Journal of Theoretical and Applied Information Technology
The insurance sector is being transformed through the combination of artificial intelligence (AI) and blockchain technologies. This study proposes the AI-Blockchain Hybrid Smart Contract Model (AIBSCM), which combines AI-based fraud detection with blockchain-based smart contracts to allow for automated insurance claim processing. A synthetic dataset of 1,000 insurance claims was used to train a random forest model, which achieved 92% accuracy on training data; however, real-world testing revealed difficulty in detecting fraudulent claims from under-represented categories. A blockchain simulation was conducted to demonstrate the secure storage and automated execution of claims, with smart contracts giving transparency and immutability. The architecture integrates decentralised oracles, zero-knowledge proofs (ZKPs), federated learning, and a DAO governance mechanism to provide a privacy-conscious, decentralised, and robust solution for the insurance business. Subsequent study will look at real-world deployment and integration with regulations. The integration of these technologies seeks to address traditional insurance systems' issues, such as data privacy concerns and a lack of transparency. By investigating real-world deployment and regulatory compliance, this model has the potential to transform the insurance business by delivering a safe and efficient method for dealing with false claims. This innovative method has the potential to boost client trust while also streamlining insurance company operations. Overall, the combination of blockchain and privacy-conscious technology might result in increased reliability and a transparent insurance sector.
Zero-knowledge proofs (ZKPs) are increasingly deployed in domains such as privacy-preserving authentication, verifiable computation, and secure finance. However, authoring ZK programs remains challenging: unlike conventional software development, ZK programming manifests a fundamental paradigm shift from \textit{imperative computation} to \textit{declarative verification}. This process requires rigorous reasoning about finite field arithmetic and complex constraint systems (which is rare in common imperative languages), making it knowledge-intensive and error-prone. While large language models (LLMs) have demonstrated strong code generation capabilities in general-purpose languages, their effectiveness for ZK programming, where correctness hinges on both language mastery and constraint-level reasoning, remains unexplored. To address this gap, we propose \textsc{ZK-Eval}, a domain-specific evaluation pipeline that probes LLM capabilities on ZK programming at three levels: language knowledge, algebraic primitive competence, and end-to-end program generation. Our evaluation of four state-of-the-art LLMs reveals that while models demonstrate strong proficiency in language syntax, they struggle when implementing and composing algebraic primitives to specify correct constraint systems, frequently producing incorrect programs. Based on these insights, we introduce \textsc{ZK-Coder}, an agentic framework that augments LLMs with constraint sketching, guided retrieval, and interactive repair. Experiments with GPT-o3 on Circom and Noir show substantial gains, with success rates improving from 20.29\% to 87.85\% and from 28.38\% to 97.79\%, respectively. With \textsc{ZK-Eval} and \textsc{ZK-Coder}, we establish a new basis for systematically measuring and augmenting LLMs in ZK code generation to lower barriers for practitioners and advance privacy computing.
Open access
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Mathematics, Computing, and Information Processing
Globalized supply chains are strained by fragmented data, multi-tier opacity, counterfeit risks, and costly disputes. Blockchain—a shared, append-only ledger—has been proposed to enhance transparency, traceability, and operational efficiency, yet real-world adoption reveals both breakthroughs and bottlenecks. This paper develops a deploymentminded view that integrates GS1 EPCIS/CBV standards for interoperable event data, permissioned ledgers for governance, and privacy-preserving proofs (zero-knowledge) to reconcile transparency with business confidentiality. We synthesize evidence from systematic reviews and flagship pilots (e.g., Walmart–IBM Food Trust) and contrast them with lessons from initiatives that wound down (e.g., TradeLens), extracting adoption patterns, KPI impacts, and failure modes. We then describe a reference methodology—data acquisition via EPCIS events, Fabric-based channels, and role-based access—plus an evaluation rubric for trace time, recall precision, dispute cycle time, and data-reconciliation costs. Results from literature-anchored benchmarks indicate orders-of-magnitude traceability lead-time (TLT) reductions (days → seconds) and measurable reductions in manual reconciliation, with gains contingent on standards compliance and high-quality “oracle” data. Finally, we map future directions—zk-proof rollups, interoperable digital product passports, and policy-aligned sustainability metrics—alongside candid limitations around ecosystem incentives, privacy, scalability, and data veracity. We conclude that blockchain can shift chains from reactive to verifiable and auditable networks when combined with data standards, sound governance, and selective privacy technologies rather than “full transparency” alone.
