International Association
for Cryptologic Research

A single-leader election (SLE) is a way to elect one leader randomly among the parties in a distributed system. If the leader is secret (i.e., unpredictable) then it is called a secret single leader election (SSLE). In this paper, we model the security of SLE in the universally composable (UC) model. Our model is adaptable to various unpredictability levels for leaders that an SLE aims to provide. We construct an SLE protocol that we call semi-anonymous single leader election (SASLE). We show that SASLE is secure against adaptive adversaries in the UC model. SASLE provides a good amount of unpredictability level to most of the honest leaders while it does not provide unpredictability to the rest of them. In this way, we obtain better communication overhead by comparing the existing SSLE protocols. In the end, we construct a PoS-protocol (Sassafras) which deploys SASLE to elect the block producers. Sassafras benefits from the efficiency of SASLE and gains significant security both to grinding attacks and the private attack as shown by Azouvi and Cappelletti (ACM AFT 2021) because it elects a single block producer.

In Private Set Intersection protocols (PSIs), a non-empty result always reveals something about the private input sets of the parties. Moreover, in various variants of PSI, not all parties necessarily receive or are interested in the result. Nevertheless, to date, the literature has assumed that those parties who do not receive or are not interested in the result still contribute their private input sets to the PSI for free, although doing so would cost them their privacy. In this work, for the first time, we propose a multi-party PSI, called “Anesidora”, that rewards parties who contribute their private input sets to the protocol. Anesidora is efficient; it mainly relies on symmetric key primitives and its computation and communication complexities are linear with the number of parties and set cardinality. It remains secure even if the majority of parties are corrupted by active colluding adversaries.

End-to-end encryption (E2EE) prevents online services from accessing user content. This important security property is also an obstacle for content moderation methods that involve content analysis. The tension between E2EE and efforts to combat child sexual abuse material (CSAM) has become a global flashpoint in encryption policy, because the predominant method of detecting harmful content---server-side perceptual hash matching on plaintext images---is unavailable.

Recent applied cryptography advances enable private hash matching (PHM), where a service can match user content against a set of known CSAM images without revealing the hash set to users or nonmatching content to the service. These designs, especially a 2021 proposal for identifying CSAM in Apple's iCloud Photos service, have attracted widespread criticism for creating risks to security, privacy, and free expression.

In this work, we aim to advance scholarship and dialogue about PHM by contributing new cryptographic methods for system verification by the general public. We begin with motivation, describing the rationale for PHM to detect CSAM and the serious societal and technical issues with its deployment. Verification could partially address shortcomings of PHM, and we systematize critiques into two areas for auditing: trust in the hash set and trust in the implementation. We explain how, while these two issues cannot be fully resolved by technology alone, there are possible cryptographic trust improvements.

The central contributions of this paper are novel cryptographic protocols that enable three types of public verification for PHM systems: (1) certification that external groups approve the hash set, (2) proof that particular lawful content is not in the hash set, and (3) eventual notification to users of false positive matches. The protocols that we describe are practical, efficient, and compatible with existing PHM constructions.

A distributed point function (DPF) (Gilboa-Ishai, Eurocrypt 2014) is a cryptographic primitive that enables compressed additive secret-sharing of a secret weight-1 vector across two or more servers. DPFs support a wide range of cryptographic applications, including efficient private information retrieval, secure aggregation, and more. Up to now, the study of DPFs was restricted to the computational security setting, relying on one-way functions. This assumption is necessary in the case of a dishonest majority.

We present the first statistically private 3-server DPF for domain size $N$ with subpolynomial key size $N^{o(1)}$. We also present a similar perfectly private 4-server DPF. Our constructions offer benefits over their computationally secure counterparts, beyond the superior security guarantee, including better computational complexity and better protocols for distributed key generation, all while having comparable communication complexity for moderate-sized parameters.

This paper describes a formalization of the specification and the algorithm of the cryptographic scheme CRYSTALS-KYBER as well as the verification of its (1 − δ)-correctness proof. During the formalization, a problem in the correctness proof was uncovered. In order to amend this issue, a necessary property on the modulus parameter of the CRYSTALS-KYBER algorithm was introduced. This property is already implicitly fulfilled by the structure of the modulus prime used in the number theoretic transform (NTT). The NTT and its convolution theorem in the case of CRYSTALS-KYBER was formalized as well. The formalization was realized in the theorem prover Isabelle.

