International Association
for Cryptologic Research

Feistel network and its generalizations (GFN) are another important building blocks for constructing hash functions, e.g., Simpira v2, Areion, and the ISO standard Lesamnta-LW. The Meet-in-the-Middle (MitM) is a general paradigm to build preimage and collision attacks on hash functions, which has been automated in several papers. However, those automatic tools mostly focus on the hash function with Substitution-Permutation network (SPN) as building blocks, and only one for Feistel network by Schrottenloher and Stevens (at CRYPTO 2022). In this paper, we introduce a new automatic model for MitM attacks on Feistel networks by generalizing the traditional direct or indirect partial matching strategies and also Sasaki’s multi-round matching strategy. Besides, we find the equivalent transformations of Feistel and GFN can significantly simplify the MILP model. Based on our automatic model, we improve the preimage attacks on Feistel-SP-MMO, Simpira-2/-4-DM, Areion-256/-512-DM by 1-2 rounds or significantly reduce the complexities. Furthermore, we fill in the gap left by Schrottenloher and Stevens at CRYPTO 2022 on the large branch (b > 4) Simpira-b’s attack and propose the first 11-round attack on Simpira-6. Besides, we significantly improve the collision attack on the ISO standard hash Lesamnta-LW by increasing the attacked round number from previous 11 to ours 17 rounds.

Motivated by the extended deployment of authenticated data structures (e.g., Merkle Patricia Tries) for verifying massive amounts of data in blockchain systems, we begin a systematic study of the I/O efficiency of such systems. We first explore the fundamental limitations of memory checking, a previously-proposed abstraction for verifiable storage, in terms of its locality---a complexity measure that we introduce for the first time and is defined as the number of non-contiguous memory regions a checker must query to verifiably answer a read or a write query. Our central result is an $\Omega(\log n /\log \log n)$ lower bound for the locality of any memory checker. Then we turn our attention to (dense and sparse) Merkle trees, one of the most celebrated memory checkers, and provide stronger lower bounds for their locality. For example, we show that any dense Merkle tree layout will have average locality at least $\frac 13 \log n$. Furthermore, if we allow node duplication, we show that if any write operation has at most polylog complexity, then the read locality cannot be less than $\log n/\log \log n$. Our lower bounds help us construct two new locality-optimized authenticated data structures (DupTree and PrefixTree) which we implement and evaluate on random operations and real workloads, and which are shown to outperform traditional Merkle trees, especially as the number of leaves increases.

In this paper we introduce a new keyed hash function based on 32-bit integer multiplication that we call Multimixer-128. In our approach, we follow the key-then-hash parallel paradigm. So, we first add a variable length input message to a secret key and split the result into blocks. A fixed length public function based on integer multiplication is then applied on each block and their results are added to form the digest. We prove an upper bound of $2^{-127}$ for the universality of Multimixer-128 by means of the differential probability and image probability of the underlying public function.

There are vector instructions for fast 32-bit integer multiplication on many CPUs and in such platforms, Multimixer-128 is very efficient. We compare our implementation of Multimixer-128 with NH hash function family that offers similar levels of security and with two fastest NIST LWC candidates. To the best of our knowledge, NH hash function is the fastest keyed hash function on software and Multimixer-128 outperforms NH while providing same levels of security.

Let $N=pq$ be the product of two balanced prime numbers $p$ and $q$. Elkamchouchi, Elshenawy and Shaban presented in 2002 an interesting RSA-like cryptosystem that uses the key equation $ed - k (p^2-1)(q^2-1) = 1$, instead of the classical RSA key equation $ed - k (p-1)(q-1) = 1$. The authors claimed that their scheme is more secure than RSA. Unfortunately, the common attacks developed against RSA can be adapted for Elkamchouchi \emph{et al.}'s scheme. In this paper, we introduce a family of RSA-like encryption schemes that uses the key equation $ed - k (p^n-1)(q^n-1) = 1$, where $n>0$ is an integer. Then, we show that regardless of the choice of $n$, there exists an attack based on continued fractions that recovers the secret exponent.