The rapid advancement of artificial intelligence (AI) and large language models (LLMs) is profoundly reshaping higher education, shifting from institution-centered paradigms to learner-centric personalized learning environments (PLEs). However, PLEs face critical challenges in identity management, including data breaches, unauthorized access, and interoperability barriers, which undermine security and trust. This study proposes the Blockchain-based Student Identity Management System (BSIMS), a conceptual model integrating blockchain technology, xAPI standards, and OAuth2 protocols to uphold confidentiality, integrity, availability, authenticity, and non-repudiation (CIAAN) principles. Grounded in the Technology Acceptance Model (TAM) and Information Systems Success Model (ISSM), BSIMS was validated through mixed-methods research involving 90 students and 16 experts from five Yunnan Province universities. Results demonstrate BSIMS' superiority in user satisfaction (explaining 81.7% variance), CIAAN performance (M=4.58 vs. 3.18 for traditional systems, p<0.001), and reliability (zero downtime, 0.3 ms response time). Ethical and legal implications, such as immutability conflicts with privacy rights, are addressed via zero-knowledge proofs and off-chain storage. BSIMS offers a scalable framework for secure PLEs, advancing educational informatization in Yunnan and beyond.
Christoph Hochrainer, Valentin Wüstholz, Maria Christakis
Zero-knowledge virtual machines (zkVMs) are increasingly deployed in decentralized applications and blockchain rollups since they enable verifiable off-chain computation. These VMs execute general-purpose programs, frequently written in Rust, and produce succinct cryptographic proofs. However, zkVMs are complex, and bugs in their constraint systems or execution logic can cause critical soundness (accepting invalid executions) or completeness (rejecting valid ones) issues. We present Arguzz, the first automated tool for testing zkVMs for soundness and completeness bugs. To detect such bugs, Arguzz combines a novel variant of metamorphic testing with fault injection. In particular, it generates semantically equivalent program pairs, merges them into a single Rust program with a known output, and runs it inside a zkVM. By injecting faults into the VM, Arguzz mimics malicious or buggy provers to uncover overly weak constraints. We used Arguzz to test six real-world zkVMs (RISC Zero, Nexus, Jolt, SP1, OpenVM, and Pico) and found eleven bugs in three of them. One RISC Zero bug resulted in a $50,000 bounty, despite prior audits, demonstrating the critical need for systematic testing of zkVMs.
Mohammed Ziaul Haider, Tayyaba Noreen, Mishah Uzziél Salman, Marcos Dias de Assunção · 5 authors
Cross-chain bridges and oracle DAOs represent some of the most vulnerable components of decentralized systems, with more than 2.8 billion lost due to trust failures, opaque validation behavior, and weak incentives. Current oracle designs are based on multisigs, optimistic assumptions, or centralized aggregation, exposing them to attacks and delays. Moreover, predictable committee selection enables manipulation, which threatens data integrity across chains. We propose V-ZOR, a verifiable oracle relay that integrates zero-knowledge proofs, quantum-grade randomness, and cross-chain restaking to mitigate these risks. Each oracle packet includes a Halo 2 proof verifying that the reported data was correctly aggregated using a deterministic median. To prevent committee manipulation, VZOR reseeds its VRF using auditable quantum entropy, ensuring unpredictable and secure selection of reporters. Reporters stake once on a shared restaking hub; any connected chain can submit a fraud proof to trigger slashing, removing the need for multisigs or optimistic assumptions. A prototype in Sepolia and Scroll achieves sub-300k gas verification, one-block latency, and a $\mathbf{1 0} \times$ increase in collusion cost. V-ZOR demonstrates that combining ZK attestation with quantum-randomized restaking enables a trust-minimized, high-performance oracle layer for cross-chain DeFi.
