## CryptoDB

### Paper: Multi-Client Functional Encryption for Linear Functions in the Standard Model from LWE

Authors: Benoît Libert Radu Ţiţiu DOI: 10.1007/978-3-030-34618-8_18 Search ePrint Search Google Multi-client functional encryption (MCFE) allows $\ell$ clients to encrypt ciphertexts $(\mathbf {C}_{t,1},\mathbf {C}_{t,2},\ldots ,\mathbf {C}_{t,\ell })$ under some label. Each client can encrypt his own data $X_i$ for a label t using a private encryption key $\mathsf {ek}_i$ issued by a trusted authority in such a way that, as long as all $\mathbf {C}_{t,i}$ share the same label t, an evaluator endowed with a functional key $\mathsf {dk}_f$ can evaluate $f(X_1,X_2,\ldots ,X_\ell )$ without learning anything else on the underlying plaintexts $X_i$. Functional decryption keys can be derived by the central authority using the master secret key. Under the Decision Diffie-Hellman assumption, Chotard et al. (Asiacrypt 2018) recently described an adaptively secure MCFE scheme for the evaluation of linear functions over the integers. They also gave a decentralized variant (DMCFE) of their scheme which does not rely on a centralized authority, but rather allows encryptors to issue functional secret keys in a distributed manner. While efficient, their constructions both rely on random oracles in their security analysis. In this paper, we build a standard-model MCFE scheme for the same functionality and prove it fully secure under adaptive corruptions. Our proof relies on the Learning-With-Errors ($\mathsf {LWE}$) assumption and does not require the random oracle model. We also provide a decentralized variant of our scheme, which we prove secure in the static corruption setting (but for adaptively chosen messages) under the $\mathsf {LWE}$ assumption.
##### BibTeX
@article{asiacrypt-2019-30072,
title={Multi-Client Functional Encryption for Linear Functions in the Standard Model from LWE},
booktitle={Advances in Cryptology – ASIACRYPT 2019},
series={Advances in Cryptology – ASIACRYPT 2019},
publisher={Springer},
volume={11923},
pages={520-551},
doi={10.1007/978-3-030-34618-8_18},