International Association
for Cryptologic Research

Who you are:
The internship is ideally intended for senior undergraduate/master students, PhD candidates, or early postdocs in one of the fields with relevance to blockchain systems, such as computer science, applied mathematics, cryptography, or economics. It is a perfect opportunity for an early-stage researcher to gain valuable research experience by collaborating with members of the IOG Research team on current challenges in blockchain technologies.

What the role involves:
The intern will work on an Internship Project that will be defined prior to the commencement of the internship, taking into account the intern’s scientific background and skillset, as well as the research priorities within IOG.

The work will be done under the guidance of a supervisor, who will be one of the members of IOG Research. Supervisors will contribute to defining the scope of the Internship Project, track the intern’s progress, provide guidance, and ensure that the work done is aligned with the broader research carried out at IOG Research.

The duration of the internship is up to 3 months and is primarily intended to take place during summer 2025, although other time periods may be considered.

Closing date for applications:

Contact: Sandro Coretti-Drayton

More information: https://apply.workable.com/io-global/j/0BC29938F1/

EPITA : École d'Ingénieurs en Informatique is offering several teaching/research positions (MCF and PR profile) in computer science within the EPITA Research Laboratory (LRE) for the start of the 2025-2026 academic year.
The LRE, https://www.lre.epita.fr, is attached to the "EDITE doctoral school" in Paris (Sorbonne University). It was evaluated by Hcéres in 2017-2018, and is currently being evaluated (wave 2024-2025). We are recruiting to strengthen the five LRE teams, in particular the Security and Systems team (https://www.lre.epita.fr/systems/), for the Paris, Rennes and Toulouse sites in the following areas:

• For the Paris site :
• Cryptography
• Post-quantum standards, protocols and primitives
• Automatic analysis
• Blockchain
• Learning detection and security
• Attack detection and analysis
• Security of learning models
• Software and hardware security
• Virology and malware analysis
• Reverse engineering at assembler and hardware level
• Systems
• Operating systems and kernels
• Cloud computing and virtualisation
• Embedded systems
• For the Rennes site:
• Static and dynamic analysis of malicious software
• Instrumentation and tools for analysis and monitoring
with teaching interventions mainly in the DevSecOps major.
• For the Toulouse site:
• The dedicated job description for an HDR or ‘almost HDR’ profile is here: https://tinyurl.com/PosteEpitaToulouseHDR2025

Closing date for applications:

Contact: [email protected]; [email protected]

More information: https://tinyurl.com/PostesEpitaSECUSYST2025

Position 1: One or two casual researchers in the field of Privacy-preserving Machine Learning, for a few hundred hours each (the exact number of hours is negotiable and depends on the availability of the candidate/s).
Expectations: to produce top-tier journal paper/s in the field of Privacy-preserving Machine Learning.

Position 2 : A casual developer with the following skill set required, for a few hundred hours (the exact number of hours is negotiable and depends on the availability of the candidate):
1. Swift (for a task specifically for iOS), and
2. Java (for Android app development), and
3. TensorFlow.js (for a specific task), and
4. Java or PHP or C# (for web page development), and
5. HTML and CSS and JavaScript (for UI design).
Expectations : to continue with some existing development work by polishing and finalizing the mobile app development.

Note : The successful candidates for both the positions above must be physically based in Australia with working rights in Australia when the work is being done.

Closing date for applications:

Contact: Dr. Zhaohui (Linda) Tang at:
[email protected]

The zero-knowledge group (a subgroup of the cryptography group) at the Institute of Computer Science of the University of Tartu seeks one postdoctoral researcher and one Ph.D student in contemporary zk-SNARKs. The existing zero-knowledge group comprises Helger Lipmaa, Janno Siim, and Ph.D students. Our current research interests include the provable security of zk-SNARKs (including more stringent security notions and more realistic cryptographic assumptions), the design of pairing-based, code-based, and lattice-based zk-SNARKs, and the design of zk-SNARKs for applications like zkVM and zkML. We collaborate actively with local groups on coding theory and machine learning to further our aims. While primarily focused on academic publishing, we are interested in collaborating with ZK companies.

