Model and data security
Note the following peculiarities for model and data security:
- Authentication: Authentication is the process of verifying a user’s identity to ensure that only authorized individuals can access high-stakes ML systems. Examples of authentication methods include login credentials, multi-factor authentication, and biometrics. By implementing strong authentication mechanisms, organizations can prevent unauthorized access and reduce the risk of malicious activities.
- Interpretable, fair, or private models: Interpretable models are designed to be more transparent and easier to understand, making them simpler to debug and secure. Fair models aim to minimize bias and ensure equitable treatment for all users, reducing potential legal and reputational risks. Private models protect sensitive data, often using privacy-preserving techniques such as differential privacy. By prioritizing accuracy, interpretability, fairness, and privacy in modeling techniques...
