Compressible FHE with Applications to PIR

Homomorphic encryption (HE) is often viewed as impractical, both in communication and computation. Here we provide an additively homomorphic encryption scheme based on (ring) LWE with nearly optimal rate ($1-\epsilon$ for any $\epsilon>0$). Moreover, we describe how to compress many FHE ciphertexts that may have come from a homomorphic evaluation (e.g., of the Gentry-Sahai-Waters (GSW) scheme), into fewer high-rate ciphertexts. Using our high-rate HE scheme, we are able for the first time to describe a single-server private information retrieval (PIR) scheme with sufficiently low computational overhead so as to be practical for large databases. Single-server PIR inherently requires the server to perform at least one bit operation per database bit, and we describe a rate-(4/9) scheme with computation which is not so much worse than this inherent lower bound. In fact it is probably faster than whole-database AES encryption -- specifically under 1.8 mod-$q$ multiplication per database byte, where $q$ is about 50 to 60 bits. Asymptotically, the computational overhead of our PIR scheme is \tilde{O}(\log \log \secparam + \log \log \log N), where \secparam is the security parameter and $N$ is the number of database files, which are assumed to be sufficiently large

Code-Based Single-Server Private Information Retrieval: Circumventing the Sub-Query Attack

Private information retrieval from a single server is considered, utilizing random linear codes. Presented is a modified version of the first code-based single-server computational PIR scheme proposed by Holzbaur, Hollanti, and Wachter-Zeh in [Holzbaur et al., "Computational Code-Based Single-Server Private Information Retrieval", 2020 IEEE ISIT]. The original scheme was broken in [Bordage et al., "On the privacy of a code-based single-server computational PIR scheme", Cryptogr. Comm., 2021] by an attack arising from highly probable rank differences in sub-matrices of the user's query. Here, this attack is now circumvented by ensuring that the sub-matrices have negligible rank difference. Furthermore, the rank difference cannot be attributed to the desired file index, thereby ensuring the privacy of the scheme. In the case of retrieving multiple files, the rate of the modified scheme is largely unaffected and at par with the original scheme.Comment: The scheme proposed in this work is a modified version of the scheme in arXiv:2001.07049 (IEEE ISIT 2020) and provides a mend against the attack discovered in arXiv:2004.00509 (Cryptography and Communications, 2021

GPU-based Private Information Retrieval for On-Device Machine Learning Inference

On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on embedding tables that are too large to be stored on-device. In particular, recommendation models typically use multiple embedding tables each on the order of 1-10 GBs of data, making them impractical to store on-device. To overcome this barrier, we propose the use of private information retrieval (PIR) to efficiently and privately retrieve embeddings from servers without sharing any private information. As off-the-shelf PIR algorithms are usually too computationally intensive to directly use for latency-sensitive inference tasks, we 1) propose novel GPU-based acceleration of PIR, and 2) co-design PIR with the downstream ML application to obtain further speedup. Our GPU acceleration strategy improves system throughput by more than $20 \times$ over an optimized CPU PIR implementation, and our PIR-ML co-design provides an over $5 \times$ additional throughput improvement at fixed model quality. Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100,000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy

Limits of Practical Sublinear Secure Computation

Secure computations on big data call for protocols that have sublinear communication complexity in the input length. While fully homomorphic encryption (FHE) provides a general solution to the problem, employing it on a large scale is currently quite far from being practical. This is also the case for secure computation tasks that reduce to weaker forms of FHE such as \u27\u27somewhat homomorphic encryption\u27\u27 or single-server private information retrieval (PIR). Quite unexpectedly, Aggarwal, Mishra, and Pinkas (Eurocrypt 2004), Brickell and Shmatikov (Asiacrypt 2005), and shelat and Venkitasubramaniam (Asiacrypt 2015) have shown that in several natural instances of secure computation on big data, there are practical sublinear communication protocols that only require sublinear local computation and minimize the use of expensive public-key operations. This raises the question of whether similar protocols exist for other natural problems. In this paper we put forward a framework for separating \u27\u27practical\u27\u27 sublinear protocols from \u27\u27impractical\u27\u27 ones, and establish a methodology for identifying \u27\u27provably hard\u27\u27 big-data problems that do not admit practical protocols. This is akin to the use of NP-completeness to separate hard algorithmic problems from easy ones. We show that while the previous protocols of Aggarwal et al., Brickell and Shmatikov, and shelat and Venkitasubramaniam are indeed classified as being \u27\u27practical\u27\u27 in this framework, slight variations of the problems they solve and other natural computational problems on big data are hard. Our negative results are established by showing that the problem at hand is \u27\u27PIR-hard\u27\u27 in the sense that any secure protocol for the problem implies PIR on a large database. This imposes a barrier on the local computational cost of secure protocols for the problem. We also identify a new natural relaxation of PIR that we call semi-PIR, which is useful for establishing \u27\u27intermediate hardness\u27\u27 of several practically motivated secure computation tasks. We show that semi-PIR implies slightly sublinear PIR via an adaptive black-box reduction and that ruling out a stronger black-box reduction would imply a major breakthrough in complexity theory. We also establish information-theoretic separations between semi-PIR and PIR, showing that some problems that we prove to be semi-PIR-hard are not PIR-hard