The postdoctoral researcher should have a strong track record in areas related to the design and analysis of efficient zero-knowledge proofs. We expect the candidate to have published a few papers at IACR conferences or venues of equivalent renown. The Ph.D student must have an MSc or equivalent by this spring, a strong mathematics and/or theoretical computer science background, and an existing cryptography background. We welcome all exceptional candidates. We especially welcome candidates with a background in PQ zk-SNARKs (hash-based or lattice-based) or applications like zkML; in the case of the Ph.D student, we interpret it as a background either in coding theory, lattice-based cryptography, or machine learning.

T apply for the positions, submit a letter of motivation (clearly stating why this project and the applicant are a good match), a full research CV, names of two references, and a research statement (obligatory for the postdoctoral researcher), clearly indicating the sought position (postdoc or Ph.D student).

The postdoc position starts on August 1, 2025, or later and lasts 2-4 years, depending on the candidate and negotiations. The Ph.D. position starts on September 1, 2025, and lasts four years. The candidates may later seek further employment, but this is not guaranteed in advance. Application deadline: 25.04.2025.

Closing date for applications:

Contact: Helger Lipmaa Professor of Cryptography, Head of Chair

https://kodu.ut.ee/~lipmaa/

helger dot lipmaa at ut dot ee

More information: https://crypto.cs.ut.ee/Main/OpenPositions

The Applied Crypto group of the University of Luxembourg is offering a Ph.D. student and a post-doc position in cryptography. Possible topics of interests are fully homomorphic encryption, public-key cryptanalysis, and side-channel attacks and countermeasures. We offer a competitive salary. The duration of the position is 3 years (+ 1 year extension) for Ph.D., and 3 years for post-doc.

Closing date for applications:

Contact: Jean-Sebastien Coron - [email protected]

More information: http://www.crypto-uni.lu/vacancies.html

We are looking for a Ph.D. student to join the Digital Security group at Radboud University. The position is fully funded for 4 years.

The candidate will work on the hardware security of symmetric-key ciphers. Topics of interest include:

• hardware implementations
• side-channel analysis
• fault analysis
• investigation of countermeasures

You will spend about 10% of your time assisting with teaching at our department. This will typically include tutoring practical assignments, grading coursework, and supervising student projects.

Your profile You hold a Master’s degree in mathematics, computer science, engineering, or a related field or expect to obtain such a degree soon. You have good programming skills and some experience with at least one of the following: cryptography, side-channel attacks or hardware description languages. You have a strong interest in cryptography and embedded systems security and especially their real-world deployment.

To apply please visit: https://www.ru.nl/en/working-at/job-opportunities/phd-position-hardware-security-of-symmetric-key-ciphers
Only applications via the official portal will be considered. Application deadline: 31 March 2025 Start date: flexible

Closing date for applications:

Contact: Dr. S. Mella

More information: https://www.ru.nl/en/working-at/job-opportunities/phd-position-hardware-security-of-symmetric-key-ciphers

This paper presents a security analysis of the South Korean Format-Preserving Encryption (FPE) standards FEA-1 and FEA-2. In 2023, Chauhan \textit{et al.} presented the first third-party analysis of FEA-1 and FEA-2 against the square attack. The authors proposed new distinguishing attacks covering up to three rounds of FEA-1 and five rounds of FEA-2, with a data complexity of $2^8$ plaintexts. Additionally, using these distinguishers, they presented key recovery attacks for four rounds of FEA-1 and six rounds of FEA-2, for 192-bit and 256-bit key sizes. The complexities of both the four-round FEA-1 and six-round FEA-2 key recovery attacks are $2^{137.6}$. \\

In this work, we successfully extend the number of rounds attacked for both FEA-1 and FEA-2, using the square attack technique. Specifically, we present a four-round distinguishing attack against FEA-1 and six-round distinguishing attack against FEA-2. The data complexities of these distinguishers are $2^{64}$ plaintexts. Furthermore, we apply these distinguishers to perform key recovery attacks on five rounds of FEA-1 and seven rounds of FEA-2, targeting the 256-bit key size. The time complexities of the presented key recovery attacks are $2^{193.6}$.

