We introduce a new cryptographic primitive called laconic function evaluation (LFE). Using LFE, Alice can compress a large circuit f into a small digest. Bob can encrypt some data x under this digest in a way that enables Alice to recover f(x) without learning anything else about Bob's data. For the scheme to be laconic, we require that the size of the digest, the run-time of the encryption algorithm and the size of the ciphertext should all be small, much smaller than the circuit-size of f. We construct an LFE scheme for general circuits under the learning with errors (LWE) assumption, where the above parameters only grow polynomially with the depth but not the size of the circuit. We then use LFE to construct secure 2-party and multi-party computation (2PC, MPC) protocols with novel properties: We construct a 2-round 2PC protocol between Alice and Bob with respective inputs xA, xBin which Alice learns the output f(xA, xB) in the second round. This is the first such protocol which is “Bob-optimized”, meaning that Alice does all the work while Bob's computation and the total communication of the protocol are smaller than the size of the circuit f or even Alice's input xA. In contrast, prior solutions based on fully homomorphic encryption are “Alice-optimized”. . We construct an MPC protocol, which allows N parties to securely evaluate a function f(x1, ..., xN) over their respective inputs, where the total amount of computation performed by the parties during the protocol execution is smaller than that of evaluating the function itself! Each party has to individually pre-process the circuit f before the protocol starts and post-process the protocol transcript to recover the output after the protocol ends, and the cost of these steps is larger than the circuit size. However, this gives the first MPC where the computation performed by each party during the actual protocol execution, from the time the first protocol message is sent until the last protocol message is received, is smaller than the circuit size.
We introduce a new cryptographic primitive called laconic function evaluation (LFE). Using LFE, Alice can compress a large circuit f into a small digest. Bob can encrypt some data x under this digest in a way that enables Alice to recover f(x) without learning anything else about Bob's data. For the scheme to be laconic, we require that the size of the digest, the run-time of the encryption algorithm and the size of the ciphertext should all be small, much smaller than the circuit-size of f. We construct an LFE scheme for general circuits under the learning with errors (LWE) assumption, where the above parameters only grow polynomially with the depth but not the size of the circuit. We then use LFE to construct secure 2-party and multi-party computation (2PC, MPC) protocols with novel properties: We construct a 2-round 2PC protocol between Alice and Bob with respective inputs xA, xBin which Alice learns the output f(xA, xB) in the second round. This is the first such protocol which is “Bob-optimized”, meaning that Alice does all the work while Bob's computation and the total communication of the protocol are smaller than the size of the circuit f or even Alice's input xA. In contrast, prior solutions based on fully homomorphic encryption are “Alice-optimized”. . We construct an MPC protocol, which allows N parties to securely evaluate a function f(x1, ..., xN) over their respective inputs, where the total amount of computation performed by the parties during the protocol execution is smaller than that of evaluating the function itself! Each party has to individually pre-process the circuit f before the protocol starts and post-process the protocol transcript to recover the output after the protocol ends, and the cost of these steps is larger than the circuit size. However, this gives the first MPC where the computation performed by each party during the actual protocol execution, from the time the first protocol message is sent until the last protocol message is received, is smaller than the circuit size.
We study the communication complexity of secure function evaluation (SFE). Consider a setting where Alice has a short input xA, Bob has an input xB and we want Bob to learn some function y = f(xA, xB) with large output size. For example, Alice has a small secret decryption key, Bob has a large encrypted database and we want Bob to learn the decrypted data without learning anything else about Alice’s key. In a trivial insecure protocol, Alice can just send her short input xA to Bob. However, all known SFE protocols have communication complexity that scales with size of the output y, which can potentially be much larger. Is such “output-size dependence” inherent in SFE? Surprisingly, we show that output-size dependence can be avoided in the honest-butcurious setting. In particular, using indistinguishability obfuscation (iO) and fully homomorphic encryption (FHE), we construct the first honest-but-curious SFE protocol whose communication complexity only scales with that of the best insecure protocol for evaluating the desired function, independent of the output size. Our construction relies on a novel way of using iO via a new tool that we call a “somewhere statistically binding (SSB) hash”, and which may be of independent interest. On the negative side, we show that output-size dependence is inherent in the fully malicious setting, or even already in an honest-but-deterministic setting, where the corrupted party follows the protocol as specified but fixes its random tape to some deterministic value. Moreover, we show that even in an offline/online protocol, the communication of the online phase must have output-size dependence. This negative result uses an incompressibility argument and it generalizes several recent lower bounds for functional encryption and (reusable) garbled circuits, which follow as simple corollaries of our general theorem.
