International Association
for Cryptologic Research

The Jacobi Symbol is an essential primitive in cryptographic applications such as primality testing, integer factorization, and various encryption schemes. By exploring the interdependencies among modular reductions within the algorithmic loop, we have developed a refined method that significantly enhances computational efficiency. Our optimized algorithm, implemented in the Rust language, achieves a performance increase of 72% over conventional textbook methods and is twice as fast as the previously fastest known Rust implementation.

This work not only provides a detailed analysis of the optimizations but also includes comprehensive benchmark comparisons to illustrate the practical advantages of our methods. Our algorithm is publicly available under an open-source license, promoting further research on foundational cryptographic optimizations.

The problem of minimizing the share size of threshold secret-sharing schemes is a basic research question that has been extensively studied. Ideally, one strives for schemes in which the share size equals the secret size. While this is achievable for large secrets (Shamir, CACM '79), no similar solutions are known for the case of binary, single-bit secrets. Current approaches often rely on so-called ramp secret sharing that achieves a constant share size at the expense of a slight gap between the privacy and the correctness thresholds. In the case of single-bit shares, this leads to a large gap which is typically unacceptable. The possibility of a meaningful notion of secret sharing scheme with 1-bit shares and almost optimal threshold has been left wide open. Of special interest is the case of threshold 0.5, which is motivated by information-theoretic honest-majority secure multiparty computation (MPC).

In this work, we present a new stochastic model for secret-sharing where each party is corrupted by the adversary with probability $p$, independently of the other parties, and correctness and privacy are required to hold with high probability over the choice of the corrupt parties. We present new secret sharing schemes with single-bit shares that tolerate any constant corruption probability $p<0.5$. Our construction is based on a novel connection between such stochastic secret-sharing schemes and error-correcting codes that achieve capacity over the binary erasure channel.

Our schemes are linear and multiplicative. We demonstrate the usefulness of the model by using our new schemes to construct MPC protocols with security against an adversary that passively corrupts an arbitrary subset of $0.499n$ of the parties, where the online communication per party consists of a single bit per AND gate and zero communication per XOR gate. Unlike competing approaches for communication-efficient MPC, our solution is applicable even in a real-time model in which the parties should compute a Boolean circuit whose gates arrive in real-time, one at a time, and are not known in advance.

Fully homomorphic encryption (FHE) schemes enable computations on encrypted data, making them a crucial component of privacy-enhancing technologies. Ducas and Micciancio introduced FHEW (Eurocrypt '15), and Chillotti et al. improved it in TFHE (Asiacrypt '16), both of which provide homomorphic binary (or larger) gate evaluations with fast latency due to their small parameters. However, their evaluation failure probability is highly sensitive to parameter selection, resulting in a limited set of viable parameters and a trade-off between failure probability and runtime.

Recently, Cheon et al. proposed a key recovery attack against FHEW/TFHE schemes based on a new security model for FHE, called IND-CPA-D security, which was first introduced by Li and Micciancio (Eurocrypt '21). To prevent this attack, it is necessary to make the failure probability negligible (e.g., $2^{-128}$). However, due to limited choice parameters, it is forced to use a parameter set with unnecessarily low failure probabilities than needed, causing inefficiencies in runtime.

We propose a new bootstrapping method for FHEW/TFHE, providing a precise balance between runtime and failure probability, and easy to implement. The proposed methods enable the selection of parameter sets that achieve negligible failure probabilities for each desired security level while optimizing runtime.

An adaptor signatures (AS) scheme is an extension of digital signatures that allows the signer to generate a pre-signature for an instance of a hard relation. This pre-signature can later be adapted to a full signature with a corresponding witness. Meanwhile, the signer can extract a witness from both the pre-signature and the signature. AS have recently garnered more attention due to its scalability and interoperability. Dai et al. [INDOCRYPT 2022] proved that AS can be constructed for any NP relation using a generic construction. However, their construction has a shortcoming: the associated witness is exposed by the adapted signature. This flaw poses limits the applications of AS, even in its motivating setting, i.e., blockchain, where the adapted signature is typically uploaded to the blockchain and is public to everyone.

To address this issue, in this work we augment the security definition of AS by a natural property which we call witness hiding. We then prove the existence of AS for any NP relation, assuming the existence of one-way functions. Concretely, we propose a generic construction of witness-hiding AS from signatures and a weak variant of trapdoor commitments, which we term trapdoor commitments with a specific adaptable message. We instantiate the latter based on the Hamiltonian cycle problem. Since the Hamiltonian cycle problem is NP-complete, we can obtain witness hiding adaptor signatures for any NP relation.