We are applying Fermat’s factorization algorithm to sets of public RSA keys. Fermat’s factorization allows efficiently calculating the prime factors of a composite number if the difference between the two primes is small. Knowledge of the prime factors of an RSA public key allows efficiently calculating the private key. A flawed RSA key generation function that produces close primes can therefore be attacked with Fermat’s factorization. We discovered a small number of vulnerable devices that generate such flawed RSA keys in the wild. These affect devices from two printer vendors - Canon and Fuji Xerox. Both use an underlying cryptographic module by Rambus.

In this paper, we investigate the security of several recent MAC constructions with provable security beyond the birthday bound (called BBB MACs) in the quantum setting. On the one hand, we give periodic functions corresponding to targeted MACs (including PMACX, PMAC with parity, HPxHP, and HPxNP), and we can recover secret states using Simon algorithm, leading to forgery attacks with complexity O(n). This implies our results realize an exponential speedup compared with the classical algorithm. Note that our attacks can even break some optimally secure MACs, such as mPMAC+-f, mPMAC+-p1, mPMAC+-p2, mLightMAC+-f, etc. On the other hand, we construct new hidden periodic functions based on SUM-ECBC-like MACs: SUM-ECBC, PolyMAC, GCM-SIV2, and 2K-ECBC−Plus, where periods reveal the information of the secret key. Then, by applying Grover-meets-Simon algorithm to specially constructed functions, we can recover full keys with O(2^(n/2)n) or O(2^(m/2)n) quantum queries, where n is the message block size and m is the length of the key. Considering the previous best quantum attack, our key-recovery attacks achieve a quadratic speedup.

Functional Encryption (FE) is a modern cryptographic technique that allows users to learn only a specific function of the encrypted data and nothing else about its actual content. While the first notions of security in FE revolved around the privacy of the encrypted data, more recent approaches also consider the privacy of the computed function. While in the public key setting, only a limited level of function-privacy can be achieved, in the private-key setting privacy potential is significantly larger. However, this potential is still limited by the lack of rich function families. For this work, we started by identifying the limitations of the current state-of-the-art approaches which, in its turn, allowed us to consider a new threat model for FE schemes. To the best of our knowledge, we here present the first attempt to quantify the leakage during the execution of an FE scheme. By leveraging the functionality offered by Trusted Execution Environments, we propose a construction that given any message-private functional encryption scheme yields a function-private one. Finally, we argue in favour of our construction's applicability on constrained devices by showing that it has low storage and computation costs.

The Feistel construction is one of the most studied ways of building block ciphers. Several generalizations were proposed in the literature, leading to the Generalized Feistel Network (GFN) construction, in which the round function operates on each pair of blocks in parallel until all branches are permuted. At FSE'10, Suzaki and Minematsu studied the diffusion of such construction, raising the question of how many rounds are required so that each block of the ciphertext depends on all blocks of the plaintext. Exhausting all possible permutations up to 16 blocks, they observed that there were always optimal permutations mapping even-number input blocks to odd-number output blocks and vice versa. Recently, both Cauchois et al. and Derbez et al. proposed new algorithms to build optimal even-odd permutations for up to 36 blocks. In this paper, we present a new algorithm based on iterative path building to search for optimal Feistel permutation. This algorithm is much faster in exhausting optimal non-even-odd permutations than all the previous approaches. Our first result is a computational proof that no non-even-odd permutation reaches a better diffusion round than optimal even-odd permutations up to 32 blocks. Furthermore, it is well known that permutations with an optimal diffusion round do not always lead to optimal permutations against differential cryptanalysis. We investigate several new criteria to build permutations leading to more secure GFN.

Embedded systems are a cornerstone of the ongoing digitization of our society, ranging from expanding markets around IoT and smart-X devices over to sensors in autonomous driving, medical equipment or critical infrastructures. Since a vast amount of embedded systems are safety-critical (e.g., due to their operation site), security is a necessity for their operation. However, unlike mobile, desktop, and server systems, where adversaries typically only act have remote access, embedded systems typically face attackers with physical access. Thus embedded system require an additional set of defense techniques, preferably leveraging hardware acceleration to minimize the impact on their stringent operation constraints. Over the last decade numerous defenses have been explored, however, they have often been analyzed in isolation.

In this work, we first systematically analyze the state of the art in defenses for both software exploitation and fault attacks on embedded systems. We then carefully design a holistic instruction set extension to augment the RISC-V instruction set architecture with instructions to deter against the threats analyzed in this work. Moreover we implement our design using the gem5 simulator system and a binary translation approach to arm software with our instruction set extension. Finally, we evaluate performance overhead on the MiBench2 benchmark suite. Our evaluation demonstrates a ROM overhead increase of 20% to defeat the aforementioned attacks.

