Blockchain Papers

Nicolin Decker

No abstract is available for this record.

Carmen Wabartha

The end-to-end verifiable e-voting system Ordinos [26] is primarily characterized by its tally-hiding property, which ensures that only the actual election result, e. g., the winner of the election, is revealed while the full tally consisting of the aggregated votes stays hidden. Ordinos is an abstract model that guarantees tally-hiding, verifiability and vote privacy if the underlying cryptographic primitives satisfy certain requirements. It uses a multi-party-computation protocol over an additively homomorphic encryption scheme and guarantees active security with zero-knowledge proofs. Ordinos has already been instantiated for several election systems using the Paillier [35] encryption scheme, which can be broken by Shor’s algorithm [41]. The aim of this thesis is to instantiate Ordinos post-quantum secure using a variant of Regev’s LWE-based cryptosystem [39], which is adapted to realize an actively secure threshold encryption scheme over an arbitrary plaintext space. Then a noise analysis of the arithmetic and logical components used in the MPC-protocol of the Paillier instantiation is conducted, and the components are slightly adapted to restrict the noise growth. Additionally, valid zero-knowledge proofs are provided and a concrete instantiation achieving a security level of 128 bits is shown.

Ruiteng Zhang, Pingbin Luo, Qiong Huang

No abstract is available for this record.

Chen-Fan Chang, Ting‐Yu Chang, Chih-Chieh Chang, Te-Chuan Chiu · 6 authors

No abstract is available for this record.

Leah Nieboer

This dissertation is a novel, STATES OF ZERO, accompanied by a critical preface on rupture in the works of German artist Rebecca Horn and Korean American artist Theresa Hak Kyung Cha. The critical preface “ON RUPTURE, ESTRANGEMENT, & EXTENSIBILITY: THE NEUTRAL IN REBECCA HORN AND THERESA HAK KYUNG CHA,” investigates negating interruptions in Rebecca Horn and Theresa Hak Kyung Cha’s lives—a life-threatening illness and its ensuing chronic conditions in the former, and displacement, exile, and linguistic rupture in the latter—that shape their innovative, multimedia works and their understandings of subjectivity, embodiment, articulation, and relationship. Their compositions, pitched at hyperbolic limits of self and language, touch Maurice Blanchot’s neutral—that substance which both underlies and threatens to obliterate speech altogether. In this preface, I explore some ways Horn’s works, given these ruptures, are expressive of this touching of the neutral and discuss the possible senses, embodiments, and modes of relationship her works—staged at the intersection of the human, nonhuman, mechanical, and environmental—offer as a result. Her works, intended to touch and to hyperbolize the limits of sense, ask us to encounter previously unimaginable configurations of self and other, insisting we reckon with the essential plurality of self, as we approach what Blanchot considers “the impossible” utterance this mode of articulation allows. I then take up Korean American artist Theresa Hak Kyung Cha’s multimedia work to extend the conversation into the invitational nature of rupture and its socio-ethical-political implications. The novel, STATES OF ZERO, is a composition that lives in this tradition of rupture. Set in a fictional, totalitarian state, the novel follows Zero, a woman who finds herself paradoxically embedded in the heart of the state’s increasingly totalitarian structure through her work at its national laboratory, even as her increasing disability—caused by early chemical exposure in her environments and exacerbated by ongoing clinical invasion—makes her its powerful, if unintentional, dissident. This early rupture in her form ensures she cannot become—or conceive of—a singular subject imagined by the state. Instead, she becomes a person of increasing alterity, an “impossible” body articulated across human, environmental, mechanical, clinical, and nonhuman worlds, in an aporia of unresolvable crossings. At the bottom of these crossings is the mother—Zero’s own sick mother, as well as the state’s toxins, clinical invasions, heteronormative structures, releasing dreams, and queer relationships that have mothered her form into its present state. This results in a narrative form that is as experimental and polyvocal as Zero herself. The novel itself becomes an impossible body, suspended across varying times and spaces, mechanisms of recording and surveillance, an unnamed narrator(s), and Zero’s own consciousness. As Zero, the state, and their intertwined narratives reach a point of no return, the novel and its world is flooded—with a local storm, and a teeming, lyric passage expressive of Blanchot’s neutral, where Zero may, finally, conceive of an impossible form for becoming into a possible future.