A sequential function is, informally speaking, a function $f$ for which a massively parallel adversary cannot compute "substantially" faster than an honest user with limited parallel computation power. Sequential functions form the backbone of many primitives that are extensively used in blockchains such as verifiable delay functions (VDFs) and time-lock puzzles. Despite this widespread practical use, there has been little work studying the complexity or theory of sequential functions.

Our main result is a black-box oracle separation between sequential functions and one-way functions: in particular, we show the existence of an oracle $\mathcal{O}$ that implies a sequential function but not a one-way function. This seems surprising since sequential functions are typically constructed from very strong assumptions that imply one-way functions and also since time-lock puzzles are known to imply one-way functions (Bitansky et al., ITCS '16).

We continue our exploration of the theory of sequential functions. We show that, informally speaking, the decisional, worst-case variant of a certain class of sequential function called a continuous iterative sequential function (CISF) is PSPACE-complete. A CISF is, in a nutshell, a sequential function $f$ that can be written in the form $f \left(k, x \right) = g^{k} \left(x \right)$ for some function $g$ where $k$ is an input determining the number of "rounds" the function is evaluated. We then show that more general forms of sequential functions are not contained in PSPACE relative to a random oracle.

Given these results, we then ask if it is possible to build any interesting cryptographic primitives from sequential functions that are not one-way. It turns out that even if we assume just the existence of a CISF that is not one-way, we can build certain "fine-grained" cryptographic primitives where security is defined similarly to traditional primitives with the exception that it is only guaranteed for some (generally polynomial) amount of time. In particular, we show how to build "fine-grained" symmetric key encryption and "fine-grained" MACs from a CISF. We also show how to build fine-grained public-key encryption from a VDF with a few extra natural properties and indistinguishability obfucsation (iO) for null circuits. We do not assume one-way functions. Finally, we define a primitive that we call a commutative sequential function - essentially a sequential function that can be computed in sequence to get the same output in two different ways - and show that it implies fine-grained key exchange.

This paper presents KyberSlash1 and KyberSlash2 – two timing vulnerabilities in several implementations (including the official reference code) of the Kyber Post-Quantum Key Encapsulation Mechanism, currently undergoing standardization as ML-KEM. We demonstrate the exploitability of both KyberSlash1 and KyberSlash2 on two popular platforms: the Raspberry Pi 2 (Arm Cortex-A7) and the Arm Cortex-M4 microprocessor. Kyber secret keys are reliably recovered within minutes for KyberSlash2 and a few hours for KyberSlash1. We responsibly disclosed these vulnerabilities to maintainers of various libraries and they have swiftly been patched. We present two approaches for detecting and avoiding similar vulnerabilities. First, we patch the dynamic analysis tool Valgrind to allow detection of variable-time instructions operating on secret data, and apply it to more than 1000 implementations of cryptographic primitives in SUPERCOP. We report multiple findings. Second, we propose a more rigid approach to guarantee the absence of variable-time instructions in cryptographic software using formal methods.

Secure merge refers to the problem of merging two sorted lists. The problem appears in different settings where each list is held by one of two parties, or the lists are themselves shared among two or more parties. The output of a secure merge protocol is secret shared. Each variant of the problem offers many useful applications.

The difficulty in designing secure merge protocols vis-a-vis insecure merge protocols (which work in linear time with a single pass over the lists) has to do with operations having to be oblivious or data-independent. In particular, the protocol cannot leak the positions of items of each list in the final merged list. On account of this, sorting-based secure merge protocols have been a common solution to the problem. However, as they introduce (poly)logarithmic overheads, there has been active investigation into the task of building (near) linear time secure merge protocols. Most recently, Hemenway et al. put forth a protocol for secure merge that does achieve linear communication and computation and a round complexity of $O({\log\log n})$, where $n$ is the length of the lists being merged. While this shows the feasibility of a linear time secure merge, it still leaves room for the design of a concretely efficient linear time secure merge.

In this work, we consider a relaxation of the problem where the lists are uniformly random. We show a secure merge protocol for uniformly random lists that achieves $O({n\log\log n})$, i.e., near linear communication and computation and a round complexity of $O({\log\log n})$, where $n$ is the length of the lists being merged. Our protocol design is general and can be instantiated in a variety of settings so long as the building blocks (basic ones such as comparisons and shuffles) can be realized in said settings. Although we do not achieve the same asymptotic guarantees as Hemenway et al., our work is concretely efficient. We implement our protocol and compare it to the state of the art sorting protocols and demonstrate an order of magnitude improvement in running times and communication for lists of size of $2^{20}$.