Development of computing devices with the proliferation of the Internet has prompted enormous opportunities for cooperative computation. These computations could occur between trusted or partially trusted partners, or even between competitors. Secure multi-party computation (MPC) protocols allow two or more parties to collaborate and compute a public functionality using their private inputs without the need for a trusted third-party. However, the generic solutions for MPC are not adequate for some particular cases where the function itself is also sensitive and required to be kept private. Private function evaluation (PFE) is a special case of MPC, where the function to be computed is known by only one party. PFE is useful in several real-life applications where an algorithm or a function itself needs to remain secret for reasons such as protecting intellectual property or security classification level. Recently, designing efficient PFE protocols have been a challenging and attractive task for cryptography researchers. iv In this dissertation, we mainly focus on improving two-party private function evaluation (2PFE) schemes. Our primary goal is enhancing the state-of-the-art by designing secure and cost-efficient 2PFE protocols for both symmetric and asymmetric cryptography based solutions. In this respect, we first aim to improve 2PFE protocols based on (mostly) symmetric cryptographic primitives. We look back at the seminal PFE framework presented by Mohassel and Sadeghian at Eurocrypt'13. We show how to adapt and utilize the well-known half gates garbling technique (Zahur et al., Eurocrypt'15) to their constant round 2PFE scheme. Compared to their scheme, our resulting optimization significantly improves both underlying oblivious extended permutation (OEP) and secure 2-party computation (2PC) protocols, and yields a more than 40% reduction in overall communication cost. We next propose a novel and highly efficient 2PFE scheme based on the decisional Di e-Hellman (DDH) assumption. Our scheme consists of two protocols, one is utilized in the initial execution, and the other is in the subsequent runs. One of the novelties of our scheme over the state-of-the-art is that it results in a significant cost reduction when the same private function is evaluated more than once between the same or varying parties. To the best of our knowledge, this is the most efficient and the first 2PFE scheme that enjoys reusability feature. Our protocols achieve linear communication and computation complexities, and a constant number of rounds which is at most three (depending on the size of the inputs of the party that holds the function).
We propose the notion of succinct oblivious tensor evaluation (OTE), where two parties compute an additive secret sharing of a tensor product of two vectors x λ- y, exchanging two simultaneous messages. Crucially, the size of both messages and of the CRS is independent of the dimension of x. We present a construction of OTE with optimal complexity from the standard learning with errors (LWE) problem. Then we show how this new technical tool enables a host of cryptographic primitives, all with security reducible to LWE, such as: (a) Adaptively secure laconic function evaluation for depth-D functions f:{0, 1}m→{0, 1}λ.," with communication m+λ.,"+D· poly(λ); (b) A trapdoor hash function for all functions; (c) An (optimally) succinct homomorphic secret sharing for all functions; (d) A rate-1/2 laconic oblivious transfer for batch messages, which is best possible. In particular, we obtain the first laconic function evaluation scheme that is adaptively secure from the standard LWE assumption, improving upon Quach, Wee, and Wichs (FOCS 2018). As a key technical ingredient, we introduce a new notion of adaptive lattice encodings, which may be of independent interest.
In a Conditional Disclosure of Secrets (CDS) a verifier V wants to reveal a message m to a prover P conditioned on the fact that x is an accepting instance of some NP-language L. An honest prover (holding the corresponding witness w) always obtains the message m at the end of the interaction. On the other hand, if x ∉ L we require that no PPT P* can learn the message m. We introduce laconic CDS, a two round CDS protocol with optimal computational cost for the verifier V and optimal communication cost. More specifically, the verifier's computation and overall communication grows with poly(|x|; λ; log(T)), where λ is the security parameter and T is the verification time for checking that x ϵ L (given w). We obtain constructions of laconic CDS under standard assumptions, such as CDH or LWE. Laconic CDS serves as a powerful tool for maliciousifying semi-honest protocols while preserving their computational and communication complexities. To substantiate this claim, we consider the setting of non-interactive secure computation: Alice wants to publish a short digest corresponding to a private large input x on her web page such that (possibly many) Bob, with a private input y, can send a short message to Alice allowing her to learn C(x; y) (where C is a public circuit). The protocol must be reusable in the sense that Bob can engage in arbitrarily many executions on the same digest. In this context we obtain the following new implications. 1) UC Secure Bob-optimized 2PC: We obtain a UC secure protocol where Bob's computational cost and the communication cost of the protocol grows with poly(|x|; |y|; λ; d), where d is the depth of the computed circuit C. 2) Malicious Laconic Function Evaluation: Next, we move on to the setting where Alice's input x is large. For this case, UC secure protocols must have communication cost growing with |x|. Thus, with the goal of achieving better efficiency, we consider a weaker notion of malicious security. For this setting, we obtain a protocol for which Bob's computational cost and the communication cost of the protocol grows with poly(|y|; λ; d), where d is the depth of the computed circuit C.
We study the communication complexity of secure function evaluation (SFE). Consider a setting where Alice has a short input χA, Bob has an input χB and we want Bob to learn some function y = f(χA, χB) with large output size. For example, Alice has a small secret decryption key, Bob has a large encrypted database and we want Bob to learn the decrypted data without learning anything else about Alice's key. In a trivial insecure protocol, Alice can just send her short input χA to Bob. However, all known SFE protocols have communication complexity that scales with size of the output y, which can potentially be much larger. Is such 'output-size dependence' inherent in SFE'
Over the last twenty years, lattice-based cryptography has quickly become a central component of modern cryptography. Its most prominent characteristics are (1) its capacity to build cryptographic primitives with powerful functionalities, famously including fully-homomorphic encryption, and (2) its widely-believed resilience against quantum attacks, leading to the currently most prevalent family of candidates for post-quantum cryptography. In this thesis, we further study these two fundamental features of lattice-based cryptography. First, we introduce a new advanced cryptographic primitive, that we call laconic function evaluation, which captures general sender-optimized two-party computation. We build laconic function evaluation from lattice-based assumptions, and show applications to efficient secure multi-party computation. Second, we take a systematic approach to studying post-quantum security of cryptographic protocols. We show that, perhaps surprisingly, post-quantum resilience of a computational assumption does not automatically imply post-quantum security of cryptographic primitives built upon it, including encryption and signature schemes. We do so by exhibiting counter-examples based on lattices. Last, we show that natural interactive protocols from lattices shed light on cryptographic arguments systems, and more specifically the so-called Fiat-Shamir transform. As a byproduct, we uncover a conceptual connection between two families of practical lattice-based signatures.--Author's abstract