The current landscape of system-on-chips (SoCs) security verification faces challenges due to manual, labor-intensive, and inflexible methodologies. These issues limit the scalability and effectiveness of security protocols, making bug detection at the Register-Transfer Level (RTL) difficult. This paper proposes a new framework named BugWhisperer that utilizes a specialized, fine-tuned Large Language Model (LLM) to address these challenges. By enhancing the LLM's hardware security knowledge and leveraging its capabilities for text inference and knowledge transfer, this approach automates and improves the adaptability and reusability of the verification process. We introduce an open-source, fine-tuned LLM specifically designed for detecting security vulnerabilities in SoC designs. Our findings demonstrate that this tailored LLM effectively enhances the efficiency and flexibility of the security verification process. Additionally, we introduce a comprehensive hardware vulnerability database that supports this work and will further assist the research community in enhancing the security verification process.

CHide is one of the most prominent e-voting protocols, which, while combining security and efficiency, suffers from having very long encrypted credentials. In this paper, starting from CHide, we propose a new protocol, based on multiparty Class Group Encryption (CGE) instead of discrete logarithm cryptography over known order groups, achieving a computational complexity of $O(nr)$, for $n$ votes and $r$ voters, and using a single MixNet. The homomorphic properties of CGE allow for more compact credentials while maintaining the same level of security at the cost of a small slowdown in efficiency.

The ETSI Technical Specification 104 015 proposes a framework to build Key Encapsulation Mechanisms (KEMs) with access policies and attributes, in the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) vein. Several security guarantees and functionalities are claimed, such as pre-quantum and post-quantum hybridization to achieve security against Chosen-Ciphertext Attacks (CCA), anonymity, and traceability. In this paper, we present a formal security analysis of a more generic construction, with application to the specific Covercrypt scheme, based on the pre-quantum ECDH and the post-quantum ML-KEM KEMs. We additionally provide an open-source library that implements the ETSI standard, in Rust, with high effiency.

In order to compute a multiple of a point on an elliptic curve in Weierstrass form one can use formulas in only one of the two coordinates of the points. When one computes with these $x$-only formulas one says that one computes on the Kummer line. Similarly, it is easy to define the Kummer line of an elliptic curve in theta coordinates. In this article we give a unified definition of what is a Kummer line.

Using Mumford's theory of the theta group and defining the isomorphism of Kummer lines, we obtain that there are only two types of Kummer lines. The same theory allows to give conversion formulas between Kummer models in a unified manner.

We also classify curves that admit these different models via Galois representation and modular curves. When an elliptic curve is viewed inside a $2$-volcano we give criteria to say if it has a given Kummer model based solely on its position in the volcano. We give applications to the ECM factorization algorithm.

Over two decades since their introduction in 2005, all major verifiable pairing delegation protocols for public inputs have been designed to ensure information-theoretic security. However, we note that a delegation protocol involving only ephemeral secret keys in the public view can achieve everlasting security, provided the server is unable to produce a pairing forgery within the protocol’s execution time. Thus, computationally bounding the adversary’s capabilities during the protocol’s execution, rather than across its entire lifespan, may be more reasonable, especially when the goal is to achieve significant efficiency gains for the delegating party. This consideration is particularly relevant given the continuously evolving computational costs associated with pairing computations and their ancillary blocks, which creates an ever-changing landscape for what constitutes efficiency in pairing delegation protocols. With the goal of fulfilling both efficiency and everlasting security, we present AmorE, a protocol equipped with an adjustable security and efficiency parameter for sequential pairing delegation, which achieves state-of-the-art amortized efficiency in terms of the number of pairing computations. For example, delegating batches of 10 pairings on the BLS48-575 elliptic curve via our protocol costs to the client, on average, less than a single scalar multiplication in G2 per delegated pairing, while still ensuring at least 40 bits of statistical security.

Power side-channel (PSC) vulnerabilities present formidable challenges to the security of ubiquitous microelectronic devices in mission-critical infrastructure. Existing side-channel assessment techniques mostly focus on post-silicon stages by analyzing power profiles of fabricated devices, suffering from low flexibility and prohibitively high cost while deploying security countermeasures. While pre-silicon PSC assessments offer flexibility and low cost, the true nature of the power signatures cannot be fully captured through RTL or gate-level design. Although physical design-level analysis provides precise power traces, collecting data is time and resource-consuming at the layout level. To address this challenge, we propose, for the first time, a fast and efficient physical design-level PSC assessment framework using a graph neural network (GNN). This framework predicts dynamic power traces for new layouts, using them to assess physical design security through metrics evaluation. Our experiments on AES-GF layout implementations achieve a tremendous 133 times speedup compared to conventional simulation-based flow without sacrificing substantial accuracy.