We also extend our protocol to work for lists sampled from arbitrary distributions. In particular, when the lists are (close to) identically distributed, we achieve the same efficiency as uniform lists. This immediately improve the performance of many crucial applications including PSI & Secure Join, thus illustrating the significance and applicability of our protocol in practice.

Machine learning is widely used for a range of applications and is increasingly offered as a service by major technology companies. However, the required massive data collection raises privacy concerns during both training and inference. Privacy-preserving machine learning aims to solve this problem. In this setting, a collection of servers secret share their data and use secure multi-party computation to train and evaluate models on the joint data. All prior work focused on the scenario where the number of servers is two or three. In this work, we study the problem where there are $N \geq 3$ servers amongst whom the data is secret shared.

A key component of machine learning algorithms is to perform fixed-point multiplication with truncation of secret shared decimal values. In this work, we design new protocols for multi-party secure fixed-point multiplication where each of the $N$ parties have one share each of the two values to be multiplied and receive one share of the product at the end of the protocol. We consider three forms of secret sharing - replicated, Shamir, and additive, and design an efficient protocol secure in the presence of a semi-honest adversary for each of the forms. Our protocols are more communication efficient than all prior work on performing multi-party fixed-point multiplication. Additionally, for replicated secret sharing, we design another efficient protocol that is secure in the presence of a malicious adversary. Finally, we leverage our fixed-point multiplication protocols to design secure multi-party computation (MPC) protocols for arbitrary arithmetic circuits that have addition and fixed-point multiplication with truncation gates. All our protocols are proven secure using a standard simulation based security definition. Our protocols for replicated and Shamir sharing work in the presence of an honest majority of parties while the one for additive sharing can tolerate a dishonest majority as well.

The sum-check protocol of Lund, Fortnow, Karloff, and Nisan underlies SNARKs with the fastest known prover. In many of its applications, the prover can be implemented with a number of field operations that is linear in the number, $n$, of terms being summed.

We describe an optimized prover implementation when the protocol is applied over an extension field of a much smaller base field. The rough idea is to keep most of the prover's multiplications over the base field, at the cost of performing more $\textit{total}$ field multiplications. When the sum-check protocol is applied to a product of polynomials that all output values in the base field, our algorithm reduces the number of extension field operations by multiple orders of magnitude. In other settings, our improvements are more modest but nonetheless meaningful.

In SNARK design, the sum-check protocol is often combined with a polynomial commitment scheme, which are growing faster, especially when the values being committed are small. These improved commitment schemes are likely to render the sum-check prover the overall bottleneck, which our results help to mitigate.

Threshold secret sharing enables distributing a message to $n$ parties such that no subset of fewer than $t$ parties can learn the message, whereas any subset of at least $t$ parties can recover the message. Despite being a fundamental primitive, secret sharing still suffers from one significant drawback, where its message reconstruction algorithm is computationally expensive for large privacy thresholds $t$. In this paper, we aim to address this significant drawback.

We study general $(t,c)$-ramp secret sharing schemes where the number of parties c needed to reconstruct the secret may be larger than $t$. We present a ramp secret sharing scheme whose reconstruction time is 2-7.8x faster than prior constructions suitable against adversaries that adaptively corrupt parties. For $t = 2^{20}$, our new protocol has reconstruction time of 5 seconds whereas prior work requires nearly half a minute. We see improvements starting from as small as $t = 256$. Furthermore, we obtain correctness threshold as small as $c \ge 1.05t$. To obtain our construction, we first improve the secret sharing frameworks by Cramer et al. (EUROCRYPT'15) and Applebaum et al. (CRYPTO'23) from erasure codes. Our new framework obtains secret sharing schemes that may be used against adversaries with adaptive corruptions while requiring only weaker correctness guarantees from the underlying erasure code with a distributed generation property. Furthermore, our new framework also maintains the linear homomorphism of the prior works. Afterwards, we present a concretely efficient erasure code from random band matrices that satisfies the distributed generation property.

We show that our secret sharing scheme can improve many real-world applications. In secure aggregation protocols for federated learning, we obtain up to 22% reductions in computational cost by replacing Shamir's scheme with our construction. We extend our protocol to obtain a verifiable ramp secret sharing scheme where each party can verify the consistency of the shares. Our new verifiable ramp secret sharing has 8.2-25.2x faster sharing and 2.7-23.2x faster reconstruction time compared to prior works. Finally, we present an improved distributed verifiable random function that may be used for decentralized randomness beacons.