Complexity Theory

Computational Complexity Theory is the mathematical study of the intrinsic power and limitations of computational resources like time, space, or randomness. The current workshop focused on recent developments in various sub-areas including arithmetic complexity, Boolean complexity, communication complexity, cryptography, probabilistic proof systems, pseudorandomness and randomness extraction. Many of the developments are related to diverse mathematical fields such as algebraic geometry, combinatorial number theory, probability theory, representation theory, and the theory of error-correcting codes

Unbalanced Private Set Intersection from Homomorphic Encryption and Nested Cuckoo Hashing

Private Set Intersection (PSI) is a well-studied secure two-party computation problem in which a client and a server want to compute the intersection of their input sets without revealing additional information to the other party. With this work, we present nested Cuckoo hashing, a novel hashing approach that can be combined with additively homomorphic encryption (AHE) to construct an efficient PSI protocol for unbalanced input sets. We formally prove the security of our protocol against semi-honest adversaries in the standard model. Our protocol yields client computation and communication complexity that is sublinear in the server’s set size and is thus of interest to clients with limited resources. The implementation and empirical evaluation of our protocol using the exponential ElGamal and BGV/BFV encryption schemes attests to state-of-the-art practical performance

Algebraic Restriction Codes and Their Applications

Consider the following problem: You have a device that is supposed to compute a linear combination of its inputs, which are taken from some finite field. However, the device may be faulty and compute arbitrary functions of its inputs. Is it possible to encode the inputs in such a way that only linear functions can be evaluated over the encodings? I.e., learning an arbitrary function of the encodings will not reveal more information about the inputs than a linear combination. In this work, we introduce the notion of algebraic restriction codes (AR codes), which constrain adversaries who might compute any function to computing a linear function. Our main result is an information-theoretic construction AR codes that restrict any class of function with a bounded number of output bits to linear functions. Our construction relies on a seed which is not provided to the adversary. While interesting and natural on its own, we show an application of this notion in cryptography. In particular, we show that AR codes lead to the first construction of rate-1 oblivious transfer with statistical sender security from the Decisional Diffie-Hellman assumption, and the first-ever construction that makes black-box use of cryptography. Previously, such protocols were known only from the LWE assumption, using non-black-box cryptographic techniques. We expect our new notion of AR codes to find further applications, e.g., in the context of non-malleability, in the future

Multi-Client Oblivious RAM with Poly-Logarithmic Communication

Oblivious RAM enables oblivious access to memory in the single-client setting, which may not be the best fit in the network setting. Multi-client oblivious RAM (MCORAM) considers a collaborative but untrusted environment, where a database owner selectively grants read access and write access to different entries of a confidential database to multiple clients. Their access pattern must remain oblivious not only to the server but also to fellow clients. This upgrade rules out many techniques for constructing ORAM, forcing us to pursue new techniques. MCORAM not only provides an alternative solution to private anonymous data access (Eurocrypt 2019) but also serves as a promising building block for equipping oblivious file systems with access control and extending other advanced cryptosystems to the multi-client setting. Despite being a powerful object, the current state-of-the-art is unsatisfactory: The only existing scheme requires $O(\sqrt n)$ communication and client computation for a database of size $n$. Whether it is possible to reduce these complexities to $\mathsf{polylog}(n)$, thereby matching the upper bounds for ORAM, is an open problem, i.e., can we enjoy access control and client-obliviousness under the same bounds? Our first result answers the above question affirmatively by giving a construction from fully homomorphic encryption (FHE). Our main technical innovation is a new technique for cross-key trial evaluation of ciphertexts. We also consider the same question in the setting with $N$ non-colluding servers, out of which at most $t$ of them can be corrupt. We build multi-server MCORAM from distributed point functions (DPF), and propose new constructions of DPF via a virtualization technique with bootstrapping, assuming the existence of homomorphic secret sharing and pseudorandom generators in NC0, which are not known to imply FHE

Interaction-Preserving Compilers for Secure Computation

In this work we consider the following question: What is the cost of security for multi-party protocols? Specifically, given an insecure protocol where parties exchange (in the worst case) ? bits in N rounds, is it possible to design a secure protocol with communication complexity close to ? and N rounds? We systematically study this problem in a variety of settings and we propose solutions based on the intractability of different cryptographic problems. For the case of two parties we design an interaction-preserving compiler where the number of bits exchanged in the secure protocol approaches ? and the number of rounds is exactly N, assuming the hardness of standard problems over lattices. For the more general multi-party case, we obtain the same result assuming either (i) an additional round of interaction or (ii) the existence of extractable witness encryption and succinct non-interactive arguments of knowledge. As a contribution of independent interest, we construct the first multi-key fully homomorphic encryption scheme with message-to-ciphertext ratio (i.e., rate) of 1 - o(1), assuming the hardness of the learning with errors (LWE) problem. We view our work as a support for the claim that, as far as interaction and communication are concerned, one does not need to pay a significant price for security in multi-party protocols