International Association
for Cryptologic Research

There are numerous settings in which people's preferences are aggregated outside of formal elections, and where privacy and verification are important but the stringent authentication and coercion-resistant properties of government elections do not apply, a prime example being social media platforms. These systems are often iterative and have no trusted authority, in contrast to the centrally organised, single-shot elections on which most of the literature is focused. Moreover, they require a continuous flow of aggregation to take place and become available even as input is still collected from the participants which is in contrast to "fairness" in classical elections where partial results should never be revealed.

In this work, we explore opinion aggregation in a decentralised, iterative setting by proposing a novel protocol in which randomly-chosen participants take turns to act in an incentive-driven manner as decryption authorities. Our construction provides public verifiability, robust vote privacy and liveness guarantees, while striving to minimise the resources each participant needs to contribute.

Hashing arbitrary values to points on an elliptic curve is a required step in many cryptographic constructions, and a number of techniques have been proposed to do so over the years. One of the first ones was due to Shallue and van de Woestijne (ANTS-VII), and it had the interesting property of applying to essentially all elliptic curves over finite fields. It did not, however, have the desirable property of being indifferentiable from a random oracle when composed with a random oracle to the base field.

Various approaches have since been considered to overcome this limitation, starting with the foundational work of Brier et al. (CRYPTO 2011). For example, if $f\colon \mathbb{F}_q\to E(\mathbb{F}_q)$ is the Shallue--van de Woestijne (SW) map and $\mathfrak{h}_1,\mathfrak{h}_2$ are two independent random oracles to $\mathbb{F}_q$, we now know that $m\mapsto f\big(\mathfrak{h}_1(m)\big)+f\big(\mathfrak{h}_2(m)\big)$ is indifferentiable from a random oracle. Unfortunately, this approach has the drawback of being twice as expensive to compute than the straightforward, but not indifferentiable, $m\mapsto f\big(\mathfrak{h}_1(m)\big)$. Most other solutions so far have had the same issue: they are at least as costly as two base field exponentiations, whereas plain encoding maps like $f$ cost only one exponentiation. Recently, Koshelev (DCC 2022) provided the first construction of indifferentiable hashing at the cost of one exponentiation, but only for a very specific class of curves (some of those with $j$-invariant $0$), and using techniques that are unlikely to apply more broadly.

In this work, we revisit this long-standing open problem, and observe that the SW map actually fits in a one-parameter family $(f_u)_{u\in\mathbb{F}_q}$ of encodings, such that for independent random oracles $\mathfrak{h}_1, \mathfrak{h}_2$ to $\mathbb{F}_q$, $F\colon m\mapsto f_{\mathfrak{h}_2(m)}\big(\mathfrak{h}_1(m)\big)$ is indifferentiable. Moreover, on a very large class of curves (essentially those that are either of odd order or of order divisible by 4), the one-parameter family admits a rational parametrization, which let us compute $F$ at almost the same cost as small $f$, and finally achieve indifferentiable hashing to most curves with a single exponentiation.

Our new approach also yields an improved variant of the Elligator Squared technique of Tibouchi (FC 2014) that represents points of arbitrary elliptic curves as close-to-uniform random strings.

Adaptive security is a highly desirable property in the design of secure protocols. It tolerates adversaries that corrupt parties as the protocol proceeds, as opposed to static security where the adversary corrupts the parties at the onset of the execution. The well-accepted folklore is that static and adaptive securities are equivalent for perfectly secure protocols. Indeed, this folklore is backed up by a transformation by Canetti et al. (EUROCRYPT'01), showing that any perfectly secure protocol that is statically secure and satisfies some basic requirements is also adaptively secure. Yet, the transformation results in an adaptively secure protocol with inefficient simulation (i.e., where the simulator might run in super-polynomial time even if the adversary runs just in polynomial time). Inefficient simulation is problematic when using the protocol as a sub-routine in the computational setting.

Our main question is whether an alternative efficient transformation from static to adaptive security exists. We show an inherent difficulty in achieving this goal generically. In contrast to the folklore, we present a protocol that is perfectly secure with efficient static simulation (therefore also adaptively secure with inefficient simulation), but for which efficient adaptive simulation does not exist (assuming the existence of one-way permutations).

In addition, we prove that the seminal protocol of Ben-Or, Goldwasser, and Wigderson (STOC'88) is secure against adaptive, semi-honest corruptions with efficient simulation. Previously, adaptive security of the protocol, as is, was only known either for a restricted class of circuits or for all circuits but with inefficient simulation.