This work studies the key-alternating ciphers (KACs) whose round permutations are not necessarily independent. We revisit existing security proofs for key-alternating ciphers with a single permutation (KACSPs), and extend their method to an arbitrary number of rounds. In particular, we propose new techniques that can significantly simplify the proofs, and also remove two unnatural restrictions in the known security bound of 3-round KACSP (Wu et al., Asiacrypt 2020). With these techniques, we prove the first tight security bound for t-round KACSP, which was an open problem. We stress that our techniques apply to all variants of KACs with non-independent round permutations, as well as to the standard KACs.

Privacy-Preserving Machine Learning (PPML) provides protocols for learning and statistical analysis of data that may be distributed amongst multiple data owners (e.g., hospitals that own proprietary healthcare data), while preserving data privacy. The PPML literature includes protocols for various learning methods, including ridge regression. Ridge regression controls the $L_2$ norm of the model, but does not aim to strictly reduce the number of non-zero coefficients, namely the $L_0$ norm of the model. Reducing the number of non-zero coefficients (a form of feature selection) is important for avoiding overfitting, and for reducing the cost of using learnt models in practice. In this work, we develop a first privacy-preserving protocol for sparse linear regression under $L_0$ constraints. The protocol addresses data contributed by several data owners (e.g., hospitals). Our protocol outsources the bulk of the computation to two non-colluding servers, using homomorphic encryption as a central tool. We provide a rigorous security proof for our protocol, where security is against semi-honest adversaries controlling any number of data owners and at most one server. We implemented our protocol, and evaluated performance with nearly a million samples and up to 40 features.

The design and analysis of dedicated tweakable block ciphers constitute a dynamic and relatively recent research field in symmetric cryptanalysis. The assessment of security in the related-tweakey model is of utmost importance owing to the existence of a public tweak. This paper proposes an automatic search model for identifying related-tweakey impossible differentials based on the propagation of states under specific constraints, which is inspired by the research of Hu et al. in ASIACRYPT 2020. Our model is universally applicable to block ciphers, but its search efficiency may be limited in some cases. To address this issue, we introduce the Locality Constraint Analysis (LCA) technique to impossible differential cryptanalysis and propose a generalized automatic search model. Technically, we transform our models into Satisfiability Modulo Theories (SMT) problems and solve them using the STP solver. We have applied our tools to several tweakable block ciphers, such as Joltik-BC, SKINNY, QARMA, and CRAFT, to evaluate their effectiveness and practicality. Specifically, we have discovered 7-round related-tweakey impossible differentials for Joltik-BC-192, and 12-round related-tweak impossible differentials, as well as 15-round related-tweakey impossible differentials for CRAFT for the first time. Based on the search results, we demonstrate that the LCA technique can be effectively performed when searching and determining the contradictory positions for the distinguisher with long trails or ciphers with large sizes in impossible differential cryptanalysis.

In this work, we tackle the problem of estimating the security of iterated symmetric ciphers in an efficient manner, with tests that do not require a deep analysis of the internal structure of the cipher. This is particularly useful during the design phase of these ciphers, especially for quickly testing several combinations of possible parameters defining several cipher design variants. We consider a popular statistical test that allows us to determine the probability of flipping each cipher output bit, given a small variation in the input of the cipher. From these probabilities, one can compute three measurable metrics related to the well-known full diffusion, avalanche and strict avalanche criteria. This highly parallelizable testing process scales linearly with the number of samples, i.e., cipher inputs, to be evaluated and the number of design variants to be tested. But, the number of design variants might grow exponentially with respect to some parameters. The high cost of CPUs, makes them a bad candidate for this kind of parallelization. As a main contribution, we propose a framework, ACE-HoT, to parallelize the testing process using multi-GPU. Our implementation does not perform any intermediate CPU-GPU data transfers. The diffusion and avalanche criteria can be seen as an application of discrete first-order derivatives. As a secondary contribution, we generalize these criteria to their high-order version. Our generalization requires an exponentially larger number of samples, in order to compute sufficiently accurate probabilities. As a case study, we apply ACE-HoT on most of the finalists of the NIST lightweight standardization process, with a special focus on the winner ASCON.