Andrey Khalov, Olga Muratovna Ataeva

Background: Ontologies and knowledge graphs have become critical for structuring data into machine-interpretable knowledge, especially in dynamic domains like IT service management (ITSM). Traditional ontology engineering relies heavily on domain experts, making it costly and slow. This study investigates whether a domain-specific ontology can be extended from a top-level ontology without expert involvement, using the IT service management ontology (ITSMO) and the descriptive ontology for linguistic and cognitive engineering (DOLCE-lite) as a test case used in this study. Methodology: We propose an automated mapping approach integrating lexical approaches, embeddings, graph neural networks (GNN), and large language models (LLMs). Two primary mapping methods were developed: (1) embedding-based matching, computing cosine similarity between class embeddings from DOLCE and ITSMO; and (2) LLM-based matching, prompting a language model (GPT-4o) to evaluate class compatibility on a numeric scale. We also experiment with GraphSAGE GNN to enrich embeddings with ontology structure. Z-score clustering is applied to similarity scores to select top candidate mappings while filtering out outliers from the top cluster. The methodology operates with no annotated data and was validated using three-steps approach: GPT-4o as a surrogate expert for baseline class matching evaluation, expert spot-check, and OWL reasoner (Pellet and HermiT) to prove logical consistency (Glimm et al., 2014; Sirin et al., 2007). Results: The automated method successfully mapped ITSMO classes under DOLCE, yielding an integrated ontology (80 classes) that extends DOLCE into the ITIL domain with minimal expert intervention (expert consolidated suggestions into a result ontology). The LLM-based approach (GPT-4o) achieved the best performance with 73.5% accuracy for top-1 mappings and 82.4% for top-3 (cluster) inclusion. Transformer-based embeddings (e.g., DeBERTa) also performed well (up to 39.3% top-1, outperform random matching with 27.6% accuracy), but classical graph embeddings (RDF2Vec/Node2Vec) failed due to the small ontology size. Incorporating a GNN provided smoother embedding distributions and increased correct mappings within top-3 clusters, but it slightly reduced top-1 precision in this small-graph setting. These findings underscore the effectiveness of LLMs in zero-shot ontology alignment and the limitations of purely structural methods on limited data. Conclusions: This work demonstrates, as a proof-of-concept, that an upper-level ontology can be extended to a domain ontology automatically, with no or minimal expert involvement, by leveraging AI-based mapping techniques. The resulting new ontology integrates ITSMO into DOLCE, providing a consistent semantic foundation for IT domain knowledge graphs. The approach is immediately applicable to ITSM and suggests a generalizable framework for ontology expansion in other domains. Future work will focus on scaling the method to larger ontologies, automatically discovering new classes/relations from text, and evaluating the approach’s practical impact on IT service management processes.

Luca Campa, Arnab Roy

No abstract is available for this record.

Gabriel Silva Atencio

The growth of the Metaverse brings new security problems that traditional perimeter-based defenses can’t manage. This research proposes and tests an integrated Zero-Trust Architecture aimed to solve these weaknesses by merging artificial intelligence (AI)-driven behavioral threat detection, blockchain-based decentralized identification, and post-quantum cryptography. For anomaly detection, the architecture uses a federated ResNet-50 model; for data management that meets regulatory standards, it uses a Hyperledger Fabric-based identification system with Zero-Knowledge Succinct Non-Interactive Argument of Knowledge; and for key exchange that is immune to quantum attacks, it uses the CRYSTALS-Kyber algorithm. Penetration testing, a Delphi study with 20 experts, and user surveys all show that the architecture greatly improves security metrics. This system has a False Acceptance Rate (FAR) of 5.2%, which is 42.7% lower than the 9.1% FAR baseline of rule-based systems, 99.1% protection against Sybil attacks, and strong quantum resilience with a 1.2× latency penalty compared to AES-256. The approach also partially complies with the General Data Protection Regulation by using cryptographic erasure proofs. But these security improvements come at a cost: AI inference now uses 3.1 times more graphics processing unit resources. The results show that the suggested architecture creates a scalable, empirically validated basis for protecting decentralized virtual environments, striking a good balance between security, compliance, and performance trade-offs.