Homomorphic encryption (HE) schemes enable computing functions on encrypted data, by means of a public $\Eval$ procedure that can be applied to ciphertexts. But the evaluated ciphertexts so generated may differ from freshly encrypted ones. This brings up the question of whether one can keep computing on evaluated ciphertexts. An \emph{$i$-hop} homomorphic encryption scheme is one where $\Eval$ can be called on its own output up to $i$~times, while still being able to decrypt the result. A \emph{multi-hop} homomorphic encryption is a scheme which is $i$-hop for all~$i$. In this work we study $i$-hop and multi-hop schemes in conjunction with the properties of function-privacy (i.e., $\Eval$'s output hides the function) and compactness (i.e., the output of $\Eval$ is short). We provide formal definitions and describe several constructions.

First, we observe that "bootstrapping" techniques can be used to convert any (1-hop) homomorphic encryption scheme into an $i$-hop scheme for any~$i$, and the result inherits the function-privacy and/or compactness of the underlying scheme. However, if the underlying scheme is not compact (such as schemes derived from Yao circuits) then the complexity of the resulting $i$-hop scheme can be as high as $k^{O(i)}$.

We then describe a specific DDH-based multi-hop homomorphic encryption scheme that does not suffer from this exponential blowup. Although not compact, this scheme has complexity linear in the size of the composed function, independently of the number of hops. The main technical ingredient in this solution is a \emph{re-randomizable} variant of the Yao circuits. Namely, given a garbled circuit, anyone can re-garble it in such a way that even the party that generated the original garbled circuit cannot recognize it. This construction may be of independent interest.

Machine learning as a service scenario typically requires the client to trust the server and provide sensitive data in plaintext. However, with the recent improvements in fully homomorphic encryption (FHE) schemes, many such applications can be designed in a privacy preserving way. In this work, we focus on such a problem, private decision tree evaluation (PDTE) --- where a server has a decision tree classification model, and a client wants to use the model to classify her private data without revealing the data or the classification result to the server. We present an efficient non-interactive design of PDTE, that we call SortingHat, based on FHE techniques. As part of our design, we solve multiple cryptographic problems related to FHE: (1) we propose a fast homomorphic comparison function where one input can be in plaintext format; (2) we design an efficient binary decision tree evaluation technique in the FHE setting, which we call homomorphic traversal, and apply it together with our homomorphic comparison to evaluate private decision tree classifiers, obtaining running times orders of magnitude faster than the state of the art; (3) we improve both the communication cost and the time complexity of transciphering, by applying our homomorphic comparison to the FiLIP stream cipher. Through a prototype implementation, we demonstrate that our improved transciphering solution runs around 400 times faster than previous works. We finally present a choice in terms of PDTE design: we present a version of SortingHat without transciphering that achieves significant improvement in terms of computation cost comparing to prior works; and another version with transciphering that has a communication cost about 20 thousand times smaller but comparable running time.

In this work we propose a new accumulator construction and efficient ways to prove knowledge of some element in a set without leaking anything about the element. This problem arises in several applications including privacy-preserving distributed ledgers (e.g., Zcash) and anonymous credentials. Our approaches do not require a trusted setup and significantly improve on the efficiency state of the of the art. We introduce new techniques inspired by commit-and-prove techniques and combine shallow Merkle trees, 2-cycles of elliptic curves to obtain constructions that are highly practical. Our basic construction—which we dub $\mathsf{Curve} \ \mathsf{Trees}$—is completely transparent (does not require a trusted setup) and is based on simple standard assumptions (DLOG and Random Oracle Model). It has small proofs and commitments and very efficient proving and verification time. Curve trees can be instantiated to be efficient in practice: the commitment to a set (accumulator) is 256 bits for any set size; for a set of size $2^{32}$ a proof is approximately 2KB, a verifier runs in $\approx 160$ms (easily parallelizable to $\approx 80$ms) and a prover in $\approx 3.6$s on an ordinary laptop. Using our construction as a building block we can construct a simple and concretely efficient anonymous cryptocurrency with full anonymity set. We estimate the verification time to be $\approx 320$ms (and trivially parallelizable to run in $\approx 160$ms) or $< 10$ms when batch-verifying multiple ($> 100$) transactions simultaneously. Transaction sizes are $< 3$KB. Our timings are competitive with those of the approach in Zcash Sapling and trade slightly larger proofs (proofs in Zcash are 0.2KB) for a completely transparent setup.

