# An Overview of AI and Blockchain Integration for Privacy-Preserving

arXiv: https://arxiv.org/abs/2305.03928 · HTML: https://mlsanity.com/paper/2305.03928/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 61 references that are in the map.
- First posted on arXiv 6 May 2023.

## Builds on (most cited first, 10 of 10)

- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [AuthROS: Secure Data Sharing Among Robot Operating Systems Based on Ethereum](https://mlsanity.com/paper/2208.14269/) (2022)
- [Characterizing Erasable Accounts in Ethereum](https://mlsanity.com/paper/2009.00607/) (2020)
- [CLUE: Towards Discovering Locked Cryptocurrencies in Ethereum](https://mlsanity.com/paper/2012.01370/) (2020)
- [A Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain](https://mlsanity.com/paper/1903.08801/) (2019)
- [STAN: Towards Describing Bytecodes of Smart Contract](https://mlsanity.com/paper/2007.09696/) (2020)
- [Security Analysis of DeFi: Vulnerabilities, Attacks and Advances](https://mlsanity.com/paper/2205.09524/) (2022)
- [A Survey of DeFi Security: Challenges and Opportunities](https://mlsanity.com/paper/2206.11821/) (2022)

## Built on by (most cited first, 3 of 3)

- [SCALM: Detecting Bad Practices in Smart Contracts Through LLMs](https://mlsanity.com/paper/2502.04347/) (2025)
- [GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereum](https://mlsanity.com/paper/2405.19971/) (2024)
- [AI-Based Vulnerability Analysis of NFT Smart Contracts](https://mlsanity.com/paper/2504.16113/) (2025)

## Abstract (arXiv)

With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This paper initially presents an overview of AI and blockchain, summarizing their combination along with derived privacy protection technologies. It then explores specific application scenarios in data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity methods. Moreover, the paper evaluates five critical aspects of AI-blockchain-integration privacy protection systems, including authorization management, access control, data protection, network security, and scalability. Furthermore, it analyzes the deficiencies and their actual cause, offering corresponding suggestions. This research also classifies and summarizes privacy protection techniques based on AI-blockchain application scenarios and technical schemes. In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy.