Securing sensitive data for long-term storage in the cloud is a challenging problem. Updatable encryption (UE) enables changing the encryption key of encrypted data in the cloud while the plaintext and all versions of the key remain secret from the cloud storage provider, making it an efficient alternative for companies that seek to outsource their data storage.

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.

ZK-SNARKs, a fundamental component of privacyoriented payment systems, identity protocols, or anonymous voting systems, are advanced cryptographic protocols for verifiable computation: modern SNARKs allow to encode the invariants of a program, expressed as an arithmetic circuit, in an appropriate constraint language from which short, zero-knowledge proofs for correct computations can be constructed. One of the most important computations that is run through SNARK systems is the verification of Merkle tree (MT) opening proofs, which relies on the evaluation of a fixed-input-length (FIL) cryptographic compression function over binary MTs. As classical, bit-oriented hash functions like SHA-2 are not compactly representable in SNARK frameworks, Arithmetization-Oriented (AO) cryptographic designs have emerged as an alternative, efficient solution. Today, the majority of AO compression functions are built from permutation-based hashing modes, such as Sponge. While this approach allows cost savings, compared to blockcipher-based modes, as it does not require key-scheduling, AO blockcipher schedulers are often cheap to compute. Furthermore, classical bitoriented cryptography has long studied how to construct provably secure compression functions from blockciphers, following the Preneel-Govaerts-Vandewalle (PGV) framework. The potential efficiency gains together with the strong provable security foundations in the classic setting, motivate the study of AO blockcipher-based compression functions. In this work, we propose AO PGV-LC and PGV-ELC, two AO blockcipher-based FIL compression modes inspired by and extending the classical PGV approach, offering flexible input and output sizes and coming with provable security guarantees in the AO setting. We prove the collision and preimage resistance in the ideal cipher model, and give bounds for collision and opening resistance over MTs of arbitrary arity. We compare experimentally the AO PGV-ELC mode over the HADES blockcipher with its popular and widely adopted Sponge instantiation, POSEIDON, and its improved variant POSEIDON2. Our resulting constructions are up to 3× faster than POSEIDONAND 2× faster than POSEIDON2 in native x86 execution, and up to 50% faster in the Groth16 SNARK framework. Finally, we study the benefits of using MTs of arity wider than two, proposing a new strategy to obtain a compact R1CS constraint system in such case. In fact, by combining an efficient parametrization of the HADES blockcipher over the PGV-ELC mode, together with an optimal choice of the MT arity, we measured an improvement of up to 9× in native MT construction time, and up to 2.5× in proof generation time, compared to POSEIDON over binary MTs.

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.