The verifiable delay function (VDF), as a kind of cryptographic primitives, has recently been adopted quite often in decentralized systems. Highly correlated to the security of VDFs, the fastest implementation for VDF evaluation is generally desired to be publicly known. In this paper, for the first time, we propose a low-latency hardware implementation for the complete VDF evaluation in the class group by joint exploiting optimizations. On one side, we reduce the required computational cycles by decreasing the hardware-unfriendly divisions and increase the parallelism of computations by reducing the data dependency. On the other side, well-optimized low-latency architectures for large-number divisions, multiplications, and additions are developed, respectively, while those operations are generally very hard to be accelerated. Based on these basic operators, we devise the architecture for the complete VDF evaluation with possibly minimal pipeline stalls. Finally, the proposed design is coded and synthesized under the TSMC 28-nm CMOS technology. The experimental results show that our design can achieve a speedup of 3.6x compared to the optimal C++ implementation for the VDF evaluation over an advanced CPU. Moreover, compared to the state-of-the-art hardware implementation for the squaring, a key step of VDF, we achieve about 2x speedup.

In this paper we propose the first efficient quantum version of key-recovery attacks on block ciphers based on impossible differentials, which was left as an open problem in previous work. These attacks work in two phases. First, a large number of differential pairs are collected, by solving a limited birthday problem with the attacked block cipher considered as a black box. Second, these pairs are filtered with respect to partial key candidates. We show how to translate the pair filtering step into a quantum procedure, and provide a complete analysis of its complexity. If the path of the attack can be properly reoptimized, this procedure can reach a significant speedup with respect to classical attacks. We provide two applications on SKINNY-128-256 and AES-192/256. These results do not threaten the security of these ciphers but allow us to better understand their (post-quantum) security margin.

Nowadays, MILP is a very popular tool to help cryptographers search for various distinguishers, in particular for integral distinguishers based on the division property. However, cryptographers tend to use MILP in a rather naive way, modeling problems in an exact manner and feeding them to a MILP solver. In this paper, we show that a proper use of some features of MILP solvers such as lazy constraints, along with using simpler but less accurate base models, can achieve much better solving times, while maintaining the precision of exact models. In particular, we describe several new modelization techniques for division property related models as well as a new variant of the Quine-McCluskey algorithm for this specific setting. Moreover, we positively answer a problem raised in [DF20] about handling the large sets of constraints describing valid transitions through Super S-boxes into a MILP model. As a result, we greatly improve the solving times to recover the distinguishers from several previous works ([DF20], [HWW20], [SWW17], [Udo21], [EY21]) and we were able to search for integral distinguishers on 5-round ARIA which was out of reach of previous modeling techniques.

Division property is an effective method for finding integral distinguishers for block ciphers, performing cube attacks on stream ciphers, and studying the algebraic degree of boolean functions. One of the main problems in this field is how to provably find the smallest input multiset leading to a balanced output. In this paper, we propose a new method based on division property for finding integral distinguishers with a provably minimum data complexity on permutation functions and block ciphers, in the conventional division property model. The new method is based on efficiently analyzing the algebraic normal form of the target output boolean function. We examine the proposed method on LBlock, TWINE, SIMON, Present, Gift, and Clyde-128 block ciphers. Although in most cases, the results are compliant with the distinguishers reported in the previous work, the proposed method proves the optimality of these results, in the conventional division property model. However, the proposed method can find distinguishers for 8-round Clyde-128 with a data complexity less than the previously reported one, based on conventional division property. The new method is also capable of determining the maximum number of balanced output bits in an integral distinguisher with a specified number of active bits. We propose an algorithm to exploit this capability and apply it to the studied ciphers. As a result, we determine the maximum number of balanced bits on integral distinguishers with minimum and non-minimum data complexities on the studied ciphers and report improved results on Gift-64, Present and SIMON64 in the conventional model.

The recently proposed YOSO model is a groundbreaking approach to MPC, executable on a public blockchain, circumventing adaptive player corruption by hiding the corruption targets until they are worthless. Players are selected unpredictably from a large pool to perform MPC subtasks, in which each selected player sends a single message (and reveals their identity). While YOSO MPC has attractive asymptotic complexity, unfortunately, it is concretely prohibitively expensive due to the cost of its building blocks.

We propose a modification to the YOSO model that preserves resilience to adaptive server corruption, but allows for much more efficient protocols. In SCALES (Small Clients And Larger Ephemeral Servers) only the servers facilitating the MPC computation are ephemeral (unpredictably selected and ``speak once''). Input providers (clients) publish problem instances and collect the output, but do not otherwise participate in computation. SCALES offers attractive features, and improves over YOSO protocols in outsourcing MPC to a large pool of servers under adaptive corruption.

We build SCALES from rerandomizable garbling schemes, which is a contribution of independent interest, with additional applications.