Abstract Background Children with subtotally resected pediatric low-grade glioma (pLGG) often face multiple lines of treatment, which are seldom capable of eliminating the entire tumor. Genomics-based biomarkers are often used to select targeted therapies, but this paradigm only yields overall response rates of ∼50% optimally. Functional precision medicine (FPM), where patient-specific therapeutic efficacy is evaluated by directly treating individuals’ tumor outside their body, can predict individualized drug responses for some cancers, but pLGG is notoriously difficult to maintain outside the body, limiting development of FPM for pLGG. Methods We describe what is, to our knowledge, the first platform that can maintain, treat, and analyze zero-passage pLGG tumor tissue ex vivo , facilitating FPM testing. We engraft pLGG tumors onto a previously validated organotypic brain slice culture (OBSC) platform. After ensuring reproducible engraftment and maintenance of living pLGG tumor tissue on OBSCs, we measured MAPK pathway response to targeted therapies via immunoblotting. We then measured tumor ex vivo response to targeted therapies. Results Each zero-passage pLGG tumor tissue specimen exhibited reproducible growth on the OBSC platform. Western blot demonstrated each BRAF KIAA1549 fusion+ tumor exhibited expected paradoxical MAPK upregulation to dabrafenib treatment. Two of three tumors demonstrated cytotoxicity from trametinib as predicted, whereas one tumor did not. No clinical correlates were measured in this proof-of-concept study, though this mixed response to MEK inhibition may be in line with real-world clinical responses. Conclusion The OBSC platform supports ex vivo maintenance of passage-zero pLGG tumor tissue and enables personalized drug screening to yield a new functional biomarker of pLGG drug response.
Traceable ring signatures (TRSs) allow a signer to create a signature that maintains anonymity while enabling traceability if needed. It merges the characteristics of traditional ring signatures with the ability to trace signers, making it ideal for applications that demand both confidentiality and accountability. In a TRS scheme, a ring of potential signers generates a signature on a message without disclosing the actual signer’s identity. However, the identity can be traced if the signer uses the same tag for multiple signatures. This paper introduces a novel formal construction of TRS under universally composable (UC) security. We integrate verifiable random functions (VRFs) and zero-knowledge proofs for membership, employing Pedersen commitments. Our signature schemes maintain a logarithmic size while preserving the UC security guarantees. Additionally, we explore the potential to extend the property of one-time anonymity in TRS to K-time anonymity.
Jamil Abedalrahim Jamil Alsayaydeh, Mohd Faizal Yusof, Nor Adnan Yahaya, Viacheslav Kovtun · 5 authors
In today's digital world, cryptocurrencies like Bitcoin can secure transactions without banks. However, the rise of quantum computing poses significant threats to their security, as traditional cryptographic methods may be easily compromised. In addition, the existing algorithms face difficulties like slow transaction speeds, interoperability issues between different cryptocurrencies, and privacy concerns. Hence, Quantum Crypto Guard for Secure Transactions (QCG-ST), a novel blockchain framework, is introduced, offering enhanced security and efficiency for cryptocurrency transactions. The QCG-ST employs lattice-based cryptography to provide robust protection against quantum threats and incorporates a new consensus mechanism to increase the transaction speed and reduce energy consumption. The QCG-ST system uses lattice-based encryption that is based on the Ring Learning With Errors (Ring-LWE) issue to protect itself from quantum assaults. It uses sharding, a Proof-of-Stake (PoS) consensus method, and a threshold signature scheme (TSS) to make the system more scalable and use less energy. Zero-knowledge proofs (ZKPs) are used to check transactions without giving out private information. We offer a cross-chain atomic swap protocol that uses hashed time-lock contracts to make sure that it works on all platforms. Blockchain transaction data utilized in testing originated from the Bitcoin Historical Dataset available on Kaggle, and quantum resistance has been assessed using the Qiskit Aer simulator. It evaluated the framework's performance to that of traditional methods like Payment Channel-Lightning Network (PC-LN), Variational Quantum Eigensolver (VQE), and Cross-Chain Transaction with Hyperledger (CCT-H). Results show that QCG-ST does far better than traditional systems in terms of transaction success rate (up to 98.5%), speed, energy efficiency, latency, and throughput, especially when tested in a quantum-simulated environment. This study completes in an essential vacuum in blockchain technology by suggesting a strong, quantum-resistant, privacy-protecting architecture that can handle the problems that could arise up in decentralized digital banking in the future.
Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a central server while keeping in- and output layers on the client-side. This setup enables SL to leverage server computation capacities without sharing data, making it highly effective in resource-constrained environments dealing with sensitive data. However, the distributed nature enables malicious clients to manipulate the training process. By sending poisoned intermediate gradients, they can inject backdoors into the shared DNN. Existing defenses are limited by often focusing on server-side protection and introducing additional overhead for the server. A significant challenge for client-side defenses is enforcing malicious clients to correctly execute the defense algorithm. We present ZORRO, a private, verifiable, and robust SL defense scheme. Through our novel design and application of interactive zero-knowledge proofs (ZKPs), clients prove their correct execution of a client-located defense algorithm, resulting in proofs of computational integrity attesting to the benign nature of locally trained DNN portions. Leveraging the frequency representation of model partitions enables ZORRO to conduct an in-depth inspection of the locally trained models in an untrusted environment, ensuring that each client forwards a benign checkpoint to its succeeding client. In our extensive evaluation, covering different model architectures as well as various attack strategies and data scenarios, we show ZORRO's effectiveness, as it reduces the attack success rate to less than 6\% while causing even for models storing \numprint{1000000} parameters on the client-side an overhead of less than 10 seconds.
David Chaum, Richard Carback, Jeremy Clark, Liu Chao · 11 authors
Abstract We revisit “silent coercion” where an adversary gains access to a voter’s credential without the voter’s knowledge in an E2E verifiable, coercion-resistant Internet voting system. We argue that in this setting, casting an intended vote is impossible since the cryptographic backend can no longer distinguish the voter and adversary. However, we affirm that the voter can still act to nullify adversarial ballots, which is preferable to inaction. We provide a new instantiation of nullification using zero-knowledge proofs and multiparty computation, which improves on the efficiency of the current state-of-the-art. We also demonstrate an example voting system—VoteXX—that uses nullification. Our nullification protocol can complement new and existing techniques for coercion resistance (which all require voters to hide cryptographic keys from the coercer), providing a failsafe option for voters whose keys leak.
A machine-checked, sorry-free development in Lean 4 over Mathlib (about 1500 lines) that fills a gap in Mathlib — the dilogarithm Li2, which the library cites but does not define — and follows it into a quantum-mechanics problem. To the best of the author's knowledge, the first formalization in any proof assistant of: the real dilogarithm with Euler's reflection identity, Landen's transformation and the duplication formula; the golden-ratio ladder Li2(1/φ2) = π2/15 − ln2φ (derived from a 3×3 linear system, no five-term relation) and the Lee–Yang effective central charge c_eff = 2/5 (the simplest thermodynamic-Bethe-ansatz dilogarithm identity); the Clausen function Cl2 and Catalan's constant G = Cl2(π/2); the Fejér–Jackson inequality; the bound Cl2(θ) ≥ sin(θ)/2 by an asymptotics-free Abel summation; and the Margolus–Levitin and (an L1 form of the) Mandelstam–Tamm quantum speed limits. These assemble into the title theorem: the weight-2 zeta state (populations proportional to 1/n2 on equally spaced energy levels) has infinite mean energy and infinite energy variance — so both textbook speed limits say nothing — yet never reaches a state orthogonal to itself, because its autocorrelation is (6/π2)·Li2(e−iθ) and the dilogarithm has no zero on the unit circle. A clock with an unbounded energy budget that never ticks. Every identity is classical (Euler, Landen, Clausen, Fejér, Jackson, Mandelstam–Tamm, Margolus–Levitin); the contribution is the machine-checked development and its assembly. Every named theorem depends only on the three standard axioms (propext, Classical.choice, Quot.sound). Formalized with AI assistance (Claude, Anthropic); the mathematics and all claims are the author's responsibility.
