International Association
for Cryptologic Research

The rapid adoption of blockchain-based financial systems has been accompanied by a surge in illicit activities, including money laundering, ransomware payments, phishing scams, and terrorist financing, necessitating robust anomalous transaction detection mechanisms. Detecting anomalies in cryptocurrency transactions is critical, as undetected illicit activity can result in significant economic losses and undermine trust in digital financial systems. This systematic review examines the state-of-the-art in cryptocurrency anomaly detection, with particular focus on methodological developments between 2008 and December 2025. A PRISMA-guided systematic literature search was conducted across IEEE Xplore, Scopus, Web of Science, ACM Digital Library, Google Scholar, and SpringerLink. From an initial set of 450 records, 32 empirical studies were selected after rigorous screening and eligibility assessment and included in the qualitative synthesis. Unlike prior surveys, this review provides a focused synthesis of empirical cryptocurrency transaction studies, a taxonomy of anomaly types, and a critical assessment of dataset bias and evaluation practices. The literature reveals a clear methodological shift from traditional feature-engineered machine learning approaches (e.g., Random Forest, XGBoost, and Support Vector Machines) toward graph-based deep learning architectures. Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), demonstrate competitive performance by capturing relational dependencies among blockchain addresses, while temporal graph models and hybrid GNN–transformer architectures enhance the detection of evolving, multi-hop laundering schemes. Unsupervised and semi-supervised approaches address the challenge of limited labeled data but introduce trade-offs in interpretability. Emerging research directions include privacy-preserving federated learning and cross-chain detection frameworks. Despite some studies reporting accuracies exceeding 90%, the field faces several limitations, including dataset bias, lack of standardized multi-chain benchmarks, inconsistency in evaluation metrics, limited adversarial robustness testing, scalability constraints, and insufficient explainability for regulatory compliance. This review aims to provide researchers and practitioners with a structured synthesis of current methodologies, a comprehensive taxonomy of anomalies, and a detailed roadmap for transitioning from experimental validation to real-world, scalable deployment.

Anonymous-credential (AC) schemes equip users with credentials on attested attributes such that users can later prove possession of a credential certifying (a subset of) those attributes without revealing anything else. In standard AC schemes, such proofs reveal the issuer of the credential, which may be more information than intended or necessary. Lately, there has been significant interest in designing stronger issuer-hiding anonymous-credential schemes that only reveal that the user has a credential from an issuer in a certain policy set.

Katz and Sefranek recently showed how to add issuer hiding to BBS-based anonymous credentials. However, their scheme requires per-verifier policy keys with corresponding secret keys needed for verification; this means proofs are no longer publicly verifiable, and may pose a barrier to practical deployment. As another drawback, security of their scheme relies on the generic group model (GGM).

In this work, we propose a template for constructing issuer-hiding, BBS-based anonymous credentials that does not require policy keys and whose security can be reduced to security of the BBS signature scheme (in particular, without relying on the GGM). At the core of our template is a technique to randomize BBS public keys and adapt signatures accordingly, which we show also has applications to tight multi-user security of BBS signatures. We design, implement, optimize, and experimentally compare various instantiations of our template that offer tradeoffs in proving time, verification time, and proof size. All instantiations offer good performance for policy sets of up to 64 issuers.

