Research Statement

My research interests lie in the design, development, and analysis of Distributed Ledgers. I am open to exploring problems involving the application of game theory and mechanism design in designing such ledgers. My current work can be classified under two topics: Fairness and Game-Theoretic Issues in Blockchains and Designing Scalable Blockchains.

Blockchains from Game theory

Blockchain is a distributed ledger that uses mechanism design to ensure correct functioning. Unlike the Visa Network or centralized banking systems, a blockchain is maintained by a decentralized network of nodes. Instead of using a central authority, it incentivizes participants to run and secure the underlying consensus protocol. Thus, it becomes crucial to ensure that the nodes that maintain the ledger are appropriately incentivized in order to prevent them from behaving maliciously. Our recent work in the WINE Workshop on Game Theory in Blockchain shows that any blockchain protocol can only ensure these miners’ rewards if and only if the protocol offers block rewards (Jain & Gujar, 2020).

In a field where practice seems to be ahead of the theory, researchers are beginning to realize various limitations and issues in existing blockchain protocols. For instance, we found that Bitcoin can face issues in guaranteeing incentives for miners when it would transition to a Transaction-Fee Only Model (Jain & Gujar, 2020).

Fairness and Game-Theoretic Issues in Blockchains

Researchers have discovered various strategies that the nodes participating in a blockchain can adopt to gain more reward than the default strategy. For example, selfish mining is a well-known attack on Bitcoin’s incentive mechanism that allows a strategic miner to reap more than his fair share of block rewards by waiting to publish his blocks until he would cause the most damage to the honest majority (Eyal & Sirer, 2018). Many subsequent papers have explored both attacks on Bitcoin’s incentive mechanism (Carlsten et al., 2016; Eyal, 2015; Judmayer et al., 2019; Liao & Katz, 2017; Nayak et al., 2015; Sapirshtein et al., 2016) as well as other cryptocurrencies (Grunspan & Perez-Marco, 2020; Neuder et al., 2019; Niu & Feng, 2019; Ritz & Zugenmaier, 2018). Often, these attacks not only negatively impact the revenue of other miners but also the security of the blockchain against byzantine adversaries.

In a blockchain protocol, each node must be incentivized to act honestly. To ensure that not only the network of nodes as a whole is provided enough incentives, but each node in the network should also be provided the correct incentive. Hence, the blockchain protocol must be fair to the participants. Research has shown that many widely used blockchain protocols are not fair to miners. In our recent work in AAMAS’21, we established that fairness measures would deteriorate even further in the future as we try to scale the blockchain protocols (Jain et al., 2021).

Scalability in Blockchains

Although cryptocurrencies like Bitcoin and Ethereum are quite popular today, they still lag behind centralized payment systems like Visa in terms of transaction rates and time to finality. As of February 2021, Bitcoin’s and Ethereum’s network processes an average of 3-4 and 10 transactions per second (TPS), respectively. In contrast, Visa’s global payment system handled a reported 1,700 TPS (Visa Inc., n.d.). For a cryptocurrency to be adopted universally, it must be able to scale to process transactions at much higher throughput, i.e., TPS rate. Hence, blockchain protocols must be scalable to be suitable for widespread adoption.

The Bitcoin protocol suffers from a reduction in security against byzantine adversaries as we try to scale the protocol (missing reference). Therefore, it would not be suitable for universal adoption. Towards this, many researchers have proposed blockchain protocols that could scale better than Bitcoin (Yu et al., 2020; Sompolinsky et al., 2016; Sompolinsky et al., 2018; Pass & Shi, 2017; Sompolinsky & Zohar, 2015; Rocket et al., 2019). However, we have still not concluded a perfectly scalable protocol since often these newly proposed protocols suffer from issues such as strategic deviations or network overheads (Yu et al., 2020; Li et al., 2018; Jain et al., 2021).

We are actively working on designing scalable blockchain protocols. One of our works on this was invited for presentation in the WINE Workshop on Game Theory in Blockchain, in which we proposed a novel blockchain protocol (Arora et al., 2020). I am also actively working on designing new blockchain protocols that are not only scalable but also maintain certain fairness properties as they scale.

