International Association
for Cryptologic Research

We construct new private-information-retrieval protocols in the single-server setting. Our schemes allow a client to privately fetch a sequence of database records from a server, while the server answers each query in average time sublinear in the database size. Specifically, we introduce the first single-server private-information-retrieval schemes that have sublinear amortized server time, require sublinear additional storage, and allow the client to make her queries adaptively. Our protocols rely only on standard cryptographic assumptions (decision Diffie-Hellman, quadratic residuosity, learning with errors, etc.). They work by having the client first fetch a small "hint" about the database contents from the server. Generating this hint requires server time linear in the database size. Thereafter, the client can use the hint to make a bounded number of adaptive queries to the server, which the server answers in sublinear time--yielding sublinear amortized cost. Finally, we give lower bounds proving that our most efficient scheme is optimal with respect to the trade-off it achieves between server online time and client storage.

We present the first protocols for private information retrieval that allow fast (sublinear-time) database lookups without increasing the server-side storage requirements. To achieve these efficiency goals, our protocols work in an offline/online model. In an offline phase, which takes place before the client has decided which database bit it wants to read, the client fetches a short string from the servers. In a subsequent online phase, the client can privately retrieve its desired bit of the database by making a second query to the servers. By pushing the bulk of the server-side computation into the offline phase (which is independent of the client’s query), our protocols allow the online phase to complete very quickly—in time sublinear in the size of the database. Our protocols can provide statistical security in the two-server setting and computational security in the single-server setting. Finally, we prove that, in this model, our protocols are optimal in terms of the trade-off they achieve between communication and running time.

An oblivious PRF, or OPRF, is a protocol between a client and a server, where the server has a key $k$ for a secure pseudorandom function $F$, and the client has an input $x$ for the function. At the end of the protocol the client learns $F(k,x)$, and nothing else, and the server learns nothing. An OPRF is verifiable if the client is convinced that the server has evaluated the PRF correctly with respect to a prior commitment to $k$. OPRFs and verifiable OPRFs have numerous applications, such as private-set-intersection protocols, password-based key-exchange protocols, and defense against denial-of-service attacks. Existing OPRF constructions use RSA-, Diffie-Hellman-, and lattice-type assumptions. The first two are not post-quantum secure. In this paper we construct OPRFs and verifiable OPRFs from isogenies. Our main construction uses isogenies of supersingular elliptic curves over $\Fpp$ and tries to adapt the Diffie-Hellman OPRF to that setting. However, a recent attack on supersingular-isogeny systems due to Galbraith~et~al.~[ASIACRYPT 2016] makes this approach difficult to secure. To overcome this attack, and to validate the server's response, we develop two new zero-knowledge protocols that convince each party that its peer has sent valid messages. With these protocols in place, we obtain an OPRF in the SIDH setting and prove its security in the UC framework. Our second construction is an adaptation of the Naor-Reingold PRF to commutative group actions. Combining it with recent constructions of oblivious transfer from isogenies, we obtain an OPRF in the CSIDH setting.

The task of function inversion is central to cryptanalysis: breaking block ciphers, forging signatures, and cracking password hashes are all special cases of the function-inversion problem. In 1980, Hellman showed that it is possible to invert a random function $$f{:}\,[N] \rightarrow [N]$$ in time $$T = \widetilde{O}(N^{2/3})$$ given only $$S = \widetilde{O}(N^{2/3})$$ bits of precomputed advice about f. Hellman’s algorithm is the basis for the popular “Rainbow Tables” technique (Oechslin 2003), which achieves the same asymptotic cost and is widely used in practical cryptanalysis.Is Hellman’s method the best possible algorithm for inverting functions with preprocessed advice? The best known lower bound, due to Yao (1990), shows that $$ST = \widetilde{\Omega }(N)$$, which still admits the possibility of an $$S = T = \widetilde{O}(N^{1/2})$$ attack. There remains a long-standing and vexing gap between Hellman’s $$N^{2/3}$$ upper bound and Yao’s $$N^{1/2}$$ lower bound. Understanding the feasibility of an $$S = T = N^{1/2}$$ algorithm is cryptanalytically relevant since such an algorithm could perform a key-recovery attack on AES-128 in time $$2^{64}$$ using a precomputed table of size $$2^{64}$$.For the past 29 years, there has been no progress either in improving Hellman’s algorithm or in strengthening Yao’s lower bound. In this work, we connect function inversion to problems in other areas of theory to (1) explain why progress may be difficult and (2) explore possible ways forward.Our results are as follows:We show that any improvement on Yao’s lower bound on function-inversion algorithms will imply new lower bounds on depth-two circuits with arbitrary gates. Further, we show that proving strong lower bounds on non-adaptive function-inversion algorithms would imply breakthrough circuit lower bounds on linear-size log-depth circuits.We take first steps towards the study of the injective function-inversion problem, which has manifold cryptographic applications. In particular, we show that improved algorithms for breaking PRGs with preprocessing would give improved algorithms for inverting injective functions with preprocessing.Finally, we show that function inversion is closely related to well-studied problems in communication complexity and data structures. Through these connections we immediately obtain the best known algorithms for problems in these domains.