This paper introduces Bicameral and Auditably Private Signatures (BAPS) -- a new privacy-preserving signature system with several novel features. In a BAPS system, given a certified attribute $\mathbf{x}$ and a certified policy $P$, a signer can issue a publicly verifiable signature $\Sigma$ on a message $m$ as long as $(m, \mathbf{x})$ satisfies $P$. A noteworthy characteristic of BAPS is that both attribute $\mathbf{x}$ and policy $P$ are kept hidden from the verifier, yet the latter is convinced that these objects were certified by an attribute-issuing authority and a policy-issuing authority, respectively. By considering bicameral certification authorities and requiring privacy for both attributes and policies, BAPS generalizes the spirit of existing advanced signature primitives with fine-grained controls on signing capabilities (e.g., attribute-based signatures, predicate signatures, policy-based signatures). Furthermore, BAPS provides an appealing feature named auditable privacy, allowing the signer of $\Sigma$ to verifiably disclose various pieces of partial information about $P$ and $\mathbf{x}$ when asked by auditor(s)/court(s) at later times. Auditable privacy is intrinsically different from and can be complementary to the notion of accountable privacy traditionally incorporated in traceable anonymous systems such as group signatures. Equipped with these distinguished features, BAPS can potentially address interesting application scenarios for which existing primitives do not offer a direct solution.

We provide rigorous security definitions for BAPS, following a ``sim-ext'' approach. We then demonstrate a generic construction based on commonly used cryptographic building blocks, which employs a sign-then-commit-then-prove design. Finally, we present a concrete instantiation of BAPS, that is proven secure in the random oracle model under lattice assumptions. The scheme can handle arbitrary policies represented by polynomial-size Boolean circuits and can address quadratic disclosing functions. In the construction process, we develop a new technical building block that could be of independent interest: a zero-knowledge argument system allowing to prove the satisfiability of a certified-and-hidden Boolean circuit on certified-and-committed inputs.

The constant-sized polynomial commitment scheme by Kate, Zaverucha, and Goldberg (Asiscrypt 2010), also known as the KZG commitment, is an essential component in designing bandwidth-efficient verifiable secret-sharing (VSS) protocols. We point out, however, that the KZG commitment is missing two important properties that are crucial for VSS protocols.

First, the KZG commitment has not been proven to be degree binding in the standard adversary model without idealized group assumptions. In other words, the committed polynomial is not guaranteed to have the claimed degree, which is supposed to be the reconstruction threshold of VSS. Without this property, shareholders in VSS may end up reconstructing different secrets depending on which shares are used.

Second, the KZG commitment does not support polynomials with different degrees at once with a single setup. If the reconstruction threshold of the underlying VSS protocol changes, the protocol must redo the setup, which involves an expensive multi-party computation known as the powers of tau setup.

In this work, we augment the KZG commitment to address both of these limitations. Our scheme is degree-binding in the standard model under the strong Diffie-Hellman (SDH) assumption. It supports any degree $0 < d \le m$ under a powers-of-tau common reference string with $m+1$ group elements generated by a one-time setup.

Constructing key-agreement protocols in the random oracle model (ROM) is a viable method to assess the feasibility of developing public-key cryptography within Minicrypt. Unfortunately, as shown by Impagliazzo and Rudich (STOC 1989) and Barak and Mahmoody (Crypto 2009), such protocols can only guarantee limited security: any $\ell$-query protocol can be attacked by an $O(\ell^2)$-query adversary. This quadratic gap matches the key-agreement protocol proposed by Merkle (CACM 78), known as Merkle's Puzzles.

Besides query complexity, the communication complexity of key-agreement protocols in the ROM is also an interesting question in the realm of find-grained cryptography, even though only limited security is achievable. Haitner et al. (ITCS 2019) first observed that in Merkle's Puzzles, to obtain secrecy against an eavesdropper with $O(\ell^2)$ queries, the honest parties must exchange $\Omega(\ell)$ bits. Therefore, they conjectured that high communication complexity is unavoidable, i.e., any $\ell$-query protocols with $c$ bits of communication could be attacked by an $O(c\cdot \ell)$-query adversary. This, if true, will suggest that Merkle's Puzzle is also optimal regarding communication complexity. Building upon techniques from communication complexity, Haitner et al. (ITCS 2019) confirmed this conjecture for two types of key agreement protocols with certain natural properties.

This work affirms the above conjecture for all non-adaptive protocols with perfect completeness. Our proof uses a novel idea called density increment argument. This method could be of independent interest as it differs from previous communication lower bounds techniques (and bypasses some technical barriers).