A Private Set Union (PSU) protocol allows parties, each holding an input set, to jointly compute the union of the sets without revealing anything else. In the literature, when we design scalable two-party PSU protocols, we follow the so-called ``split-execute-assemble'' paradigm, and also use Oblivious Transfer as a building block. Recently, Kolesnikov et al. (ASIACRYPT 2019) pointed out that security issues could be introduced when we design PSU protocols following the ``split-execute-assemble'' paradigm. Surprisingly, we observe that the typical way of invoking Oblivious Transfer also causes unnecessary leakage.

In this work, to enable a better understanding of the security for PSU, we provide a systematic treatment of the typical PSU protocols, which may shed light on the design of practical and secure PSU protocols in the future. More specifically, we define different versions of PSU functionalities to properly capture the subtle security issues arising from protocols following the ``split-execute-assemble'' paradigm and using Oblivious Transfer as subroutines. Then, we survey the typical PSU protocols, and categorize these protocols into three design frameworks, and prove what PSU functionality the protocols under each framework can achieve at best, in the semi-honest setting.

Draco is a lightweight stream cipher designed by Hamann et al. in IACR ToSC 2022. It has a Grain-like structure with two state registers of size 95 and 33 bits. In addition, the cipher uses a 128-bit secret key and a 96-bit IV. The first 32 bits of the key and the IV forms a non-volatile internal state that does not change during the time that the cipher produces keystream bits. The authors claim that the cipher is provably secure against Time Memory Data (TMD) Tradeoff attacks. However in this paper, we first present two TMD tradeoff attacks against Draco. Both attacks leverage the fact that for certain judiciously chosen IVs, the state update function of the cipher depend on only a small fraction of the non-volatile internal state. This makes the state update function in Draco essentially a one way function over a much smaller domain and range. The first attack requires around $2^{114.2}$ Draco iterations and requires that the adversary has access to $2^{32}$ chosen IVs. The second attack is such that the attack parameters can be tuned as per the requirements of the attacker. If the attacker prioritizes that the number of different chosen IVs is limited to $2^{20}$ say, then the attack can be done in around time proportional to $2^{126}$ Draco rounds. However if the total attack complexity is to be optimized, then the attack can be performed in $2^{107}$ time using around $2^{40}$ chosen IVs.

We revisit and improve performance of arithmetic in the binary GLS254 curve by introducing the 2D-GLS scalar multiplication algorithm. The algorithm includes theoretical and practice-oriented contributions of potential independent interest: (i) for the first time, a proof that the GLS scalar multiplication algorithm does not incur exceptions, such that faster incomplete formulas can be used; (ii) faster dedicated atomic formulas that alleviate the cost of precomputation; (iii) a table compression technique that reduces the storage needed for precomputed points; (iv) a refined constant-time scalar decomposition algorithm that is more robust to rounding. We also present the first GLS254 implementation for Armv8. With our contributions, we set new speed records for constant-time scalar multiplication by $6\%$ and $34.5\%$ on respectively 64-bit Intel and Arm platforms.

Lightweight cryptography ensures cryptography applications to devices with limited resources. Low-area implementations of linear layers usually play an essential role in lightweight cryptography. The previous works have provided plenty of methods to generate low-area implementations using 2-input xor gates for various linear layers. However, it is still challenging to search for smaller implementations using two or more inputs xor gates. This paper, inspired by Banik et al., proposes a novel approach to construct a quantity of lower area implementations with (n+1)-input gates based on the given implementations with n-input gates. Based on the novel algorithm, we present the corresponding search algorithms for n=2 and n=3, which means that we can efficiently convert an implementation with 2-input xor gates and 3-input xor gates to lower-area implementations with 3-input xor gates and 4-input xor gates, respectively.

We improve the previous implementations of linear layers for many block ciphers according to the area with these search algorithms. For example, we achieve a better implementation with 4-input xor gates for AES MixColumns, which only requires 243 GE in the STM 130 nm library, while the previous public result is 258.9 GE. Besides, we obtain better implementations for all 5500 lightweight matrices proposed by Li et al. at FSE 2019, and the area for them is decreased by about 21% on average.

Recently, there has been great interest towards constructing efficient zero-knowledge proofs for practical languages. In this work, we focus on proofs for threshold relations, in which the prover is required to prove knowledge of witnesses for $k$ out of $\ell$ statements.

The main contribution of our work is an efficient and modular transformation that starting from a large class of $\Sigma$-protocols and a corresponding threshold relation $\mathcal{R}_\mathsf{k,\ell}$, provides an efficient $\Sigma$-protocol for $\mathcal{R}_\mathsf{k,\ell}$ with improved communication complexity w.r.t. prior results. Moreover, our transformation preserves statistical/perfect honest-verifier zero knowledge.