Trustworthy federated learning requires both update privacy and aggregate integrity. While secure aggregation protects the confidentiality of client updates, it does not prevent a malicious server from tampering with the final aggregate. Existing verifiable schemes typically address this limitation by introducing separate integrity-verification layers, such as zero-knowledge proofs, homomorphic hashes, and commitmentbased mechanisms. However, these approaches either require clients to remain online after uploading their updates for additional verification or recovery procedures, or incur substantial computation overhead that scales linearly with the model dimension. To address these limitations, we propose SealAgg, a one-shot verifiable secure aggregation framework that simultaneously guarantees update confidentiality and aggregate integrity against a malicious server. SealAgg allows clients to disconnect immediately after uploading their updates and introduces an aggregatenative auditing mechanism that enables lightweight integrity verification without a separate costly verification pipeline. Specifically, each client embeds dual hidden finite-field linear projections and a context-binding heartbeat value into its update before encryption, so that the audit material is co-aggregated with the gradients along the same path. Moreover, we design an asymmetric three-server architecture that confines integrity validation entirely to the server side, thereby fully supporting one-shot client participation. We provide formal analyses of the correctness and security of SealAgg. Extensive experiments show that SealAgg achieves practical end-to-end efficiency, incurs only a small auditing overhead relative to the total cost, and outperforms state-of-the-art schemes.

We address the question of realizing privacy preserving analysis of user data. The abstract scenario considered is that an analyst aims to evaluate a function $f$ on some user data $X$. To achieve comprehensive privacy, it is necessary to protect the input $X$ directly. However, it is known that $f(X)$ may leak too much information about $X$ as well. A common approach to mitigate such risks is to make the computation differentially private. In practice, this is often accomplished by replacing $f$ by a noisy variant $f^*$.

We investigate the use of multi-input functional encryption (MIFE) for achieving input and output privacy in one cryptographic mechanism. In an MIFE scheme, a setup authority can generate restricted decryption keys which enable to learn specific functions of encrypted messages, without revealing any additional information. To achieve differential privacy in this process, we introduce as a new cryptographic primitive: noisy multi-input functional encryption (NMIFE). It extends the concept of MIFE such that the decryption key may also encode a noisy function where the noise value is secret.

While the change from MIFE to NMIFE is rather straightforward, the challenge is to come up with precise and workable definitions of correctness and security that we propose and explain in this work. Here, the security definition is tailored to the use case of differential privacy. As it is a special case of the established notion of full-hiding security, we present a generic transformation that enables turning any full-hiding MIFE scheme into a secure NMIFE scheme that has practically the same performance as the initial MIFE scheme.

Moreover, we make use of the fact that the proposed security definition is less restrictive and present a new concrete NMIFE scheme for evaluating the inner product. It is dubbed DiffPIPE (short for DIFFerentially Private Inner Product Evaluation). DiffPIPE is not the result from the transformation and outperforms all from existing full-hiding MIFE schemes constructed NMIFE schemes. In experiments, we demonstrate its applicability for realizing privacy preserving counting queries on data sets.

As digital identity systems gain traction around the world, many see privacy-enhancing technologies (PETs) as the key to ensuring safe deployment. We critically examine whether this is the case using the European Digital Identity Framework (EUDIF) as an example. We leverage techniques from cryptographic modeling to formally capture the necessary leakage of the functionality of the EUDIF and its proposed applications. Then, we develop a harm analysis methodology that illustrates, using harm trees, how this leakage — and other constraints stemming from design decisions or the context of deployment — lead to harms. Moreover, our harm modeling enables us to distinguish between which pathways to harm are inherent to the core functionality, and which pathways can be prevented with PETs. Our analysis shows that, while PETs can reduce information flows, they fall short in mitigating the harms that deploying digital identity can bring to individuals and society.