Publicly Verifiable Secret Sharing (PVSS) is a fundamental primitive that allows to share a secret $S$ among $n$ parties via a publicly verifiable transcript $T$. Existing (efficient) PVSS are only proven secure against static adversaries who must choose who to corrupt ahead of a protocol execution. As a result, any protocol (e.g., a distributed randomness beacon) that builds on top of such a PVSS scheme inherits this limitation. To overcome this barrier, we revisit the security of PVSS under adaptive corruptions and show that, surprisingly, many protocols from the literature already achieve it in a meaningful way:

- We propose a new security definition for aggregatable PVSS, i.e., schemes that allow to homomorphically combine multiple transcripts into one compact aggregate transcript $AT$ that shares the sum of their individual secrets. Our notion captures that if the secret shared by $AT$ contains at least one contribution from an honestly generated transcript, it should not be predictable. We then prove that several existing schemes satisfy this notion against adaptive corruptions in the algebraic group model.

- To motivate our new notion, we show that it implies the adaptive security of two recent random beacon protocols, SPURT (S&P '22) and OptRand (NDSS '23), who build on top of aggregatable PVSS schemes satisfying our notion of unpredictability. For a security parameter $\lambda$, our result improves the communication complexity of the best known adaptively secure random beacon protocols to $O(\lambda n^2)$ for synchronous networks with $t

Repeated modular squaring plays a crucial role in various time-based cryptographic primitives, such as Time-Lock Puzzles and Verifiable Delay Functions. At ACM CCS 2021, Thyagarajan et al. introduced “OpenSquare”, a decentralised protocol that lets a client delegate the computation of repeated modular squaring to third-party servers while ensuring that these servers are compensated only if they deliver valid results. In this work, we unveil a significant vulnerability in OpenSquare, which enables servers to receive payments without fulfilling the delegated task. To tackle this issue, we present a series of mitigation measures.

Over the last decade, online reputation has become a central aspect of our digital lives. Most online services and communities assign a reputation score to users, based on feedback from other users about various criteria such as how reliable, helpful, or knowledgeable a person is. While many online services compute reputation based on the same set of such criteria, users currently do not have the ability to use their reputation scores across services. As a result, users face trouble establishing themselves on new services or trusting each other on services that do not support reputation tracking. Existing systems that aggregate reputation scores, unfortunately, provide no guarantee in terms of user privacy, and their use makes user accounts linkable. Such a lack of privacy may result in embarrassment, or worse, place users in danger.

In this paper, we present StreetRep, a practical system for aggregating user reputation scores in a privacy-preserving manner. StreetRep makes it possible for users to provide their aggregated scores over multiple services without revealing their respective identities on each service. We discuss our novel approach for tamper-proof privacy preserving score aggregation from multiple sources by combining existing techniques such as blind signatures, homomorphic signatures and private information retrieval. We discuss its practicality and resiliency against different types of attacks. We also built a prototype implementation of StreetRep. Our evaluation demonstrates that StreetRep (a) performs efficiently and (b) practically scales to a large user base.

How can a model owner prove they trained their model according to the correct specification? More importantly, how can they do so while preserving the privacy of the underlying dataset and the final model? We study this problem and formulate the notion of zero-knowledge proof of training (zkPoT), which formalizes rigorous security guarantees that should be achieved by a privacy-preserving proof of training. While it is theoretically possible to design zkPoT for any model using generic zero-knowledge proof systems, this approach results in extremely unpractical proof generation times. Towards designing a practical solution, we propose the idea of combining techniques from MPC-in-the-head and zkSNARKs literature to strike an appropriate trade-off between proof size and proof computation time. We instantiate this idea and propose a concretely efficient, novel zkPoT protocol for logistic regression.

Crucially, our protocol is streaming-friendly and does not require RAM proportional to the size of the circuit being trained and, hence, can be adapted to the requirements of available hardware. We expect the techniques developed in this paper to also generally be useful for designing efficient zkPoT protocols for other relatively more sophisticated ML models.