Automatic tools to search for boomerang distinguishers have seen significant advances over the past few years. However, most of the previous works in this context focus on ciphers based on a Substitution Permutation Network (SPN), while analyzing the Feistel structure is of great significance. Although Boukerrou et al. provided a theoretical framework to formulate the boomerang switch over multiple Feistel rounds very recently, they did not provide an automatic tool to search for boomerang distinguishers of Feistel structures taking the switching effect into account. In this paper, by enhancing the recently proposed method to search for boomerang distinguishers by Hadipour et al., we provide an automatic tool to search for boomerang distinguishers and apply it to block ciphers following the Generalized Feistel Structure (GFS). Applying our tool to a wide range of GFS ciphers, we show that it yields a significant improvement compared to the best previous results concerning boomerang analysis. In particular, we improve the best previous boomerang distinguishers for 20 and 21 rounds of WARP by a factor of $2^{38.28$ and $2^{36.56$, respectively. Thanks to the effectiveness of our method, we even improve the boomerang distinguishers of WARP by two rounds and distinguish 23 rounds of this cipher from a random permutation. Applying our method to the internationally-standardized cipher CLEFIA, we achieve a 9-round boomerang distinguisher which improves the best previous boomerang distinguisher by one round. Furthermore, based on this distinguisher, we build a key-recovery attack on 11 rounds of CLEFIA, which improves the best previous sandwich attack on this cipher by one round. We also apply our method to LBlock, LBlock-s, and TWINE and improve the best previous boomerang distinguisher of these ciphers.

We propose MoNet, the first bi-directional payment channel network with unlimited lifetime for Monero. It is fully compatible with Monero without requiring any modification of the current Monero blockchain. MoNet preserves transaction fungibility, i.e., transactions over MoNet and Monero are indistinguishable, and guarantees anonymity of Monero and MoNet users by avoiding any potential privacy leakage introduced by the new payment channel network. We also propose a new crypto primitive, named Verifiable Consecutive One-way Function (VCOF). It allows one to generate a sequence of statement-witness pairs in a consecutive and verifiable way, and these statement-witness pairs are one-way, namely it is easy to compute a statement-witness pair by knowing any of the pre-generated pairs, but hard in an opposite flow. By using VCOF, a signer can produce a series of consecutive adaptor signatures CAS. We further propose the generic construction of consecutive adaptor signature as an important building block of MoNet. We develop a proof-of-concept implementation for MoNet, and our evaluation shows that MoNet can reach the same transaction throughput as Lightning Network, the payment channel network for Bitcoin. Moreover, we provide a security analysis of MoNet under the Universal Composable (UC) security framework.

Replay attacks are among the most well-known attacks against vote privacy. Many e-voting systems have been proven vulnerable to replay attacks, including systems like Helios that are used in real practical elections.

Despite their popularity, it is commonly believed that replay attacks are inefficient but the actual threat that they pose to vote privacy has never been studied formally. Therefore, in this paper, we precisely analyze for the first time how efficient replay attacks really are.

We study this question from commonly used and complementary perspectives on vote privacy, showing as an independent contribution that a simple extension of a popular game-based privacy definition corresponds to a strong entropy-based notion.

Our results demonstrate that replay attacks can be devastating for a voter's privacy even when an adversary's resources are very limited. We illustrate our formal findings by applying them to a number of real-world elections, showing that a modest number of replays can result in significant privacy loss. Overall, our work reveals that, contrary to a common belief, replay attacks can be very efficient and must therefore be considered a serious threat.

In this paper, we propose a new approach to the study of lattice problems used in cryptography. We specifically focus on module lattices of a fixed rank over some number field. An essential question is the hardness of certain computational problems on such module lattices, as the additional structure may allow exploitation. The fundamental insight is the fact that the collection of those lattices are quotients of algebraic manifolds by arithmetic subgroups. Functions on these spaces are studied in mathematics as part of number theory. In particular, those form a module over the Hecke algebra associated with the general linear group. We use results on these function spaces to define a class of distributions on the space of lattices. Using the Hecke algebra, we define Hecke operators associated with collections of prime ideals of the number field and show a criterion on distributions to converge to the uniform distribution, if the Hecke operators are applied to the chosen distribution. Our approach is motivated by the work of de Boer, Ducas, Pellet-Mary, and Wesolowski (CRYPTO'20) on self-reduction of ideal lattices via Arakelov divisors.