The literature sometimes uses slow algorithms to find minimum-length continued-fraction differential addition chains to speed up subsequent computations of multiples of points on elliptic curves. This paper introduces two faster algorithms to find these chains. The first algorithm prunes more effectively than previous algorithms. The second algorithm uses a meet-in-the-middle approach and appears to have a limiting cost exponent below 1.

Common random string model is a popular model in classical cryptography. We study a quantum analogue of this model called the common Haar state (CHS) model. In this model, every party participating in the cryptographic system receives many copies of one or more i.i.d Haar random states.

We study feasibility and limitations of cryptographic primitives in this model and its variants:

- We present a construction of pseudorandom function-like states with security against computationally unbounded adversaries, as long as the adversaries only receive (a priori) bounded number of copies. By suitably instantiating the CHS model, we obtain a new approach to construct pseudorandom function-like states in the plain model.

- We present separations between pseudorandom function-like states (with super-logarithmic length) and quantum cryptographic primitives, such as interactive key agreement and bit commitment, with classical communication. To show these separations, we prove new results on the indistinguishability of identical versus independent Haar states against LOCC (local operations, classical communication) adversaries. \end{itemize}

Local differential privacy (LDP) is an efficient solution for providing privacy to client's sensitive data while simultaneously releasing aggregate statistics without relying on a trusted central server (aggregator) as in the central model of differential privacy. The shuffle model with LDP provides an additional layer of privacy, by disconnecting the link between clients and the aggregator, further improving the utility of LDP. However, LDP has been shown to be vulnerable to malicious clients who can perform both input and output manipulation attacks, i.e., before and after applying the LDP mechanism, to skew the aggregator's results. In this work, we show how to prevent malicious clients from compromising LDP schemes. Specifically, we give efficient constructions to prevent both input ánd output manipulation attacks from malicious clients for generic LDP algorithms. Our proposed schemes for verifiable LDP (VLDP), completely protect from output manipulation attacks, and prevent input attacks using signed data, requiring only one-time interaction between client and server, unlike existing alternatives [28, 33]. Most importantly, we are the first to provide an efficient scheme for VLDP in the shuffle model. We describe and prove secure, two schemes for VLDP in the regular model, and one in the shuffle model. We show that all schemes are highly practical, with client runtimes of < 2 seconds, and server runtimes of 5-7 milliseconds per client.

Many lattice-based crypstosystems employ ideal lattices for high efficiency. However, the additional algebraic structure of ideal lattices usually makes us worry about the security, and it is widely believed that the algebraic structure will help us solve the hard problems in ideal lattices more efficiently. In this paper, we study the additional algebraic structure of ideal lattices further and find that a given ideal lattice in a polynomial ring can be embedded as an ideal into infinitely many different polynomial rings by the coefficient embedding. We design an algorithm to verify whether a given full-rank lattice in $\mathbb{Z}^n$ is an ideal lattice and output all the polynomial rings that the given lattice can be embedded into as an ideal with bit operations $\mathcal{O}(n^3(\log n + B)^2(\log n)^2)$, where $n$ is the dimension of the lattice and $B$ is the upper bound of the bit length of the entries of the input lattice basis. We would like to point out that Ding and Lindner proposed an algorithm for identifying ideal lattices and outputting a single polynomial ring of which the input lattice can be regarded as an ideal with bit operations $\mathcal{O}(n^5B^2)$ in 2007. However, we find a flaw in Ding and Lindner's algorithm, and it causes some ideal lattices can't be identified by their algorithm.

PeaceFounder is a centralised E2E verifiable e-voting system that leverages pseudonym braiding and history trees. The immutability of the bulletin board is maintained replication-free by voter’s client devices with locally stored consistency-proof chains. Meanwhile, pseudonym braiding done via an exponentiation mix before the vote allows anonymisation to be transactional with a single braider at a time. In contrast to existing E2E verifiable e-voting systems, it is much easier to deploy as the system is fully centralised, free from threshold decryption ceremonies, trusted setup phases and bulletin board replication. Furthermore, the body of a vote is signed with a braided pseudonym, enabling unlimited ballot types.

This paper presents a novel reduction from the average-case hardness of the Module Inhomogeneous Short Integer Solution (M-ISIS) problem to the worst-case hardness of the Closest Vector Problem (CVP) by defining and leveraging “perfect” lattices for cryptographic purposes.

Perfect lattices, previously only theoretical constructs, are characterized by their highly regular structure, optimal density, and a central void, which we term the “Origin Cell.” The simplest Origin Cell is a hypercube with edge length 1 centered at the origin, guaranteed to be devoid of any valid lattice points.

