Collaborative filtering is a powerful tool used by AMMs to prevent market manipulation and suspicious activity. By sharing information with other AMMs or exchanges, they can:

1. Cross-check trading activity: Verify trades and identify potential manipulation or suspicious activity across multiple platforms.

2. Share intelligence: Exchange information about known manipulators or suspicious actors.

3. Enhance pattern recognition: Combine data to identify patterns and anomalies that may indicate manipulation or suspicious activity.

4. Improve machine learning models: Share data to train and improve machine learning models used to detect manipulation or suspicious activity.

5. Increase transparency: Share information about trading activity and market conditions to increase transparency and prevent manipulation.

6. Enhance market surveillance: Collaborate to monitor the market and detect potential manipulation or suspicious activity in real-time.

7. Implement joint measures: Take coordinated action to prevent manipulation or suspicious activity, such as freezing accounts or rejecting trades.

8. Foster a secure ecosystem: Collaborate to maintain a secure and trustworthy trading environment.

By sharing information and best practices, AMMs and exchanges can stay ahead of potential manipulators and ensure a fair and efficient market for all traders.