SNARKs are frequently used to prove encryption, yet the circuit size often becomes large due to the intricate operations inherent in encryption. It entails considerable computational overhead for a prover and can also lead to an increase in the size of the public parameters (e.g., evaluation key). We propose an encryption-friendly SNARK framework, $\textsf{Tangram}$, which allows anyone to construct a system by using their desired encryption and proof system. Our approach revises existing encryption schemes to produce Pedersen-like ciphertext, including identity-based, hierarchical identity-based, and attribute-based encryption. Afterward, to prove the knowledge of the encryption, we utilize a modular manner of commit-and-prove SNARKs, which uses commitment as a `bridge'. With our framework, one can prove encryption significantly faster than proving the whole encryption within the circuit. We implement various $\textsf{Tangram}$ gadgets and evaluate their performance. Our results show 12x - 3500x times better performance than encryption-in-the-circuit.

Blockchain advancements, currency digitalization, and declining cash usage have fueled global interest in Central Bank Digital Currencies (CBDCs). The BIS states that the hybrid model, where central banks authorize intermediaries to manage distribution, is more suitable than the direct model. However, designing a CBDC for practical implementation requires careful consideration. First, the public blockchain raises privacy concerns due to transparency. While zk-SNARKs can be a solution, they can introduce significant proof generation overhead for large-scale transactions. Second, intermediaries that provide user-facing services on behalf of the central bank commonly performs Proof of Liabilities on customers' static liabilities. However, in real-world scenarios where user liabilities can arbitrarily increase or decrease, the static nature poses such as window attacks.

In this paper, we propose a new smart contract-based privacy-preserving CBDC framework based on zk-SNARKs, called $\textbf{Aegis}$. our framework introduces a transaction batching technique to enhance scalability and defines a new dynamic PoL which is near-real time. We formally define the security models for our system and provide rigorous security proofs to demonstrate its robustness. To evaluate the system’s performance, we instantiate our proposed framework and measure its efficiency. The result indicates that, the end-to-end process, including proof generation for 512 transactions, takes approximately 2.8 seconds, with a gas consumption of 74,726 per user.

Digital signatures underpin identity, authenticity, and trust in modern computer systems. Cryptography research has shown that it is possible to prove possession of a valid message and signature for some public key, without revealing the message or signature. These proofs of possession work only for specially-designed signature schemes. Though these proofs of possession have many useful applications to improving security, privacy, and anonymity, they are not currently usable for widely deployed, legacy signature schemes such as RSA, ECDSA, and Ed25519. Unlocking practical proofs of possession for these legacy signature schemes requires closing a huge efficiency gap.

This work brings proofs of possession for legacy signature schemes very close to practicality. Our design strategy is to encode the signature's verification algorithm as a rank-one constraint system (R1CS), then use a zkSNARK to prove knowledge of a solution. To do this efficiently we (1) design and analyze a new zkSNARK called Dorian that supports randomized computations, (2) introduce several new techniques for encoding hashes, elliptic curve operations, and modular arithmetic, (3) give a new approach that allows performing the most expensive parts of ECDSA and Ed25519 verifications outside R1CS, and (4) generate a novel elliptic curve that allows expressing Ed25519 curve operations very efficiently. Our techniques reduce R1CS sizes by up to 200$\times$ and prover times by 3-22$\times$.

We can generate a 240-byte proof of possession of an RSA signature over a message the size of a typical TLS certificate (two kilobytes) in only three seconds.

In recent years, the intersection of deep learning and differential cryptanalysis has given rise to the emerging field of differential neural cryptanalysis, providing an efficient data-driven paradigm for security evaluation of modern cryptographic algorithms. Traditional differential cryptanalysis relies on manual search for high-probability differential characteristics, a process limited by the nonlinearity complexity of the algorithm. In contrast, differential neural cryptanalysis improves the efficiency and automation of the analysis by training neural networks to automatically extract statistical features from ciphertext pairs. As research advances, Lu et al. proposed improved related-key neural distinguishers for the SIMON and SIMECK algorithms. However, current methodologies for constructing related-key distinguishers remain highly specialized, lacking a generalized optimization framework to address diverse cryptographic structures.

