How We Collected Bitcoin/Ethereum Data and Tested Miner Collusion

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Abstract/zusammenfassung

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Recognition

Chapter 1: Introduction

1. Introduction

   1.1 General -Idesa of thesis contributions

   1.2 Thesis Outline

Chapter 2: Background

2.1 Blockchain and Smart Contracts

2.2 Transaction Prioritization Standards

2.3 Transaction Prioritization and Contention Transparency

2.4 Decentralized Management

2.5 Blockchain Scalability with Layer 2.0 solutions

Chapter 3. Transaction prioritization standards

3. Transaction prioritization standards

   3.1 methods

   3.2 Study of compliance with the standard

   3.3 Investigation of violations of the standard

   3.4 Dark transactions

   3.5 ending reminders

Chapter 4. Transaction Prioritization and Contention Transparency

4. Transaction prioritization and dispute transparency

   4.1 Procedure

   4.2 In the transparency of dispute

   4.3 In Prioritization Transparency

   4.4 Finishing reminders

Chapter 5. Decentralized Management

5. Decentralized management

   5.1 methods

   5.2 Attitude Attitude

   5.3 Compound management

   5.4 Finishing comments

Chapter 6. Related Tasks

6.1 Transaction Prioritization Standards

6.2 Transaction Prioritization and Contention Transparency

6.3 Decentralized Management

Chapter 7. Discussion, limitations and future labor

7.1 ordering transaction

7.2 Transaction Transaction

7.3 Distribution of Voting Power to Change Intelligent Contracts

Conclusion

Appendices

Appendix A: Further Analysis of Prioritization Standards of Transactions

Appendix B: Further Analysis of Prioritization and Contention Transparency Transactions

Appendix C: Further Analysis of Voting Power Distribution

Bibliography

4.1 Procedure

In this section, we outline our method for evaluating the lack of transparency in the dispute of transaction and prioritization. First, we provide a general meal of our Bitcoin and Ethereum data sets used in our review. Subsequently, we offer detailed information about the active experiments used to assess the treatment of transactions and the combined power of miners.

4.1.1 Data Set Collection

Set of data D. To determine the accelerated transactions, we gathered all Bitcoin blocks mined from January 1 St 2018 to December 31st 2020. In total, each table 4.1, there were 161,954 blocks from the height of Block 501,951 to 663,904, and 313,575,387 transactions. In Bitcoin, mining pools may indicate their inhabitants of the block by incorporating a signature or marker into the Coinbase transaction (i.e., the first transaction of each block). We used such markers for mining recognition (owned) of each block following procedures from the previous job (Judmayer et al., 2017; Messias et al., 2021; Romiti et al., 2019). We failed to recognize, however, those with -owned 4911 blocks (approximately 3% of the blocks) and combined these blocks under the “unknown” label. Figure 4.1A shows the number of mined blocks and transactions confirmed by top-20 mining pools. We even removed 65,902,514 (21.02%) child-Pays-for-Parent (CPFP) transactions from our acceleration studies. If we have ranked MPOs by the number of blocks (B) mined (which is essentially estimated to be the hashing h of MPO's hashing H), the top five MPO is BTC.com (b: 27,534; H: 17.00%), F2Pool (B: 20,665; H: 12.76%), Antpool (b: 20,188; h: 12.47%), Poolin (b: 15,096; but, not surprisingly, MPOS hash rates significantly vary in recent years (refer to picture B.1 in §b.3). Moreover, we rely on our SPPE measure to lower if a transaction is likely to accelerate.

Set of data In We gathered all Ethereum blocks mined over a 9-month period-from September 8, 2021 to June 30, 2022-to investigate the behavior of Ethereum mining pools (refer to table 4.1). This data set contains 347,629,393 that released transactions and 1,867,000 blocks (from block number 13,183,000 to 15,049,999). We used miners' purse addresses to lower those with the block, but we failed to identify those with -owned 46,895 blocks (or 2.51% of the total); We combined the latter into a category, “Unknown.” Figure 4.1b shows the distribution of blocks and transactions mined at Ethereum of top-20 mining pools. If we rank mining pools according to the number of blocks (b) mined (estimated to have the hashing H hashing Hashing Hashing Hashing Hashing Hashing is ethymine (b: 523,633; H: 28.05%), F2Pool (b: 299,418; h: 16.04%), Hiveon (b: b: b: 185,495 we also report the hash rate of Ethereum in Figure B.2 in §B.3.

In addition, we also obtained 6,937,292 transactions (2% of all released Ethereum transactions) contained in 3,284,886 bundles from flashbots; These are the transactions that are privately sent to miners.

4.1.2 Publication of public transactions

To determine bundles that are likely to be sent through the P2P public network, we use a simple heuristic. We focus on transaction bundles of size 2 and 3, looking for transactions that probably contributed to a transaction sent to the public and signs of sandwich attacks (Qin et al., 2022).

The underlying idea is that miners do not have the incentive to include transactions that offer zero fees, as there is no reward for mining such transactions – unless they receive additional payment by transferring Coinbase flashbots. As such, transactions with a non-zero max-priority fee are likely to undergo public shipping.

We discuss the details and our results in §4.3.1.

4

Falling into mining pools directly challenges the basic principle of truly decentralized blockchains. For example, when powerful mining pools give preference to certain transactions, and together they have a hash rate of more than 50% of the network, there is no barrier that prevents them from proving verification of other transactions. As a result, they can gain great control over which transactions are included in the blockchain, creating a significant risk of centralization.

To assess the real world of pool mining, we conducted an active experiment within the Bitcoin network. In this experiment, we pay a single pool of mining to accelerate a set of public-low-fee-rate transactions. This is done in times of high congestion in mempool. Without this acceleration, these transactions will face a long delay before being included in the blockchain, due to their low public fees. However, although we only pay a pool of mining to speed up these transactions, our findings have opened up about revelation: These accelerated transactions are also included and preceded within a block of other powerful mining pools. Together -sa, Mining pools hold a hash rate of over 50%. This situation raises significant concerns that return to the entire blockchain ecosystem.

In §4.3.2, we investigate a comprehensive discussion about our findings and implications of pool mining.