Post-quantum migration in Transport Layer Security (TLS) requires evidence-aware measurements that distinguish session negotiation, endpoint capability, certificate-chain evidence, and the provenance of missing observations. This distinction is essential under TLS 1.3 encryption, resumption, mutual TLS, trace truncation, fragmentation, coalescing, active certificate retrieval, and temporal drift. We present a multi-surface framework for post-quantum TLS observability. The framework separates passive session evidence, active probing, certificate-chain evidence, and registry knowledge, and maps them onto measurement planes for session behavior, key establishment, endpoint capability, authentication, lifecycle, observability, and policy. We instantiate it as a reproducible artifact with schema-enforced observations and results, versioned registries, auditable inference rules, stress contracts, and baseline adapters. We evaluate the framework on 29 controlled scenarios spanning TLS 1.2 and TLS 1.3, classical and hybrid key establishment, mutual TLS, resumption, HelloRetryRequest, truncation, fragmentation and coalescing, temporal drift, IPv6, and chain-depth variation. Passive evidence closes session-level planes, active probing establishes capability lower bounds, and multi-surface evidence closes the full measurement object while preserving uncertainty and contradiction when required. Against an inherited TLS quantum-vulnerability analyzer, the baseline detects 2 of 29 runs and 0 of 23 TLS 1.3 runs. In a stratified public campaign over 1000 targets and 2000 fresh probes, the framework completes 1971 handshakes, collects 1368 chain artifacts, confirms hybrid capability for 310 targets, and identifies 310 cases where endpoint capability exceeds what any single classical session view reveals. These results support post-quantum TLS readiness assessment as a structured observability problem based on explicit evidence surfaces, per-plane closure, active corroboration, source linkage, and first-class treatment of unknown, na, ambiguity, and contradiction.

Cryptography, Security & Privacy Research Group at Koç University has multiple openings for summer research interns (at both undergraduate and graduate level). Topics include cryptography, cyber security, blockchains, artificial intelligence and machine learning, game theory and mechanism design. Apply via:

https://research.ku.edu.tr/research-outreach/summer-research/kusrp/

For more information about joining our group and projects, visit

https://crypto.ku.edu.tr/

All applications must be completed online. Applications with missing documents will not be considered. Applications via e-mail will not be considered. Application Requirements:

1. CV
2. 2 Recommendation Letters
3. Official transcripts from all the universities attended
4. Statement of Purpose

Internships are unpaid, on a voluntary basis, but can be performed remotely.

Deadline is 16 May 2026.

Closing date for applications:

Contact: https://research.ku.edu.tr/research-outreach/summer-research/kusrp/

More information: https://research.ku.edu.tr/research-outreach/summer-research/kusrp/

Cryptography, Security & Privacy Research Group at Koç University has multiple openings at every level. Accepted Computer Science and Engineering applicants may receive competitive scholarships including monthly stipend, tuition waiver, housing (accommodation) support, health insurance, computer, travel support, and lunch meal card.

Your duties include performing research on cryptography, cyber security, and privacy in line with our research group's focus, assisting teaching, as well as collaborating with other graduate and undergraduate students. Computer Science, Mathematics, Cryptography, or related background is necessary.

All applications must be completed online. Applications with missing documents or exam scores will not be considered. Applications via e-mail will not be considered. Application Requirements:

1. CV
2. Recommendation Letters (2 for MSc, 3 for PhD)
3. TOEFL score (for everyone whose native language is not English, Internet Based: Minimum Score 80)
4. GRE score
5. Official transcripts from all the universities attended
6. Statement of Purpose

https://gsse.ku.edu.tr/en/application/

Deadline: 15 May 2026.

For more information about joining our group and projects, visit

https://crypto.ku.edu.tr/

Closing date for applications:

Contact: https://gsse.ku.edu.tr/en/application/

More information: https://gsse.ku.edu.tr/en/application/

The cryptography group at IBM Research in Zurich is looking to hire a Ph.D. student to work on constructions and applications of lattice-based zero knowledge proofs. The group is one of the world-leaders in quantum-safe cryptography research and has significantly contributed to all three lattice-based NIST quantum-safe standards. Our current research emphasis is on practical zero-knowledge proofs based on the same foundations and their application to privacy-preserving cryptography. A motivated researcher should find this to be a very exciting environment to work in. A strong background in (applied) mathematics and some experience with cryptography is desirable. Additionally, the ideal candidate is someone who has a strong interest in high-performance implementations on modern CPU and/or GPU architectures. Zurich is consistently ranked as one of the top cities for living standards and the immediate proximity of lakes and mountains to the lab allows for the pursuit of numerous hobbies. IBM is committed to fostering diversity and inclusion in the workplace. You will join an open, multicultural research environment that values different perspectives and supports flexible working arrangements. Our goal is to help all genders and backgrounds thrive professionally while maintaining a healthy work–life balance.

