EXTENDING THE SPV CONTRACT CONCEPT WITH PRIVACY GADGETS
DOI: 10.31673/2412-4338.2024.024961
Abstract
The SPV (Simple Payment Verification) contract extends the functionality of Bitcoin by enabling cross-chain operations through trustless synchronization with other blockchain systems. This approach allows users to verify Bitcoin transactions without running a full node by utilizing block headers, making it feasible for use on lightweight devices. The SPV contract enables the proof of transaction confirmations on the Bitcoin mainnet, which can then trigger predefined actions in the destination system. This paper also introduces innovative mechanisms for creating untraceable yet verifiable proofs of actions within the Bitcoin network, thereby enhancing privacy. By leveraging the SPV contract architecture, various use cases such as transaction status verification, cross-chain transactions, and decentralized applications can be efficiently implemented. Additionally, security considerations and optimizations like the use of zk-SNARKs for cost-effective Merkle proof verification are discussed. The paper concludes by exploring the potential of SPV contracts in facilitating trustless cross-chain interoperability and advancing the development of complex, automated financial instruments across blockchain ecosystems.
Keywords: simple payment verification, concept proposal, light node, smart contract, transaction verification, block header, event trigger.
References
[BFT] Breaking bft. quantifying the cost to attack bitcoin and ethereum.
[BTC] Bitcoin whitepaper. https://bitcoin.org/bitcoin.pdf.
[PoW] Pow verification. https://developer.bitcoin.org/devguide/block chain.
[SPV] Svp contracts repository. https://github.com/summa-tx/bitcoin-spv.
1. Kober J, Bagnell JA, Peters J. Reinforcement learning in robotics: A survey. The International
Journal of Robotics Research. 2013. Vol. 32, 1
2. Rybczak, M.; Popowniak, N.; Lazarowska, A. A Survey of Machine Learning Approaches for
Mobile Robot Control. Robotics. 2024, Vol. 13, No. 1. P.12-22.
3. Gao, J.; Ye, W.; Guo, J.; Li, Z. Deep Reinforcement Learning for Indoor Mobile Robot Path
Planning. Sensors 2020. No. 20, 5493.
4. Tai L., Paolo G., Liu M., Virtual-to-real deep reinforcement learning: Continuous control of
mobile robots for mapless navigation, 2017 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS), Vancouver, Canada . 2017, P. 31-36,
5. Zhao Y., Zhang Y., Wang S. A Review of Mobile Robot Path Planning Based on Deep
Reinforcement Learning Algorithm. Journal of Physics: Conference Series, Vol. 2138, International
Conference on Artificial Intelligence and Big Data Applications (ICAIBD 2021) 24-25
6. Wang W., Wu Zh., Luo H., Zhang B. Path Planning Method of Mobile Robot Using Improved
Deep Reinforcement Learning. Journal of Electrical and Computer Engineering, vol. 2022. P. 7.
7. Zheng, J.; Mao, S.; Wu, Z.; Kong, P.; Qiang, H. Improved Path Planning for Indoor Patrol Robot
Based on Deep Reinforcement Learning. Symmetry 2022, No. 14, 132.
8. Xin J., Zhao H., Liu D., Li M., Application of deep reinforcement learning in mobile robot path
planning, Chinese Automation Congress (CAC), Jinan, China, 2017, p. 7112-7116.
9. Low Ee S., Ong P., Cheah K. Ch., Solving the optimal path planning of a mobile robot using
improved Q-learning. Robotics and Autonomous Systems, 2019, Vol. 115, P. 143-161.
10. Jiang Q. Path Planning Method of Mobile Robot Based on Q-learning. Journal of Physics:
Conference Series, International Symposium on Artificial Intelligence and Intelligent Manufacturing
(AIIM 2021) 12/11/2021 - 14/11/2021 Huzhou 2022. Vol. 2181.
11. Khriji L, Touati F, Benhmed K, Al-Yahmedi A. Mobile Robot Navigation Based on Q-Learning
Technique. International Journal of Advanced Robotic Systems. 2011. Vol. 8 No. 1.
12. Singh, R.; Ren, J.; Lin, X. A Review of Deep Reinforcement Learning Algorithms for Mobile
Robot Path Planning. Vehicles. 2023. No. 5, P. 1423-1451.
13. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. Proximal policy optimization
algorithms. 2017. No. 5. P. 56-67.
14. Srikonda S.; Norris W.R.; Nottage D.; Soylemezoglu A. Deep Reinforcement Learning for
Autonomous Dynamic Skid Steer Vehicle Trajectory Tracking. Robotics. 2022, No. 11, P. 95.
15. Xing X., Ding H., Liang Zh., Li B., Yang Zh., Robot path planner based on deep reinforcement
learning and the seeker optimization algorithm, Mechatronics, 2022. Vol. 88. P. 102918
16. Zhang, Y.; Chen, P. Path Planning of a Mobile Robot for a Dynamic Indoor Environment Based
on an SAC-LSTM Algorithm. Sensors 2023. No. 23, P. 9802.
17. Ganenko L. D., Zhebka V. V. Analytical review of issues of navigation of mobile robots in
closed spaces. Telecommunications and information technologies. 2023. No. 3(80). Art. 85-98.
18. Malinov V., Zhebka V., Zolotukhina O., Franchuk T., Chubaievskyi V. Biomining as an
Effective Mechanism for Utilizing the Bioenergy Potential of Processing Enterprises in the Agricultural
Sector. CEUR Workshop Proceedings. 2023, 3421, p. 223–230