1, O2) create blocks and set their accounts estimate the average mining power of a pool in that interval as as coinbases to receive rewards (denoted as Miner : O1 the ratio of the mining rewards received by the pool over the and Miner : O2 in the boxes representing blocks). Instead of using their own computation power to solve the block 4Ethereum’s variant of PoW algorithm allows a block to include no more puzzles as what solo miners (denoted as S1) do, the pool than two recent stale blocks as uncles (ommers) to reward their miners and factor in their mining power contribution [18]. Our block reward calculation 3Miners also collect transaction fees if they pack transactions into their includes these uncle (ommer) blocks. The rest of the paper will not explicitly mined blocks. mention this again. total rewards of all the mining pools and solo miners received explored in future work. Our results do not depend on this in the interval. knowledge, unless otherwise noted and addressed. mining rewards received by this pool B. Data Set Collection Ppool = total mining rewards Based on the above methodology and assumptions, we use The average mining power of a pool participant is estimated the following steps to collect the accounts used by pools and similarly: participants and the rewards received by these accounts. pool rewards received by the participant 1) We collect the name of the pools and the accounts they P = participant total mining rewards use by crawling the mining pool database of Ether- scan [27], which is the most widely used blockchain In the rest of the paper, for brevity, we use “mining power” to explorer for Ethereum. refer to the “average mining power in a given time interval”. 2) We collect the accounts of pool participants by using the Mining pools receive rewards by setting the coinbase ac- following steps. count of the blocks they mine as the Ethereum accounts a) From step 1, we get the accounts of the 5 large they own. For transparency, mining pools usually disclose the pools (see Section III-A) whose APIs can be used accounts they use. With this information, it is straightforward to verify participant accounts5. to collect all the rewards received by each of the public mining b) We scan all the Ethereum transactions and collect pools. However, collecting the pool rewards sent out by pools those transactions that are sent out by the accounts to their participants is more challenging, because not all the of the 5 pools to transfer Ethers. transactions that transfer Ethers out of pools’ accounts are c) We collect all the receiver accounts of the trans- for rewarding participants. Some of the transactions might be actions we get from the previous step as candidate used for moving pools’ own cut of the rewards (pool fees) accounts of participants. or even for other unrelated purposes. To identify whether d) We use the candidate accounts to query the APIs an Ether transfer transaction from a pool’s account is for and recognize an account as a participant’s account sending pool rewards to a participant, we need to know as long as it can be verified by any of the pools’ whether the receiver account of the transaction belongs to a APIs. pool participant. A difficulty for us is that pool participants 3) We collect the mining rewards received by all the usually stay anonymous and do not disclose their accounts identified pools and other miners (i.e., unidentified pools as there is no obvious reason for them to do so. Fortunately, and solo miners). we discover a data source that can help us identify a large 4) We collect the pool rewards sent to all the participant ac- number of accounts used by pool participants. Among all counts by scanning all the Ethereum transactions to find the public mining pools, we find that 5 large pools, namely, out those which transfer Ethers from all the identified Ethermine [20], SparkPool [21], NanoPool [22], F2Pool [23], pools to all the identified participant accounts. EthPool [24], provide public APIs which could be used to verify whether a given Ethereum account is owned by their We develop and assemble a cloud-based pipeline to collect, participants. Note that a participant account may be used by store, and process the data set. It consists of the following the participant for other unrelated purposes. We only assume components: a blockchain data collector, which queries an that if a participant account receives an Ether transfer from an Ethereum full node to get all the blocks and transactions and identified pool account (not necessarily the pool whose API is uploads them to a cloud-based data lake in JSON format; a used to identify the account), this particular transfer is for the pool collector, which crawls the Etherscan website to get all pool to send pool rewards to the participant. More explicitly, the publicly known Etheruem mining pools and the accounts if an account A is verified as a participant account by pool P’s they use; a participant collector, which