International Association
for Cryptologic Research

We construct somewhat homomorphic encryption from the sparse learning-parities-with-noise problem, along with an assumption that implies linearly homomorphic encryption (e.g., the decisional Diffie-Hellman or decisional composite residuosity assumptions). Our resulting schemes support an a-priori bounded number of homomorphic operations: O(log \lambda/ log log \lambda) multiplications followed by poly(\lambda) additions, where \lambda \in \N is a security parameter. These schemes have compact ciphertexts: before and after homomorphic evaluation, the bit length of each ciphertext is a fixed polynomial in the security parameter \lambda, independent of the number of homomorphic operations that the scheme supports. This gives the first constructions of somewhat homomorphic encryption that can evaluate the class of bounded-degree polynomials without relying on lattice assumptions or bilinear maps. Our new encryption schemes are conceptually simple: much as in Gentry, Sahai, and Waters’ fully homomorphic encryption scheme, ciphertexts in our scheme are matrices, homomorphic addition is matrix addition, and homomorphic multiplication is matrix multiplication. Moreover, when encrypting many messages at once and performing many homomorphic evaluations at once, the bit length of the ciphertexts in (some of) our schemes can be made arbitrarily close to the bit length of the plaintexts. The main limitation of our schemes is that they require a large evaluation key, whose size scales with the complexity of the homomorphic computation performed, though this key can be re-used across any polynomial number of encryptions and evaluations.

We show that under a mild number-theoretic conjecture, recovering an integer from its Jacobi signature modulo $N = p^2 q$, for primes $p$ and $q$, is as hard as factoring $N$. This relates, for the first time, the one-wayness of a pseudorandom generator that Damgård proposed in 1988, to a standard number-theoretic problem. In addition, we show breaking the Jacobi pseudorandom function is no harder than factoring.

Our web search queries reveal sensitive information about us: where we are (“Hikes in Toronto”), how we are feeling (“Causes of neck pain”), what we are doing (“How to find a lawyer”), and much more. Even if we use privacy-conscious search engines, such as DuckDuckGo, the search engine’s servers see our query strings in plaintext. As a result, search engines today accumulate a trove of sensitive data about us; this data is an attractive target for theft in a data breach, abuse by an authoritarian government, or sale to a third party. This talk will present Tiptoe, a search engine that learns nothing about what its users are searching for. With Tiptoe, a client sends only the encryption of its search query to the search engine’s servers. The search engine then executes a cryptographic protocol to identify the web pages that best answer the user’s query—without ever decrypting the query, without learning what the user is searching for, and without learning what search results it is sending back. Tiptoe’s privacy guarantee is based on cryptography alone; it does not require any trusted hardware or non-colluding servers. The Tiptoe search engine answers these queries in the span of seconds: searching over a public web crawl (360 million pages) incurs 57 MiB of client-server communication and 2.7 seconds of client-perceived latency.

This paper introduces arithmetic sketching, an abstraction of a primitive that several previous works use to achieve lightweight, low-communication zero-knowledge verification of secret-shared vectors. An arithmetic sketching scheme for a language L ⊆ F^n consists of (1) a randomized linear function compressing a long input x to a short “sketch,” and (2) a small arithmetic circuit that accepts the sketch if and only if x ∈ L, up to some small error. If the language L has an arithmetic sketching scheme with short sketches, then it is possible to test membership in L using an arithmetic circuit with few multiplication gates. Since multiplications are the dominant cost in protocols for computation on secret-shared, encrypted, and committed data, arithmetic sketching schemes give rise to lightweight protocols in each of these settings. In addition to the formalization of arithmetic sketching, our contributions are: – A general framework for constructing arithmetic sketching schemes from algebraic varieties. This framework unifies schemes from prior work and gives rise to schemes for useful new languages and with improved soundness error. – The first arithmetic sketching schemes for languages of sparse vectors: vectors with bounded Hamming weight, bounded L1 norm, and vectors whose few non-zero values satisfy a given predicate. – A method for “compiling” any arithmetic sketching scheme for a language L into a low-communication malicious-secure multi-server protocol for securely testing that a client-provided secret-shared vector is in L. We also prove the first nontrivial lower bounds showing limits on the sketch size for certain languages (e.g., vectors of Hamming-weight one) and proving the non-existence of arithmetic sketching schemes for others (e.g., the language of all vectors that contain a specific value).

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.

We introduce and study the notion of fully linear probabilistically checkable proof systems. In such a proof system, the verifier can make a small number of linear queries that apply jointly to the input and a proof vector.Our new type of proof system is motivated by applications in which the input statement is not fully available to any single verifier, but can still be efficiently accessed via linear queries. This situation arises in scenarios where the input is partitioned or secret-shared between two or more parties, or alternatively is encoded using an additively homomorphic encryption or commitment scheme. This setting appears in the context of secure messaging platforms, verifiable outsourced computation, PIR writing, private computation of aggregate statistics, and secure multiparty computation (MPC). In all these applications, there is a need for fully linear proof systems with short proofs.While several efficient constructions of fully linear proof systems are implicit in the interactive proofs literature, many questions about their complexity are open. We present several new constructions of fully linear zero-knowledge proof systems with sublinear proof size for “simple” or “structured” languages. For example, in the non-interactive setting of fully linear PCPs, we show how to prove that an input vector $$x\in {\mathbb {F}}^n$$, for a finite field $${\mathbb {F}}$$, satisfies a single degree-2 equation with a proof of size $$O(\sqrt{n})$$ and $$O(\sqrt{n})$$ linear queries, which we show to be optimal. More generally, for languages that can be recognized by systems of constant-degree equations, we can reduce the proof size to $$O(\log n)$$ at the cost of $$O(\log n)$$ rounds of interaction.We use our new proof systems to construct new short zero-knowledge proofs on distributed and secret-shared data. These proofs can be used to improve the performance of the example systems mentioned above.Finally, we observe that zero-knowledge proofs on distributed data provide a general-purpose tool for protecting MPC protocols against malicious parties. Applying our short fully linear PCPs to “natural” MPC protocols in the honest-majority setting, we can achieve unconditional protection against malicious parties with sublinear additive communication cost. We use this to improve the communication complexity of recent honest-majority MPC protocols. For instance, using any pseudorandom generator, we obtain a 3-party protocol for Boolean circuits in which the amortized communication cost is only one bit per AND gate per party (compared to 10 bits in the best previous protocol), matching the best known protocols for semi-honest parties.

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.