Adversarial attacks aim to manipulate the input data or model parameters to cause the machine learning model to make incorrect predictions or behave in an unintended manner.

Evasion attacks involve modifying the input data to cause the machine learning model to make incorrect predictions, while preserving the integrity of the data.

Data poisoning involves intentionally introducing errors or malicious data into the training dataset to compromise the performance or integrity of the machine learning model.

Data sanitization involves removing or correcting errors and malicious data from the training dataset before training the machine learning model.

Model extraction involves recovering the model parameters or architecture of a trained machine learning model, often without access to the original training data.

Obfuscation involves modifying the model parameters or architecture to make it more difficult to extract or interpret.

Privacy-preserving machine learning involves developing machine learning algorithms and techniques that protect the privacy of the data used for training, while still allowing the model to learn effectively.

Differential privacy is a mathematical framework that provides a rigorous definition of privacy for machine learning algorithms, ensuring that the output of the algorithm does not reveal sensitive information about any individual data point.

Federated learning allows multiple devices or organizations to train a shared machine learning model without sharing their individual data, preserving data privacy.

Federated learning faces challenges such as communication overhead due to data transfer between devices or organizations, data heterogeneity due to different data distributions across devices or organizations, and model aggregation to combine the updates from different devices or organizations.

Homomorphic encryption allows computations to be performed on encrypted data without decrypting it, enabling secure machine learning on encrypted data.

Homomorphic encryption faces limitations such as high computational cost, limited precision due to the noise introduced during encryption, and difficulty in implementing complex operations due to the mathematical constraints of homomorphic encryption schemes.

Secure multi-party computation allows multiple parties to jointly train a machine learning model without revealing their individual data, preserving data privacy.

Secure multi-party computation faces challenges such as communication overhead due to the need for secure communication between parties, computational overhead due to the complex cryptographic operations, and scalability issues as the number of parties or data size increases.

Best practices for securing machine learning models and systems include regularly monitoring and updating the model, implementing security controls to protect the data and model from unauthorized access, and using robust authentication and authorization mechanisms to control access to the model and data.