We implemented and benchmarked prover/verifier runtimes and proof sizes for training a logistic regression model using mini-batch gradient descent on a 4~GB dataset of 262,144 records with 1024 features. We divide our protocol into three phases: (1) data-independent offline phase (2) data-dependent phase that is independent of the model (3) online phase that depends both on the data and the model. The total proof size (across all three phases) is less than $10\%$ of the data set size ($<350$~MB). In the online phase, the prover and verifier times are under 10 minutes and half a minute respectively, whereas in the data-dependent phase, they are close to one hour and a few seconds respectively.

The Algorand consensus protocol is interesting both in theory and in practice. On the theoretical side, to achieve adaptive security, it introduces the novel idea of player replaceability, where each step of the protocol is executed by a different randomly selected committee whose members remain secret until they send their first and only message. The protocol provides consistency under arbitrary network conditions and liveness under intermittent network partitions. On the practical side, the protocol is used to secure the Algorand cryptocurrency, whose total value is approximately $850M at the time of writing.

The Algorand protocol in use differs substantially from the protocols described in the published literature on Algorand. Despite its significance, it lacks a formal analysis. In this work, we describe and analyze the Algorand consensus protocol as deployed today in Algorand’s ecosystem. We show that the overall protocol framework is sound by characterizing network conditions and parameter settings under which the protocol can be proven secure.

User privacy is becoming increasingly important in our digital society. Yet, many applications face legal requirements or regulations that prohibit unconditional anonymity guarantees, e.g., in electronic payments where surveillance is mandated to investigate suspected crimes.

As a result, many systems have no effective privacy protections at all, or have backdoors, e.g., stored at the operator side of the system, that can be used by authorities to disclose a user’s private information (e.g., lawful interception). The problem with such backdoors is that they also enable silent mass surveillance within the system. To prevent such misuse, various approaches have been suggested which limit possible abuse or ensure it can be detected. Many works consider auditability of surveillance actions but do not enforce that traces are left when backdoors are retrieved. A notable exception which offers retrospective and silent surveillance is the recent work on misuse-resistant surveillance by Green et al. (EUROCRYPT’21). However, their approach relies on extractable witness encryption, which is a very strong primitive with no known efficient and secure implementations.

In this work, we develop a building block for auditable surveillance. In our protocol, backdoors or escrow secrets of users are protected in multiple ways: (1) Backdoors are short-term and user-specific; (2) they are shared between trustworthy parties to avoid a single point of failure; and (3) backdoor access is given conditionally. Moreover (4) there are audit trails and public statistics for every (granted) backdoor request; and (5) surveillance remains silent, i.e., users do not know they are surveilled. Concretely, we present an abstract UC-functionality which can be used to augment applications with auditable surveillance capabilities. Our realization makes use of threshold encryption to protect user secrets, and is concretely built in a blockchain context with committee-based YOSO MPC. As a consequence, the committee can verify that the conditions for backdoor access are given, e.g., that law enforcement is in possession of a valid surveillance warrant (via a zero-knowledge proof). Moreover, access leaves an audit trail on the ledger, which allows an auditor to retrospectively examine surveillance decisions.

As a toy example, we present an Auditably Sender-Traceable Encryption scheme, a PKE scheme where the sender can be deanonymized by law enforcement. We observe and solve problems posed by retrospective surveillance via a special non-interactive non-committing encryption scheme which allows zero-knowledge proofs over message, sender identity and (escrow) secrets.

Event date: 21 December to 23 December 2023
Submission deadline: 15 September 2023
Notification: 31 October 2023

Event date: 4 September to 7 September 2024

The research involves developing SW and HW security solutions and acceleration of core cryptographic primitives deployed at the Edge. The successful candidate will be part of a Horizon Network of Excellence for distributed, trustworthy, efficient and scalable AI at the Edge.
Responsibilities:

Security and privacy threat modeling.

Security evaluation and assessment.

Design and implement optimized privacy-preserving computation protocols for low-power general purpose processors.

Write research papers and project deliverables, and present findings at intessrnational conferences and workshops.
Specific skills required
To be considered for this position, you should have a PhD degree in computer science or mathematics or cryptography, with a strong publication track record in top venues. In addition, good communication and programming skills are required.

Closing date for applications:

Contact: jobs-cosic@esat.kuleuven.be

More information: https://www.esat.kuleuven.be/cosic/vacancies/