Deep learning techniques have been widely used in side-channel analysis (SCA) in recent years and shown better performance compared with traditional methods. However, there has been little research dealing with deep learning techniques in fault analysis to date. This article undertakes the first study to introduce deep learning into fault analysis. We investigate the application of multi-layer perceptron (MLP) and convolutional neural network (CNN) in persistent fault analysis (PFA) and propose deep learning-based persistent fault analysis (DLPFA). DLPFA is first applied to advanced encryption standard (AES) to verify its availability. Then, to push the study further, we extend DLPFA to PRESENT, which is a lightweight substitution–permutation network (SPN)-based block cipher. The experimental results show that DLPFA can handle random faults and provides outstanding performance with a suitable selection of hyper-parameters.

It is known that the Scholz conjecture on addition chains is true for all integers n with ℓ(2n) = ℓ(n) + 1. There exists infinitely many integers with ℓ(2n) ≤ ℓ(n) and we don’t know if the conjecture still holds for them. The conjecture is also proven to hold for integers n with v(n) ≤ 5 and for infinitely many integers with v(n) = 6. There is no specific results on integers with v(n) = 7. In [14], an infinite list of integers satisfying ℓ(n) = ℓ(2n) and v(n) = 7 is given by Thurber. In this paper, we prove that the conjecture holds for all of them.

Hyperparameter tuning represents one of the main challenges in deep learning-based profiling side-channel analysis. For each different side-channel dataset, the typical procedure to find a profiling model is applying hyperparameter tuning from scratch. The main reason is that side-channel measurements from various targets contain different underlying leakage distributions. Consequently, the same profiling model hyperparameters are usually not equally efficient for other targets. This paper considers autoencoders for dimensionality reduction to verify if encoded datasets from different targets enable the portability of profiling models and architectures. Successful portability reduces the hyperparameter tuning efforts as profiling model tuning is eliminated for the new dataset, and tuning autoencoders is simpler. We first search for the best autoencoder for each dataset and the best profiling model when the encoded dataset becomes the training set. Our results show no significant difference in tuning efforts using original and encoded traces, meaning that encoded data reliably represents the original data. Next, we verify how portable is the best profiling model among different datasets. Our results show that tuning autoencoders enables and improves portability while reducing the effort in hyperparameter search for profiling models. Lastly, we present a transfer learning case where dimensionality reduction might be necessary if the model is tuned for a dataset with fewer features than the new dataset. In this case, tuning of the profiling model is eliminated and training time reduced.

Optimizing the quantum circuit for implementing Advanced Encryption Standard (AES) is crucial for estimating the necessary resources in attacking AES by Grover algorithm. Previous studies have reduced the number of qubits required for the quantum circuits of AES-128/-192/-256 from 984/1112/1336 to 270/334/398, which is close to the optimal value of 256/320/384. It becomes a challenging task to further optimize them. Aiming at this task, we find a method about how the quantum circuit of AES S-box can be designed with the help of automation tool LIGHTER-R. Particularly, the multiplicative inversion in F_2^8, which is the main part of S-box, is converted into the multiplicative inversion (and multiplication) in F_2^4, then the latter can be implemented by LIGHTER-R because its search space is small enough. By this method, we construct the quantum circuits of S-box for mapping |a>|0> to |a>|S(a)> and |a>|b> to |a>|b+S(a)> with 20 qubits instead of 22 in the previous studies. Besides, we introduce new techniques to reduce the number of qubits required by the S-box circuit for mapping |a> to |S(a)>from 22 in the previous studies to 16. Accordingly, we synthesize the quantum circuits of AES-128/-192/-256 with 264/328/392 qubits, which implies a new record.

We consider actions of a group or a semigroup on a set, which generalize the setup of discrete logarithm based cryptosystems. Such cryptographic group actions have gained increasing attention recently in the context of isogeny-based cryptography. We introduce generic algorithms for the semigroup action problem and discuss lower and upper bounds. Also, we investigate Pohlig-Hellman type attacks in a general sense. In particular, we consider reductions provided by non-invertible elements in a semigroup, and we deal with subgroups in the case of group actions.

The learning with errors (LWE) assumption is a powerful tool for building encryption schemes with useful properties, such as plausible resistance to quantum computers, or support for homomorphic computations. Despite this, essentially the only method of achieving threshold decryption in schemes based on LWE requires a modulus that is superpolynomial in the security parameter, leading to a large overhead in ciphertext sizes and computation time.

In this work, we propose a (fully homomorphic) encryption scheme that supports a simple $t$-out-of-$n$ threshold decryption protocol while allowing for a polynomial modulus. The main idea is to use the Rényi divergence (as opposed to the statistical distance as in previous works) as a measure of distribution closeness. This comes with some technical obstacles, due to the difficulty of using the Rényi divergence in decisional security notions such as standard semantic security. We overcome this by constructing a threshold scheme with a weaker notion of one-way security and then showing how to transform any one-way threshold scheme into one guaranteeing semantic security.