John C. Moore, Marc Macias‐Fauria, Michael Wolovick
Key points • Academics, activists, and Arctic inhabitants are deeply concerned about cryosphere systems at imminent risk of collapse, and yet decades of "consequences-based" lobbying have failed to produce sufficient political will for deep decarbonization. • There are moral imperatives to search for tools that may help stabilise Arctic earth systems and to explore knowledge co-production and co-design with Arctic peoples to ensure both local and global benefits. • We propose a "compassionate harm reduction" paradigm, whereby climate scientists prioritise the well-being of humanity, and take responsibility to thoroughly understand any potential interventions that might minimise the harm from the consequences of climate change. A New Paradigm The prevailing "consequences-based paradigm" defines the role of climate scientists as informing the public about the negative effects of climate change, assuming this will mobilize political action to reduce emissions. Under this paradigm, research into strategies other than decarbonization is often seen as counterproductive, an argument advanced by Siegert et al. (2025) in their lead article, "Safeguarding the polar regions from dangerous geoengineering", in Frontiers in Science. Yet after half a century of alarm-raising, this paradigm has failed to generate the political will needed for deep decarbonization. This article presents insights from 27 academics, activists and Arctic inhabitants who propose an alternative: a "harm-reduction paradigm." We maintain that climate interventions research and decarbonization are not mutually exclusive. Instead of focusing solely on the problems, climate scientists should also explore all potential solutions to reduce harm to humanity. The effectiveness and risks of interventions remain uncertain, and only further research can address these questions – research that some, including Siegert et al. (2025) seek to halt. This perspective also carries implications for governance. Stewards of the Arctic Unlike Antarctica, the Arctic is more accessible, making it a more likely starting point for intervention field trials. It is not a Global Commons, and Arctic Peoples must be central to any decision-making. Preference from those in the mid-latitudes are secondary. The key question is how should decisions be made – through evidence or guesswork? Evidence includes both traditional knowledge and the scientific method, each of which has long produced valuable insights. Whether climate interventions make sense, pose risks, or are preferable to inaction remains unknown and cannot be determined without comprehensive research across legal, scientific, and technical domains. Pirita Näkkäläjärvi, President of the Sámi Parliament in Finland, states: "It is my personal opinion that we need to keep all options open and research climate interventions because of the risk of exceeding the goal of limiting global temperature rise to 1.5 degrees and the risk of crossing multiple climate tipping points" (Operaatio Arktis, 2023). Motivated by these concerns, the University of the Arctic has set up a review process for high latitude interventions (https://climateinterventions.org/), led by the Saami Council and incorporating both academic and traditional knowledge perspectives (Fig. 1). Gunn-Britt Retter, Head of the Arctic and Environmental Unit of the Saami Council, explains "The Saami Council acknowledges the need to face suggested intervention ideas and initiatives that are developed or are being developed. (...) It is our position that a rights-based perspective must be the foundation in any evaluation of intervention suggestions in relation to strengths and weaknesses and benefits and co-benefits.(...) Saami Council's participation in this project should not be interpreted as Saami Council's endorsement or support of the intervention suggestions assessed". Another core group interested in the future Arctic are the youth. Anni Pokela, strategic planner with Operaatio Arktis and a Gender Studies student at Helsinki University, asks: "Are we truly ready to accept the damage, the suffering that's unavoidable without climate intervention? Or are we going to give climate repair a chance?" In their lead article, Siegert et al. (2025) ask "why would a nation such as Greenland embrace a geoengineering solution to sea level rise" —since sea levels around Greenland's coasts are falling as the ice disappears. A good way to find out is to ask Greenlanders. Those of us that have readily identified several reasons for pursuing interventions research. For example, many Greenlanders have empathy and feel a sense of solidarity