Authors unavailable

Research on proof in mathematics is extensive, as is research on students' reasoning in linear algebra. However, few studies specifically investigate how students engage with proof in linear algebra (Stewart et al., 2019), highlighting a gap at the intersection of these two well-studied areas. Linear algebra is often one of the first undergraduate courses where students encounter formal proofs (Carlson, 1993), making it a valuable context for exploring early proof experiences at the undergraduate level. In this domain, particularly in linear dependence and independence, students often show a disconnect in their reasoning about procedures and concepts. They may rely on algorithmic techniques such as row reduction without fully understanding how these procedures connect to formal definitions or solution structures (Dogan, 2019). This dissertation investigates how students' concept images influence the proof methods they employ when reasoning about linear dependence and independence. This study draws on undergraduate students' written responses to an instructional sequence designed in Desmos to support proof comprehension, validation, and construction, with a focus on linear dependence and independence. Using Stylianides' (2008) framework for reasoning and proving, the responses of 54 students were analyzed to categorize their approaches to proof construction. To examine students' concept images, Tall and Vinner's (1981) theory was applied to a subset of 18 students to investigate their understanding across multiple activities in the sequence. Both proof construction and concept images were qualitatively analyzed; the former was coded using an existing framework, while the latter involved identifying emergent features from the data. To explore the relationship between students' proof construction and concept images, a qualitative analysis was first conducted with 18 students, identifying key features in their concept images and examining how these related to their approaches to proof construction. A subsequent quantitative component broadened this analysis through a crosstab of all 54 students and a multinomial logistic regression on a subset of 36 students to examine significant associations. The analysis of responses from 54 students revealed a diverse range of approaches to proof construction. Of these, 17 students (31.5%) employed deductive methods, 10 (18.5%) used rationale-based methods, 9 students (16.7%) relied on empirical arguments, and 18 (33.3%) produced unclear responses or arguments that did not address the prompt. These approaches varied in formality, accuracy, and completeness, reflecting differences in how students proved a mathematical statement about linear dependence. The analysis of concept images from 18 students showed varied interpretations across four key features: students' interpretations of linear combinations, trivial solutions, non-trivial solutions, and definitions of linear dependence and independence. Students reasoned about linear combinations in three ways: as multiple representations of vector relationships, as combinations involving all vectors in a set, or as expressing one vector in terms of others. Most characterized trivial solutions as solutions (to the homogeneous vector equation) with all scalar coefficients being zero, though some held interpretations not aligned with the formal concept definition, such as the zero vector on the right-hand side of the homogeneous vector equation. Similarly, non-trivial solutions were generally seen as including at least one non-zero scalar, though some students held conflicting or non-aligned interpretations. The greatest variation appeared in interpretations of trivial and non-trivial solutions, for example, some defined a non-trivial solution as one in which all scalar coefficients are zero, while others described it as one in which all coefficients are non-zero. The qualitative analysis revealed that students who produced deductive proofs tended to hold more consistent and formal interpretations of key concept image features. In contrast, those who produced empirical or rationale-based proofs exhibited greater variation in their concept image features, particularly interpretations not aligned with formal definitions. These findings suggest a meaningful connection between students' proof construction and their concept images. A quantitative analysis using multinomial logistic regression with 36 students revealed modest relationships between concept image features and proof method. Across models, students' interpretation of the trivial solution emerged most consistently, although the associations were not statistically conclusive. This study suggests that concept images aligned with formal definitions may be necessary but not sufficient for constructing deductive proofs. Some students who understood formal definitions did not consistently produce deductive proofs. Among the concept image features, the interpretation of the trivial solution appeared most consistently linked to students' approaches. This finding points to the importance of addressing students' concept images in instruction, as well as providing guidance on the nature and structure of deductive proofs. Future research could explore how concept images interact with other forms of knowledge, such as strategic or procedural knowledge, to more effectively support proof development.

Rohan Sharma

No abstract is available for this record.

Jakranpally Karthik

No abstract is available for this record.

Jonas Ryan Nouri

No abstract is available for this record.

Chen Sun, Nurshazwani Muhamad Mahfuz, WANG YALI

No abstract is available for this record.

Yuval Ishai, Eyal Kushilevitz, Varun Narayanan, Rafail Ostrovsky · 5 authors

No abstract is available for this record.

