A Key Derivation Function (KDF) generates a uniform and highly random key-stream from weakly random key material. KDFs are broadly used in various security protocols such as digital signatures and key exchange protocols. HKDF, the most deployed KDF in practice, is based on the extract-then-expand paradigm. It is presently used, among others, in the Signal Protocol for end-to-end encrypted messaging.

IoT devices collect privacy-sensitive data, e.g., in smart grids or in medical devices, and send this data to cloud servers for further processing. In order to ensure confidentiality as well as authenticity of the sensor data in the untrusted cloud environment, we consider a transciphering scenario between embedded IoT devices and multiple cloud servers that perform secure multi-party computation (MPC). Concretely, the IoT devices encrypt their data with a lightweight symmetric cipher and send the ciphertext to the cloud servers. To obtain the secret shares of the cleartext message for further processing, the cloud servers engage in an MPC protocol to decrypt the ciphertext in a distributed manner. This way, the plaintext is never exposed to the individual servers.