Encryption

Encryption is a fundamental security measure in AI, particularly when dealing with sensitive data used for training, inference, or storage. It protects data from unauthorized access, whether during transmission or at rest. There are two main types of encryption: symmetric and asymmetric. Symmetric encryption uses the same key for both encryption and decryption, making it faster but requiring secure key exchange. Examples include AES and DES. Asymmetric encryption (also known as public-key cryptography) uses a pair of keys: a public key for encryption and a private key for decryption. The public key can be shared, while the private key must be kept secret. Examples include RSA and ECC. In AI, encryption is used to protect training datasets (especially in federated learning), secure API communications, and ensure the privacy of user data used by AI models. Homomorphic encryption is a more advanced form allowing computations to be performed directly on ciphertext, without decryption, further enhancing data privacy in AI applications.