International Association for Cryptologic Research

International Association
for Cryptologic Research

CryptoDB

Radu Titiu

Publications

Year
Venue
Title
2020
PKC
Adaptive Simulation Security for Inner Product Functional Encryption 📺
Inner product functional encryption ( $${mathsf {IPFE}}$$ ) [ 1 ] is a popular primitive which enables inner product computations on encrypted data. In $${mathsf {IPFE}}$$ , the ciphertext is associated with a vector $$varvec{x}$$ , the secret key is associated with a vector $$varvec{y}$$ and decryption reveals the inner product $$langle varvec{x},varvec{y} angle $$ . Previously, it was known how to achieve adaptive indistinguishability ( $$mathsf {IND}$$ ) based security for $${mathsf {IPFE}}$$ from the $$mathsf {DDH}$$ , $$mathsf {DCR}$$ and $$mathsf {LWE}$$ assumptions [ 8 ]. However, in the stronger simulation ( $$mathsf {SIM}$$ ) based security game, it was only known how to support a restricted adversary that makes all its key requests either before or after seeing the challenge ciphertext, but not both. In more detail, Wee [ 46 ] showed that the $$mathsf {DDH}$$ -based scheme of Agrawal et al. (Crypto 2016) achieves semi-adaptive simulation-based security, where the adversary must make all its key requests after seeing the challenge ciphertext. On the other hand, O’Neill showed that all $$mathsf {IND}$$ -secure $${mathsf {IPFE}}$$ schemes (which may be based on $$mathsf {DDH}$$ , $$mathsf {DCR}$$ and $$mathsf {LWE}$$ ) satisfy $$mathsf {SIM}$$ based security in the restricted model where the adversary makes all its key requests before seeing the challenge ciphertext. In this work, we resolve the question of $$mathsf {SIM}$$ -based security for $${mathsf {IPFE}}$$ by showing that variants of the $${mathsf {IPFE}}$$ constructions by Agrawal et al. , based on $$mathsf {DDH}$$ , Paillier and $$mathsf {LWE}$$ , satisfy the strongest possible adaptive $$mathsf {SIM}$$ -based security where the adversary can make an unbounded number of key requests both before and after seeing the (single) challenge ciphertext. This establishes optimal security of the $${mathsf {IPFE}}$$ schemes, under all hardness assumptions on which it can (presently) be based.
2019
ASIACRYPT
Multi-Client Functional Encryption for Linear Functions in the Standard Model from LWE
Benoît Libert Radu Ţiţiu
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.
2018
TCC
Adaptively Secure Distributed PRFs from $\mathsf {LWE}$
In distributed pseudorandom functions (DPRFs), a PRF secret key SK is secret shared among N servers so that each server can locally compute a partial evaluation of the PRF on some input X. A combiner that collects t partial evaluations can then reconstruct the evaluation F(SK, X) of the PRF under the initial secret key. So far, all non-interactive constructions in the standard model are based on lattice assumptions. One caveat is that they are only known to be secure in the static corruption setting, where the adversary chooses the servers to corrupt at the very beginning of the game, before any evaluation query. In this work, we construct the first fully non-interactive adaptively secure DPRF in the standard model. Our construction is proved secure under the $$\mathsf {LWE}$$ assumption against adversaries that may adaptively decide which servers they want to corrupt. We also extend our construction in order to achieve robustness against malicious adversaries.