with low-income communities around the world who are already feeling the impacts of sea level rise. Furthermore, the ice itself is a global good which, if valued appropriately (Brown et al., 2021), would be highly rewarding for Greenland. Finally, declining sea levels impact Greenlanders, for instance through boat collisions with unmapped islets previously submerged. Challenges of Decarbonization There is near-universal agreement amongst scientists and policymakers that decarbonization is essential. The Earth is already at 1.5ºC above pre-industrial levels, and no climate intervention can work sustainably without rapid emissions cuts. Yet, glossing over the serious challenges involved is, at best, naive. Siegert et al. (2025) describe a scenario in which global temperature is stabilized at 0.9°C above preindustrial levels - a target long since past. While we agree that this "simple vision for Antarctica is appealing", it is also nearly impossible. Even in 2020, achieving net-zero required global investments in clean-energy and carbon-removal infrastructure exceeding $4 trillion annually by 2030 (IEA, 2021). With President Trump now actively reversing U.S. commitments to renewable energy, despite the U.S. being the world's largest historical emitter of greenhouse gasses, the likelihood of achieving rapid global decarbonization in time to meet climate goals is increasingly remote. Meanwhile, human activities continue to drive planetary warming – the most effective large-scale geoengineering experiment to date. Governance Moral hazard A widely touted argument against research into climate interventions, and used by Siegert et al. (2025), is that such research may reduce the likelihood of decarbonization. This is known as the "Mitigation Deterrence" or "Moral Hazard" argument. However, evidence is mixed: public attitudes show weak and variable support for this hazard, and there is equally strong evidence of the opposite effect (Reynolds 2014): that is, the idea of interventions can motivate people to take decarbonization more seriously. Furthermore, anticipating moral hazard may limit policymakers' options (Andrews et al., 2022). Support for intervention research is strongest among those suffering the worst climate impacts—especially in the Global South and among Indigenous Peoples (Sovacool et al., 2024). Motives and vested interests Opinion on geoengineering are often influenced by who funds the research and their motives. Who gains from Arctic intervention research? The fossil fuel and mineral extraction industries have clear interests in the Arctic, which holds an estimated 25% of global untapped gas reserves and 13% of oil, and large amounts of rare earth elements, such as 40% of global palladium (Borgerson, 2013). These resources become more accessible with reduced snow, ice cover or minimal sea ice, which also facilitates safer transport. Thus, resource extraction industries are unlikely to fund efforts to preserve the Arctic cryosphere intact, unless they are cynically assuming interventions will fail. Many institutions active in Arctic research, including the University of the Arctic and the University of Cambridge have published strong ethical statements rejecting support from such sources (University of Cambridge, 2023). The Precautionary Principle Environmental risks are often cited to oppose geoengineering research (e.g., Siegert et al., 2025). The Precautionary Approach has framed most environmental legislation over the last 30 years. Davis and Vinders (2025) examine how it might apply to geoengineering. In the case of intervention field trials, environmental risks are generally very small. However, perceived risk often includes concern over a "slippery slope" toward broader deployment. Davis and Vinders (2025) argue that political risk should be included when evaluating harms, but this assessment must consider both the risk of using an intervention and the risk of not using one – the "Moral Hazard of non-research." This latter risk is missing in both Siegert et al.'s review, and more broadly, such as in the EU advisory report (SAPEA, 2024). Risks of inaction include the socio-economic damages from crossing climate tipping points which are concentrated in the polar regions (Armstrong-McKay et al., 2022). Sticky slopes not slippery ones Does research inevitably lead to deployment – the so-called "slippery slope"? Not if research is ethically guided (e.g., AGU, 2024). There is a duty to report all findings, positive and negative, often required by funders. A roadmap to potential deployment involves many checkpoints along the way (e.g., Diamond et al., 2022). So far, the slope has proven "sticky," not slippery. For