References

  1. Jain, A., & Gujar, S. (2020). Block Rewards, Not Transaction Fees Keep Miners Faithful In Blockchain Protocols. Workshop on Game Theory in Blockchain at WINE 2020 (GTiB@WINE 2020).
  2. Eyal, I., & Sirer, E. G. (2018). Majority is Not Enough: Bitcoin Mining is Vulnerable. Commun. ACM, 61(7), 95–102. https://doi.org/10.1145/3212998
  3. Carlsten, M., Kalodner, H., Weinberg, S. M., & Narayanan, A. (2016). On the Instability of Bitcoin Without the Block Reward. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 154–167. https://doi.org/10.1145/2976749.2978408
  4. Eyal, I. (2015). The Miner’s Dilemma. Proceedings of the 2015 IEEE Symposium on Security and Privacy, 89–103. https://doi.org/10.1109/SP.2015.13
  5. Judmayer, A., Stifter, N., Zamyatin, A., Tsabary, I., Eyal, I., Gazi, P., Meiklejohn, S., & Weippl, E. (2019). Pay To Win: Cheap, Crowdfundable, Cross-chain Algorithmic Incentive Manipulation Attacks on PoW Cryptocurrencies. Cryptology ePrint Archive, Report 2019/775.
  6. Liao, K., & Katz, J. (2017). Incentivizing blockchain forks via whale transactions. International Conference on Financial Cryptography and Data Security, 264–279.
  7. Nayak, K., Kumar, S., Miller, A., & Shi, E. (2015). Stubborn Mining: Generalizing Selfish Mining and Combining with an Eclipse Attack. Cryptology ePrint Archive, Report 2015/796.
  8. Sapirshtein, A., Sompolinsky, Y., & Zohar, A. (2016). Optimal selfish mining strategies in bitcoin. International Conference on Financial Cryptography and Data Security, 515–532.
  9. Grunspan, C., & Perez-Marco, R. (2020). Selfish Mining in Ethereum. In P. Pardalos, I. Kotsireas, Y. Guo, & W. Knottenbelt (Eds.), Mathematical Research for Blockchain Economy (pp. 65–90). Springer International Publishing.
  10. Neuder, M., Moroz, D. J., Rao, R., & Parkes, D. C. (2019). Selfish Behavior in the Tezos Proof-of-Stake Protocol. CoRR, abs/1912.02954. http://arxiv.org/abs/1912.02954
  11. Niu, J., & Feng, C. (2019). Selfish Mining in Ethereum. CoRR, abs/1901.04620. http://arxiv.org/abs/1901.04620
  12. Ritz, F., & Zugenmaier, A. (2018). The Impact of Uncle Rewards on Selfish Mining in Ethereum. 2018 IEEE European Symposium on Security and Privacy Workshops (EuroS PW), 50–57. https://doi.org/10.1109/EuroSPW.2018.00013
  13. Jain, A., Siddiqui, S., & Gujar, S. (2021). We might walk together, but I run faster: Network Fairness and Scalability in Blockchains. 20th International Conference on Autonomous Agents and Multiagent Systems.
  14. Visa Inc. VisaNet Booklet.
  15. Yu, H., Nikolic, I., Hou, R., & Saxena, P. (2020). OHIE: Blockchain Scaling Made Simple. 2020 IEEE Symposium on Security and Privacy (SP), 112–127. https://doi.org/10.1109/SP.2020.00008
  16. Sompolinsky, Y., Lewenberg, Y., & Zohar, A. (2016). SPECTRE: A Fast and Scalable Cryptocurrency Protocol.
  17. Sompolinsky, Y., Wyborski, S., & Zohar, A. (2018). PHANTOM and GHOSTDAG: A Scalable Generalization of Nakamoto Consensus. Cryptology ePrint Archive, Report 2018/104.
  18. Pass, R., & Shi, E. (2017). Fruitchains: A fair blockchain. Proceedings of the ACM Symposium on Principles of Distributed Computing, 315–324.
  19. Sompolinsky, Y., & Zohar, A. (2015). Secure high-rate transaction processing in bitcoin. International Conference on Financial Cryptography and Data Security, 507–527.
  20. Rocket, T., Yin, M., Sekniqi, K., van Renesse, R., & Sirer, E. G. (2019). Scalable and Probabilistic Leaderless BFT Consensus through Metastability. CoRR, abs/1906.08936. http://arxiv.org/abs/1906.08936
  21. Li, C., Li, P., Zhou, D., Xu, W., Long, F., & Yao, A. (2018). Scaling Nakamoto Consensus to Thousands of Transactions per Second.
  22. Jain, A., Siddiqui, S., & Gujar, S. (2021). We might walk together, but I run faster: Network Fairness and Scalability in Blockchains.
  23. Arora, S., Jain, A., Damle, S., & Gujar, S. (2020). \ ASHWAChain: A Fast, Scalable and Strategy-proof Committee-based Blockchain Protocol. Workshop on Game Theory in Blockchain at WINE 2020 (GTiB@WINE 2020).