Non-interactive zero-knowledge (NIZK) proof systems are often constructed based on cryptographic assumptions. In this paper, we propose the first unconditionally secure NIZK system in the AC0-fine-grained setting. More precisely, our NIZK system has perfect soundness for all adversaries and unconditional zero-knowledge for AC0 adversaries, namely, an AC0 adversary can only break the zero-knowledge property with negligible probability unconditionally. At the core of our construction is an OR-proof system for satisfiability of 1 out of polynomial many statements.

Micciancio and Sorrel (ICALP 2018) proposed a bootstrapping algorithm that can refresh many messages at once with sublinearly many homomorphic operations per message. However, despite the attractive asymptotic cost, it is unclear if their algorithm can be practical, which reduces the impact of their results. In this work, we follow their general framework, but propose an amortized bootstrapping that is conceptually simpler and asymptotically cheaper. We reduce the number of homomorphic operations per refreshed message from $O(3^\rho \cdot n^{1/\rho} \cdot \log n)$ to $O(\rho \cdot n^{1/\rho})$, and the noise overhead from $\tilde{O}(n^{2 + 3 \cdot \rho})$ to $\tilde{O}(n^{1.5 + \rho})$. To obtain a concrete instantiation of our bootstrapping algorithm, we propose a double-CRT (aka RNS) version of the GSW scheme, including a new operation, called shrinking, used to speed-up homomorphic operations by reducing the dimension and ciphertext modulus of the ciphertexts. We provide a C++ implementation of our algorithm, thus showing that the amortized bootstrapping is not only theoretical, but practical. Moreover, it is up to 2.7 times faster than an equivalent non-amortized version for the smallest parameter set we consider, and gains are expected to increase as the parameters increase.

The SIDH protocol is an isogeny-based key exchange protocol using supersingular isogenies, designed by Jao and De Feo in 2011. The protocol underlies the SIKE algorithm which advanced to the fourth round of NIST's post-quantum standardization project in May 2022. The algorithm was considered very promising: indeed the most significant attacks against SIDH were meet-in-the-middle variants with exponential complexity, and torsion point attacks which only applied to unbalanced parameters (and in particular, not to SIKE).

This security picture dramatically changed in August 2022 with new attacks by Castryck-Decru, Maino-Martindale and Robert. Like prior attacks on unbalanced versions, these new attacks exploit torsion point information provided in the SIDH protocol. Crucially however, the new attacks embed the isogeny problem into a similar isogeny problem in a higher dimension to also affect the balanced parameters. As a result of these works, the SIKE algorithm is now fully broken both in theory and in practice.

Given the considerable interest attracted by SIKE and related protocols in recent years, it is natural to seek countermeasures to the new attacks. In this paper, we introduce two such countermeasures based on partially hiding the isogeny degrees and torsion point information in the SIDH protocol. We present a preliminary analysis of the resulting schemes including non-trivial generalizations of prior attacks. Based on this analysis we suggest parameters for our M-SIDH variant with public key sizes of 4434, 7037 and 9750 bytes respectively for NIST security levels 1, 3, 5.

Private matching for compute (PMC) establishes a match between two databases owned by mutually distrusted parties ($C$ and $P$) and allows the parties to input more data for the matched records for arbitrary downstream secure computation without rerunning the private matching component. The state-of-the-art PMC protocols only support two parties and assume that both parties can participate in computationally intensive secure computation. We observe that such operational overhead limits the adoption of these protocols to solely powerful entities as small data owners or devices with minimal computing power will not be able to participate.

We introduce two protocols to delegate PMC from party $P$ to untrusted cloud servers, called delegates, allowing multiple smaller $P$ parties to provide inputs containing identifiers and associated values. Our Delegated Private Matching for Compute protocols, called DPMC and D$^S$PMC, establish a join between the databases of party $C$ and multiple delegators $P$ based on multiple identifiers and compute secret shares of associated values for the identifiers that the parties have in common. We introduce a novel rerandomizable encrypted oblivious pseudorandom function (OPRF) construction, called EO, which allows two parties to encrypt, mask, and shuffle their data and is secure against semi-honest adversaries. Note that EO may be of independent interest. Our D$^S$PMC protocol limits the leakages of DPMC by combining our novel EO scheme and secure three-party shuffling. Finally, our implementation demonstrates the efficiency of our constructions by outperforming related works by approximately $10\times$ for the total protocol execution and by at least $20\times$ for the computation on the delegators.