leverages Apache Spark API, we assume that: to scan all the Ethereum transactions stored in the data lake, aggregates the receiver accounts of all pool initiated transac- • Assumption 1: all the Ether transfers from the accounts tions, and verifies the receiver accounts by calling the APIs of of pool P to account A are for distributing pool rewards. the five large pools in a rate-throttled way6; a data analysis and • Assumption 2: all the Ether transfers from the accounts visualization platform, which includes Apache Spark, Python, of other pools to account A are also for these pools to Jupyter Notebook and Matplotlib. Our data set is accessible at distribute their rewards. https://github.com/yangsrc/pool-dataset, allowing reproducing In Section III-C, we will provide evidence to show why the results and further study. we believe that the above two assumptions are mostly true, Our data set (first 9,847,646 blocks) covers Ethereum’s although we do not depend on them being 100% true. For entire history, starting from inception (July 30, 2015) through example, even without Assumption 2, the core findings about the multi-pool participants in Section IV-B are still valid. 5These APIs cannot be used to enumerate all the participant accounts of the The APIs we discovered do not tell us whether two or more pools. They can only be used to verify whether a given account is a participant account or not. accounts belong to the same participant. De-anonymization 6The pools’ web APIs use rate limiting to prevent abuse. 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The top subgraph of Figure 6 change pools is another metric to indicate how stable the shows a participant who migrated twice: it first received relation between pools and their participants is. If such multi- rewards only from DwarfPool, then migrated to NanoPool, pool participants control significant power, it suggests that and then moved again to Ethermine. The bottom one shows dishonest top pools might pay a heavy price if they launched a participant who simultaneously participated in two pools a 51% attack (which is a public noticeable event, as we as it received pool rewards alternatively from NanoPool and explained in Section II), because many participants can easily DwarfPool over a long time. leave these dishonest pools as the public’s trust on them has collapsed. In Figure 7, for each week, we show all the mining power controlled by the multi-pool participants. We find 171,354 such Observation: We identify multi-pool participants by observing participant accounts and they always control a significant part whether their accounts are used to receive pool rewards from of the mining power, once as much as 61.91% of the total in more than one pools. For each of the recognized 980,168 2016. In 2020, they control at least 39% of the network total participant accounts, we count the number of pools from which mining power. Figure 8 shows, for each of the 5 large pools they have received pool rewards. The results are aggregated whose APIs are used to verify participants, the percentage of and shown in Figure 5. We can see that over 17% of the their mining power contributed by the multi-pool participants. accounts receive rewards from more than one pools, ranging We can see that they account for a significant share (at least from 2 pools to 20 pools. Our analyses reveal a wide spectrum 31% since 2019) of the total power controlled by each pool. of participation behaviors including migration of mining power They could significantly weaken these pools if they choose to from one pool to another in one go, simultaneous participation move their power out of these pools. in multiple pools and many variations in between. In Figure 6, 9 In Figure 9, we further quantify what would happen if we present two examples about how participants receive pool all the multi-pool participants in the top 3 pools move their 9The accounts are 0xa06b0fa1384e28b87354b459a5798cdf5b6fa094 and mining power elsewhere. We can see that, should the multi- 0x013fb52a8d412739aae37745db813478ee6f9996. pool participants choose to move their power out of the top i.9 ekymnn oe ftetp3posi ut-olparticipants multi-pool if pools 3 pools. top 3 the the of of out power power mining their Weekly move 9. 