Closing date for applications:

Contact: Please apply via our career webpage: https://www.zurich.ibm.com/careers/2026_013.html

More information: https://www.zurich.ibm.com/careers/2026_013.html

The Fraunhofer-Gesellschaft (www.fraunhofer.com) is one of the world's leading organizations for application-oriented research. 75 institutes develop pioneering technologies for our economy and society – more precisely: 32 000 people from technology, science, administration and IT. They know: Anyone who comes to Fraunhofer wants to and can make a difference. For themselves, for us and for the markets of today and tomorrow. ATHENE is the National Research Center for Applied Cybersecurity and is one of the leading institutions in Europe and worldwide. Our contributors include the Fraunhofer Institutes SIT and IGD as well as TU Darmstadt, Goethe University Frankfurt, and Darmstadt University of Applied Sciences. Through excellence-driven research, we jointly develop innovative solutions to pressing security and data protection challenges and work closely with national and international partners, including leading companies, universities, and research institutions in Europe, the USA, and Israel. Our department, Applied Cybersecurity, focuses cutting-edge problems in the foundations and applications of cryptography, including privacy‑enhancing technologies, secure and verifiable computation, and securing AI-driven system operating in adversarial environments. The department offers a dynamic and growing research team, with a collaborative and supportive environment for research and learning.

Closing date for applications:

Contact: Prof. Dr. Adi Akavia

More information: https://jobs.fraunhofer.de/job/Darmstadt-Postdoctoral-Researcher-Cryptography-&-AI-%28Foundations-and-Applications%29-64295/1377969033/

We have a PhD internship position at Nokia Bell Labs in Belgium (Antwerp) for the autumn (September onwards):

Your profile (at least 3 of these):

• You have experience with FHE (+ applications).
• You have experience with MPC (preferred).
• You have good knowledge of lattices, and you are interested in PQ protocols.
• Not afraid of programming, using your favourite AI.

Our offer (at least 3 of these):

• 3 to 4 months internship.
• Fully paid.
• Office side by side with the Antwerp Zoo, working next to the rhinos.
• Helicopter landing pad for your vehicle.

You must be enrolled as a PhD student at a university. The salaries are on a European scale (at the Belgian PhD scholarship level).

Closing date for applications:

Contact: Emad Heydari Beni ([email protected])

Event date: 11 October 2026
Submission deadline: 30 June 2026
Notification: 21 August 2026

Event date: 10 May to

Event date: 26 November to 28 November 2026
Submission deadline: 15 July 2026
Notification: 20 September 2026

Event date: 2 November to 4 November 2026
Submission deadline: 26 July 2026
Notification: 15 September 2026

Event date: 1 September to 3 September 2026

In secret-key private information retrieval (SK-PIR), the client in an offline phase processes the database using a short secret key. In the online phase the client could then use the secret key to make queries to the server, without revealing the entries accessed, and using only sublinear communication $o(N)$ in the database size $N$. While (non-SK) PIR requires public-key cryptography, recent work provides evidence that SK-PIR may not. In particular, Chen, Ishai, Mour, and Rosen (STOC 26) construct SK-PIR with communication $N^{\varepsilon}$, for any $\varepsilon$, from high-noise LPN, which is not known to imply public-key cryptography.

We construct SK-PIR with online communication $\tilde{O}(\sqrt{N)}$, under the minimal assumption of one-way functions. More generally we can achieve client-to-server communication $\tilde{O}(N_c)$ and server-to-client communication $\tilde{O}(N_s)$ as long as $N_c \cdot N_s \geq N$.

Our construction is simple and is based on garbled circuits satisfying an uncorrelated input encoding property. We show that this property is satisfied by point and permute schemes from the literature.