Özge Taş

Summary Zero-Knowledge Proof Machine Learning (ZKML) is a new approach that combines zero-knowledge proofs (ZKP) with machine learning (ML) to develop privacy-focused and secure artificial intelligence systems. ZKP are cryptographic techniques that enable one party to prove the validity of certain information without disclosing any additional data. This mechanism is particularly important in fields that require high levels of privacy, such as finance, healthcare, and identity verification. ZKML enables the cryptographic verification of the accuracy of model inferences or training processes without disclosing model parameters or user data. In the context of federated learning, the accuracy of each participant's contribution to the model training process can be verified using proof systems such as zk-SNARKs, thereby enabling a secure collaboration environment without the risk of data leakage. Similarly, during the inference phase, it can be verified whether the model produced a specific output, which builds trust in fields such as medicine and finance where sensitive decisions are made. Currently, the production of ZK proofs requires high computing power. However, thanks to advances in hardware, distributed systems, and cryptography, proof production is now more feasible even for larger and more complex models. Startups like Modulus Labs and tools like the ezkl library enable the production of ZK proofs on models in ONNX format, offering practical solutions to developers. Systems like Plonky2 have reduced proof production for models with millions of parameters to just minutes. ZKML has a bunch of use cases, including on-chain ML verification (e.g., in DeFi protocols), transparency of ML services (MLaaS), fraud detection, and private inference. For example, in decentralized Kaggle-like systems, the accuracy of a model can be proven without revealing its details. In healthcare, patients can access diagnostic results without disclosing their data. In conclusion, ZKML combines privacy protection with the security of verification processes, enabling the development of more ethical and reliable artificial intelligence systems. This approach, which lies at the intersection of cryptography and machine learning disciplines, has the potential to increase the transparency and security of AI systems at both technical and societal levels.

Kharzan P. Matasheva, Yunus M.-G. Gadamurov

The article examines cryptographic methods of zero-knowledge proof (ZKP) as a tool for overcoming information asymmetry in data markets. The theoretical foundations of ZKP are considered, including their formal properties and classification, as well as practical application scenarios in digital identification systems, confidential auctions, machine learning and data management. The impact of ZKP on the behavior of economic agents, transaction costs and market structure is analyzed. Both potential benefits of the technology — increased trust and automated verification — and risks associated with computational complexity and possible participant segmentation are identified. The study concludes that further development of hybrid privacy architectures and regulatory mechanisms is necessary to support open data markets.

Qi Feng, Kang Yang, Kaiyi Zhang, Xiao Wang · 6 authors

No abstract is available for this record.

Fady R. Alkhateeb, Adib Habbal

The rapid growth of the metaverse has led to a scattered ecosystem in which digital assets are deployed on different blockchain platforms. This disintegration creates significant challenges for interoperability, as users need secure, decentralized, and privacy-preserving protocols to enable interoperability between chains. Existing solutions typically depend on centralized exchanges or third-party relays, introducing a single point of failure and potential privacy risks. We propose MAM (Metaverse Asset Management), a novel user-centric protocol utilizing zkSNARK technology (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) that enables seamless movement of metaverse assets across various blockchain platforms. MAM’s architecture ensures privacy by generating all zkSNARK proofs locally on the user’s machine, ensuring that sensitive data, including private keys and asset metadata, never leave the device. The protocol employs a secure one-time setup to distribute the global circuit-specific proving key, ensuring the permanent destruction of toxic-waste data. Experimental evaluation demonstrates that MAM achieves a constant and minimal proof size (192 bytes), low gas cost (281,107 Gas per verification), and an end-to-end asset transfer latency under 15 seconds, outperforming recent alternatives such as MetaOpera and MAP. Static security analysis confirms the robustness of MAM’s smart contracts against the most significant vulnerability types. This research enhances the state-of-the-art of privacy-preserving and scalable cross-metaverse interoperability, providing a practical approach for fully decentralized digital asset management and transfer across the Metaverse.

R. N. Karthika, C. Valliyammai, P. Sundaravadivel, Augustian Isaac R

No abstract is available for this record.

Karel Velička

This thesis explores the concept of zero-knowledge proofs and their application in zkVMs - virtual machines capable of generating proofs of correct computation without revealing private input. We begin by introducing fundamental cryptographic tools such as zk-SNARKs and zk-STARKs, along with supporting techniques such as lookup tables. We then analyze the architecture of two concrete zkVM implementations: RISC Zero and SP1. We describe their different approaches to proof systems, recursion mechanisms, and optimization strategies. In the final part, we present comparative benchmarks on various examples, highlighting their performance differences. The results show that RISC Zero produces smaller proofs and has faster verification, while SP1 is faster in proof generation and requires fewer computation cycles.