This paper proposes a novel framework for constructing related-key neural differential distinguishers that optimizes three key components: dataset construction (multi-ciphertext multi-difference formats), differential path selection (structural filtering), and network architecture (DRSN for noise suppression). By applying this framework to two standardized algorithms, DES and PRESENT, our experiments demonstrate significant advancements. For DES, the framework achieves an 8-round related-key neural distinguisher and improves 6/7-round distinguisher accuracy by over 40%. For PRESENT, we construct the first 9-round related-key neural distinguisher, which outperforms existing single-key distinguishers in both round coverage and accuracy. Additionally, we employ kernel principal component analysis (KPCA) and K-means clustering to evaluate the quality of differential datasets for DES and PRESENT, revealing that clustering compactness strongly correlates with distinguisher performance. Furthermore, we propose a validation algorithm to verify differential combinations with cryptographic advantages from a machine learning perspective, identifying 'good' plaintext-key differential combinations. We apply this approach to the SIMECK algorithm, demonstrating its broad applicability. These findings validate the framework’s effectiveness in bridging cryptographic analysis with data-driven feature extraction and offer new insights for automated security evaluation of block ciphers.

The Fiat-Shamir transformation underlies numerous non-interactive arguments, with variants that differ in important ways. This paper addresses a gap between variants analyzed by theoreticians and variants implemented (and deployed) by practitioners. Specifically, theoretical analyses typically assume parties have access to random oracles with sufficiently large input and output size, while cryptographic hash functions in practice have fixed input and output sizes (pushing practitioners towards other variants).

In this paper we propose and analyze a variant of the Fiat-Shamir transformation that is based on an ideal permutation of fixed size. The transformation relies on the popular duplex sponge paradigm, and minimizes the number of calls to the permutation (given the amount of information to absorb and to squeeze). Our variant closely models deployed variants of the Fiat-Shamir transformation, and our analysis provides concrete security bounds that can be used to set security parameters in practice.

We additionally contribute spongefish, an open-source Rust library implementing our Fiat-Shamir transformation. The library is interoperable across multiple cryptographic frameworks, and works with any choice of permutation. The library comes equipped with Keccak and Poseidon permutations, as well as several "codecs" for re-mapping prover and verifier messages to the permutation's domain.

As artificial intelligence (AI) becomes increasingly embedded in high-stakes applications such as healthcare, finance, and autonomous systems, ensuring the verifiability of AI computations without compromising sensitive data or proprietary models is crucial. Zero-knowledge machine learning (ZKML) leverages zero-knowledge proofs (ZKPs) to enable the verification of AI model outputs while preserving confidentiality. However, existing ZKML approaches require specialized cryptographic expertise, making them inaccessible to traditional AI developers.

In this paper, we introduce ZKPyTorch, a compiler that seamlessly integrates ML frameworks like PyTorch with ZKP engines like Expander, simplifying the development of ZKML. ZKPyTorch automates the translation of ML operations into optimized ZKP circuits through three key components. First, a ZKP preprocessor converts models into structured computational graphs and injects necessary auxiliary information to facilitate proof generation. Second, a ZKP-friendly quantization module introduces an optimized quantization strategy that reduces computation bit-widths, enabling efficient ZKP execution within smaller finite fields such as M61. Third, a hierarchical ZKP circuit optimizer employs a multi-level optimization framework at model, operation, and circuit levels to improve proof generation efficiency.

We demonstrate ZKPyTorch effectiveness through end-to-end case studies, successfully converting VGG-16 and Llama-3 models from PyTorch, a leading ML framework, into ZKP-compatible circuits recognizable by Expander, a state-of-the-art ZKP engine. Using Expander, we generate zero-knowledge proofs for these models, achieving proof generation for the VGG-16 model in 2.2 seconds per CIFAR-10 image for VGG-16 and 150 seconds per token for Llama-3 inference, improving the practical adoption of ZKML.

Rank-1 Constraint Systems (R1CS) and Plonk constraint systems are two commonly used circuit formats for zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs). We present Plonkify, a tool that converts a circuit in an R1CS arithmetization to Plonk, with support for both vanilla gates and custom gates. Our tool is able to convert an R1CS circuit with 229,847 constraints to a vanilla Plonk circuit with 855,296 constraints, or a jellyfish turbo Plonk circuit with 429,166 constraints, representing a $2.59\times$ and $1.9\times$ reduction in the number of constraints over the respective naïve conversions.