Hash-based SNARKs are arguably the most prominent and widely deployed class of transparent SNARKs, owing to desirable properties such as plausible post-quantum security and the avoidance of public-key cryptography. The key ingredients in the construction of hash-based SNARKs are an error-correcting code and an Interactive Oracle Proof of Proximity (IOPP) for that code. A particularly desirable property for hash-based SNARKs is field-agnosticism, i.e., the ability to natively prove statements over arbitrary fields (of sufficient size). We advance the study of field-agnostic hash-based SNARKs as follows:

$\bullet{}$ We introduce a new family of linear-time encodable field-agnostic error-correcting codes called Encode-Repeat-Accumulate (ERA) codes. These codes achieve a strong trade-off between encoding time and relative distance, the two properties that largely determine the commitment time and proof size of the resulting hash-based SNARK. We also show that ERA codes are efficiently codeswitchable (Ron-Zewi and Rothblum [JACM 2024]), i.e., we can efficiently reduce the task of checking the proximity of a word to an ERA code to checking the proximity of a related word to an arbitrary code.

$\bullet{}$ We present an IOPP for (interleaved) ERA codes that leverages their fast encoding time, strong distance guarantees, and efficient codeswitchability to achieve concretely small query complexity (and hence small proof size in the resulting hash-based SNARK) while maintaining good prover efficiency.

The Recording Standard Oracle with Errors (RstOE) technique is an important tool for quantum security proofs in symmetric-key cryptography. However, against adaptive quantum chosen-plaintext adversaries, traditional RstOE-based proofs may suffer from the ``trivialization of norm” problem. This issue arises from three main causes: the delayed evaluation of bad events, the presence of unrecorded external variables, and the unconstrained independence of intermediate variables. To address this obstacle, we propose two refinements of the RstOE methodology, namely Pure-Database Substitution and Early-Defense. Among them, Pure-Database Substitution algebraically eliminates unrecorded external variables and reformulates collision constraints in terms of internal database records. Building on this substitution, Early-Defense moves the collision check to the point at which a new internal variable is sampled. Because the bad event now depends on this freshly generated quantum randomness, only a negligible fraction of the $2^n$ superposition branches will satisfy the collision constraint. This reduces the amplitude of transitions into the bad subspace and avoids the $O(1)$ norm collapse. We demonstrate the method on TNT as a case study and outline extensions to EDMQ, EDMDQ, LRWQ and QPMAC.

Broadcast encryption (BE) allows a sender to succinctly encrypt a message to any dynamically chosen subset of recipients. The gold-standard for BE is optimal succinctness (parameters independent of the number of users) and adaptive security, and attaining both from falsifiable post-quantum assumptions has been a central open problem. Recently, Goyal and Yadugiri (GY) gave the first adaptively-secure and optimally-succinct slotted distributed BE under a falsifiable lattice assumption, but their techniques inherently require an a-priori bound on the number of users and a slotted user structure. Two highly-sought-after generalizations thus remained open: flexible BE (FBE), where users asynchronously sample and register their own keys; and identity-based BE (IBBE), where a trusted authority issues keys for identities drawn from a super-polynomially large space.

In this work, we present the first adaptively-secure FBE and IBBE schemes with all parameter sizes independent of the number of users, both under the same falsifiable lattice assumption (decomposed LWE) and in the same model (Random Oracle Model) as the prior state-of-the-art for slotted distributed BE. Our FBE additionally enjoys a transparent setup, in line with the trustless ethos motivating distributed and flexible BE. At the technical heart of our results, we extend the equivocal encryption framework of GY to capture unbounded and dynamic broadcast systems, and introduce Equivocal Matrix Commitments---a strengthening of matrix commitments that supports adaptive equivocation of the committed matrix. We expect this new abstraction to find broader applications in designing adaptively-secure trustless lattice-based encryption.