Sorger, Tom

The growing importance of software supply chain security has revealed the critical need for securely sharing Software Bills of Materials (SBOMs). SBOMs enhance transparency by providing a detailed inventory of software components, but can inadvertently expose sensitive proprietary information and vulnerabilities when shared publicly. Addressing this challenge, this thesis explores the use of cryptographic techniques to achieve privacy-preserving and verifiable SBOM sharing. This thesis addresses this challenge by proposing zkSBOM (Zero-Knowledge Software Bill of Materials), a proof-of-concept system for privacy-preserving and verifiable SBOM sharing. It explores the system requirements and design for achieving secure yet transparent SBOM sharing, the effectiveness of various cryptographic techniques in safeguarding sensitive SBOM information, and the integration with real-world SBOMs. Through system design analysis and an experimental approach, this work provides solid insights into privacy-enhanced SBOM-sharing. The results demonstrate that the use of established cryptographic techniques is suitable to securely share SBOMs in real-world scenarios. We propose a centralised system enabling software vendors to upload their SBOMs and allowing verifiers to query for vulnerabilities. Additionally, we offer a local verifier system that allows verifiers to independently validate the proofs generated by the centralised system. The system leverages cryptographic techniques such as Merkle Trees, Sparse Merkle Trees, Merkle Patricia Tries, and Zero-Knowledge Sets. Using them, zkSBOM enables selective disclosure of SBOM information efficiently. The system ensures transparency through verifiable inclusion and non-inclusion proofs while safeguarding critical information. In a case study, we successfully ingest 16 out of 18 SBOMs and generate inclusion proofs for dependencies affected by a given vulnerability. This research contributes to advancing privacy-preserving SBOM sharing, paving the way for broader adoption in the software industry while strengthening the security of software supply chains.

Junkai Yi, Xuefeng Gao, Xin Wang, Lingling Tan

No abstract is available for this record.

Yurii Paslavskyi, Ihor Kroshnyi

An important cryptographic mechanism that guarantees confidentiality (the zero-disclosure property) and ensures that it is impossible to prove a false statement to the verifier is zero-disclosure proofs. A popular implementation of zero-disclosure proofs is short, noninteractive proofs that can be quickly verified and that do not require interaction between the parties after the initial setup. The main direction in the development of modern proof systems is interactive proof, which is built in two steps. The first is sending a confirmation of the polynomial of an interactive oracle proof and the second is creating correct oracles of the polynomial commitment scheme using well-defined cryptographic methods for evaluating polynomials. Verifying the use of the same coefficients in each linear combination requires checking both polynomial consistency and variable consistency. To construct general schemes of concise non-interactive zerodisclosure knowledge argument, an interactive oracle proof polynomial was proposed that models messages as polynomial oracles. All tests are proved using polynomial commitment schemes and then evaluated with zero knowledge at a point specified by the person verifying the information. The reliability and confidentiality of all tests are based on three main categories of interactive oracle proof polynomials, namely polynomial commitment schemes with conjunction, with inner product argument and with code theory. The protocols of concise noninteractive zero-disclosure knowledge arguments are implemented through high-level programs (compilers), which are converted into an intermediate representation, i.e. a scheme defined by a system of constraints. The compilers used are divided into domain-oriented languages, embedded domain-oriented languages, and zero-knowledge virtual machines. Specialized domain-oriented hardware description languages or programming languages offer an adapted syntax for efficiently expressing constraints in arithmetic schemes. Embedded domain-oriented languages are implemented as functions in general-purpose programming languages and are oriented to the overhead schemes inherited from the embedded language. Zero-knowledge virtual machines process the opcode of the fetch-decodeexecute cycle, replicating the computation trace for general programs and generating corresponding zeroknowledge proofs. They are compatible with existing high-level programming languages and can use the features of existing compilers. Compilers are evaluated for cross- or syntactic compatibility. In general, the biggest obstacle to using non-interactive proof libraries is the lack of documentation. Standardization can help developers compare important features across libraries and establish a more consistent performance baseline. Library documentation for these core features is implicit, and developers need to understand the underlying cryptographic techniques to choose an appropriate scheme. Standardization of compiler options is important, making it difficult to reuse existing tools.

Carsten Baum, Ward Beullens, Lennart Braun, Cyprien Delpech de Saint Guilhem · 12 authors

No abstract is available for this record.