International Association
for Cryptologic Research

Anonymous transfer, recently introduced by Agrikola, Couteau and Maier [ACM22] (TCC '22), allows a sender to leak a message anonymously by participating in a public non-anonymous discussion where everyone knows who said what. This opens up the intriguing possibility of using cryptography to ensure strong anonymity guarantees in a seemingly non-anonymous environment. The work of [ACM22] presented a lower bound on anonymous transfer, ruling out constructions with strong anonymity guarantees (where the adversary's advantage in identifying the sender is negligible) against arbitrary polynomial-time adversaries. They also provided a (heuristic) upper bound, giving a scheme with weak anonymity guarantees (the adversary's advantage in identifying the sender is inverse in the number of rounds) against fine-grained adversaries whose run-time is bounded by some fixed polynomial that exceeds the run-time of the honest users. This leaves a large gap between the lower bound and the upper bound, raising the intriguing possibility that one may be able to achieve weak anonymity against arbitrary polynomial time adversaries, or strong anonymity against fine grained adversaries. In this work, we present improved lower bounds on anonymous transfer, that rule out both of the above possibilities: - We rule out the existence of anonymous transfer with any non-trivial anonymity guarantees against general polynomial time adversaries. - Even if we restrict ourselves to fine-grained adversaries whose run-time is essentially equivalent to that of the honest parties, we cannot achieve strong anonymity, or even quantitatively improve over the inverse polynomial anonymity guarantees (heuristically) achieved by [ACM22]. Consequently, constructions of anonymous transfer can only provide security against fine-grained adversaries, and even in that case they achieve at most weak quantitative forms of anonymity.

Property-preserving hashing (PPH) consists of a family of compressing hash functions $h$ such that, for any two inputs $x,y$, we can correctly identify whether some property $P(x,y)$ holds given only the digests $h(x),h(y)$. In a basic PPH, correctness should hold with overwhelming probability over the choice of $h$ when $x,y$ are worst-case values chosen a-priori and independently of $h$. In an adversarially robust PPH (RPPH), correctness must hold even when $x,y$ are chosen adversarially and adaptively depending on $h$. Here, we study (R)PPH for the property that the Hamming distance between $x$ and $y$ is at most $t$. The notion of (R)PPH was introduced by Boyle, LaVigne and Vaikuntanathan (ITCS '19), and further studied by Fleischhacker, Simkin (Eurocrypt '21) and Fleischhacker, Larsen, Simkin (Eurocrypt '22). In this work, we obtain improved constructions that are conceptually simpler, have nearly optimal parameters, and rely on more general assumptions than prior works. Our results are: * We construct information-theoretic non-robust PPH for Hamming distance via syndrome list-decoding of linear error-correcting codes. We provide a lower bound showing that this construction is essentially optimal. * We make the above construction robust with little additional overhead, by relying on homomorphic collision-resistant hash functions, which can be constructed from either the discrete-logarithm or the short-integer-solution assumptions. The resulting RPPH achieves improved compression compared to prior constructions, and is nearly optimal. * We also show an alternate construction of RPPH for Hamming distance under the minimal assumption that standard collision-resistant hash functions exist. The compression is slightly worse than our optimized construction using homomorphic collision-resistance, but essentially matches the prior state of the art constructions from specific algebraic assumptions. * Lastly, we study a new notion of randomized robust PPH (R2P2H) for Hamming distance, which relaxes RPPH by allowing the hashing algorithm itself to be randomized. We give an information-theoretic construction with optimal parameters.