By exploiting the unique properties of the Origin Cell, we recalibrate the parameters of the M-ISIS and CVP problems. Our results demonstrate that solving M-ISIS on average over perfect lattices is at least as hard as solving CVP in the worst case, thereby providing a robust hardness guarantee for M-ISIS. Additionally, perfect lattices facilitate exceptionally compact cryptographic variables, enhancing the efficiency of cryptographic schemes.

This significant finding enhances the theoretical foundation of lattice-based cryptographic problems and confirms the potential of perfect lattices in ensuring strong cryptographic security. The Appendix includes SageMath code to demonstrate the reproducibility of the reduction process from M-ISIS to CVP.

SNARKs based on the sum-check protocol often invoke the ``zero-check PIOP''. This reduces the vanishing of many constraints to a single sum-check instance applied to an $n$-variate polynomial of the form $g(x) = \text{eq}(r,x) \cdot p(x)$, where $p$ is a product of multilinear polynomials, $r$ is a random vector, and $\text{eq}$ is the multilinear extension of the equality function. In recent SNARK designs, $p(x)$ is defined over a ``small'' base field, while $r$ is drawn from a large extension field $\mathbb{F}$ for security.

Recent papers (Bagad, Domb, and Thaler 2024; Gruen 2024) have optimized the sum-check protocol prover for this setting. However, these works still require the prover to ``pre-compute'' all evaluations of $\text{eq}(r, x)$ as $x$ ranges over $\{0, 1\}^{n}$, and this computation involves about $n$ multiplications over the extension field $\mathbb{F}$. In this note, we describe a modification to the zero-check PIOP in the case of binary tower fields that reduces this ``pre-computation'' cost by a factor of close to $\log |\mathbb{F}|$, which is $128$ in important applications. We show that our modification is sound, and that it strictly generalizes a (possibly folklore) technique of constraint-packing over field extensions.

We discuss zero-knowledge in the context of FRI-based STARKs using techniques desirable in practice: Randomization by polynomials over the basefield, and decomposing the overall quotient into polynomials of smaller degree.

For primes $p$ with $p+1$ being smooth, the G-FFT from Li and Xing [LX23] is an algebraic FFT, which at first glance seems equivalent to the circle FFT from [IACR eprint 2024/278]: It also uses the circle curve over $\mathbb F_p$ (in other words the projective line) as underlying domain, and interpolates by low-degree functions with poles over the same set of points. However, their approach to control the degree of the FFT basis is fundamentally different. The G-FFT makes use of punctured Riemann-Roch spaces, and the construction works with the group doubling map only, no projection onto the $x$-axis involved.

In this note we give an elementary description of the G-FFT without using abstract algebra. We describe a variant which uses a simpler, and in our opinion more natural function space, and which treats the exceptional point of the domain (the group identity) differently. In comparison to the circle FFT, the G-FFT (both the original as well as our variant) has the following downsides. Interpolation and domain evaluation costs the double number of multiplications (the twiddle is not an ``odd'' function), and the function space is not invariant under the group action, causing additional overhead when applied in STARKs.

Garbled circuits (GC) are a secure multiparty computation protocol that enables two parties to jointly compute a function using their private data without revealing it to each other. While garbled circuits are proven secure at the protocol level, implementations can still be vulnerable to side-channel attacks. Recently, side-channel analysis of GC implementations has garnered significant interest from researchers.

We investigate popular open-source GC frameworks and discover that the AES encryption used in the garbling process follows a secret-dependent sequence. This vulnerability allows private inputs to be exposed through side-channel analysis. Based on this finding, we propose a side-channel attack on garbled circuits to recover the private inputs of both parties. Our attack does not require access to any plaintexts or ciphertexts in the protocol and is single-trace, adhering to the constraint that a garbled circuit can be executed only once. Furthermore, unlike existing attacks that can only target input non-XOR gates, our method applies to both input and internal non-XOR gates. Consequently, the secrets associated with every non-XOR gate are fully exposed as in an open book.

We comprehensively evaluate our attack in various scenarios. First, we perform the attack on single-platform software implementations of standard AES and interleaved AES on a 32-bit ARM processor, achieving a $100\%$ success rate in both cases. Next, we target a hardware implementation on a Xilinx Artix-7 FPGA, where the resolution of power consumption measurements and the number of samples are significantly limited. In this scenario, our attack achieves a success rate of $79.58\%$. Finally, we perform a cross-platform attack on two processors with different microarchitectures representing the two parties. The differing execution cycles and power sensors across the platforms increase the difficulty of side-channel analysis. Despite these challenges, our point-of-interest (POI) selection method allows our attack to achieve a $100\%$ success rate in this scenario as well. We also discuss effective countermeasures that can be readily applied to GC frameworks to mitigate this vulnerability.