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2020-03 2020-03 Discussion: Our results suggest that we should identify other disincentivize pool formation and a multitier reward scheme to incentives when designing protocols to effectively disincen- pay small-power miners more frequently [10]. Luu et. al. pro- tivize pool participation. Admittedly, some possible incentives posed SmartPool, a decentralized pool that improves security might be very difficult to remove because pools do offer good and reduces income variance for participants [9]. Similarly, value to participants. For example, pools take care of partic- Fruitchain is introduced to make the mining reward steady ipants’ operational overhead (e.g., running a well-connected for single miners, to remove the incentive of participating in full node, handling software upgrade), so that participants’ mining pools [11]. Our work suggests that participants join job can be simplified into providing computing power. This and stay in pools for more reasons than getting a more stable is a strong incentive for participants, which may be hard to income. Proposals only focusing on the latter issue might remove in practice. not work as well as expected. On the mining hardware side, researchers find ways, such as memory-hard puzzles, V. RELATED WORK to reduce the performance advantage of ASICs over CPUs In this section, we summarize the related work from the and GPUs, which lowers the entry barrier and investment risk following aspects: to attract more miners. For example, cryptocurrencies such Ethereum. The Ethereum blockchain has been widely studied. as Ethereum, Litecoin, , , and Grin [48] have Many studies [29]–[32] focus on the security of Etherum’s adopted Ethash [18], [49], CryptoNight [50], Equihash smart contracts. Some [33], [34] reveal various attacks at the [51] and Cuckoo cycle [52], respectively. Our findings show p2p level. Other works [35], [36] propose layer-2 protocols to that this does increase decentralization for Ethereum at the improve Ethereum’s scalability and confidentiality. In addition, participant level. researchers also study how to build decentralized applications Empirical study of blockchain. Since blockchain data are (Dapps) on Ethereum [37], [38]. This paper studies the mining public, many studies have looked into the data and made many power decentralization problem from the view of Ethereum’s observations. Meiklejohn et. al. analyze the bitcoin ledger to pool participants and the effectiveness of the existing mitigat- deanonymize addresses [53]. Similarly, Zcash [25], Monero ing proposals. [54], and cross-ledger transactions [55] are analyzed. Chen et. Mining pools/participants. Kroll et. al. theoretically analyzed al. generally study Ethereum ledger data and propose efficient the economy of Bitcoin mining and whether the Bitcoin algorithms to detect DDoS-like attacks [56]. Compared to protocol can survive attacks from Goldfinger-type adversaries [56], our study focuses on the evolving mining powers of [39]. Eyal et. al. proposed selfish mining attack [40] and Ethereum with much more details. Different from the previous block withholding attack [41] that can be employed by mining work which analyzes the ledger data, Kim et. al. collect data pools to gain an unfair advantage. Kwon et. al. proposed from the p2p layer of Ethereum and conduct comprehensive fork after withholding (FAW) attack, which is more efficient analytics [57]. Gencer et. al. also study the p2p network of than selfish mining [42]. Gervais et. al. proposed a framework both bitcoin and ethereum [4]. which can analyze the security and performance of a POW- based blockchain regarding different parameters [3]. For pool VI.CONCLUSIONS participants, Wang et. al. studied the computation power In this paper, we present a large-scale and longitudinal anal- distribution among participants of F2pool in Bitcoin [43]. ysis of Ethereum’s pool participants. To our best knowledge, Lewenberg et. al. have studied the advantage a participant it is the first empirical characterization of the mining power may achieve with pool switching strategies [44]. Belotti et. al. decentralization of Ethereum beneath the surface of pools. We studied the pool-hopping phenomenon in Bitcoin [45]. Our show that a new perspective of the mining power could deepen work conducts a large-scale empirical study on the mining and challenge the current understanding of the decentralization power evolution and mining behaviors of pool participants in problem. We admit that this paper raises more questions than Ethereum. it answers. Our results invite similar comparative studies of the Centralization. Several works have studied mining power pool participants of other major blockchains (e.g., Bitcoin), as centralization in PoW blockchains [4]–[7]. The work done by well as future work that uncovers new data sets or uses new Gencer et.al. [4] shows that the top three Ethereum mining angles to analyze pool participants. pools have a combined 61% mining power of the network, indicating that Ethereum is quite “centralized” at the pool ACKNOWLEDGMENT level. In addition, various attacks related to centralization We would like to show great appreciation to anonymous re- such as collusion attacks [46], [47], are discussed. However, viewers for their insightful comments. This work is supported none of them provides the large-scale and cross-pool study in part by National Natural Science Foundation of China of Ethereum’s pool participants, which turns out to be another (NSFC) Grant 71872094, the Zhongguancun Haihua Institute perspective for evaluating decentralization. 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