example, the Arctic Ice Project ceased sea ice albedo modification research due to toxicity concerns about hollow glass microspheres that they proposed using (https://srm360.org/news-reaction/arctic-ice-project-shuts-down/). Similarly, simulations suggest the retreat of the Sermeq Kujalleq (Jakobshavn Isbrae) glacier is unstoppable (Zhao et al., 2025). The Induction Fallacy Both decarbonization and climate interventions are extraordinarily challenging. Many intervention proposals will prove unworkable – due to feasibility, cost, timing, or being too risky. Whether this has already been demonstrated, as Siegert et al. (2025) claim, is debatable given how little research exists on most options (Fig. 1). Dismissing all climate interventions because some face serious challenges is a logical error, known as the Induction Fallacy. Siegert et al. examine only 5 of the 61 intervention ideas identified so far for the Arctic (https://climateinterventions.org), with more likely to emerge in future. Conversely, advocating a sole focus on carbon emissions reduction risks falling into "single action bias"; the tendency to favour one familiar solution while neglecting others that may also be necessary. Risk-risk assessment Any analysis of climate interventions must be framed as a risk-risk assessment, that is, comparing the risks and benefits of doing something versus doing nothing. Neither the present nor a past climate state can serve as a viable baseline; we must compare against plausible future scenarios. While research is still at an early stage for many intervention ideas, the literature on Stratospheric Aerosol Injection (SAI) is relatively mature. It generally finds that projected impacts under SAI are less severe, and crucially more equitable, than those under future greenhouse gas climate scenarios. Supporting references (see supplementary material) point to economic benefits, an overwhelmingly positive cryospheric response, and net human health gains from reduced temperatures that outweigh risks from air pollution and from ozone depletion by a factor of 13 (Harding et al). Conclusions While scientific and public support for climate action is strong (Leiserowitz et al., 2023), the political will for large-scale emissions reductions remains insufficient. Siegert et al. (2025) frequently cite fossil fuel-funded opposition as a key barrier. While such interests have obstructed other major societal and economic changes in the past (e.g., workers' rights, environmental regulations), those changes still occurred. Hence, lack of climate action is likely not solely due to fossil fuel-funded opposition but to fossil fuels being integral to modern lifestyles (e.g., Lemaire, 2025). We argue that the "consequences-based paradigm" – the belief that warning the public will generate political action – has failed. After decades of warnings, emissions remain high. Worse, fear-based messaging may even boost support for right-wing parties (e.g., Nguyen et al., 2022), which typically oppose climate action. By contrast, research into climate interventions could offer much-needed optimism and agency. Our "harm-reduction paradigm" suggests that such research may strengthen public confidence in our ability to meet climate challenges, thereby fostering solidarity, expanding empathy, and ultimately increasing political support for decarbonization. Of course, we could be wrong. And even if we are right that climate interventions research boosts support for decarbonization, that does not necessarily mean interventions themselves are good ideas. Only more research can answer that. Yet, Siegert et al. (2025) claim that "further research into these techniques would not be an effective use of limited time and resources". Arguing to shut down an entire field of scientific research is an extraordinary claim, and requires extraordinary proof. Pointing to specific problems in individual techniques, as Siegert et al. do, is not sufficient. Further research may resolve those problems. The only potentially valid argument against all interventions research is political: that it might reduce motivation to decarbonize. But even setting aside the ethical concern of suppressing science for political reasons, this argument is unproven. It rests entirely on the same "consequences-based paradigm" of climate outreach. It is fair to say that the "consequences-based paradigm" has, after half a century, failed to deliver. Perhaps, it is time that we try compassionate harm reduction instead. Formal Analysis Yes Yes Yes Funding acquisition N/A N/A N/A Investigation Yes Yes Yes Methodology Yes Yes Yes Project administration Yes No No Resources N/A N/A N/A Software N/A N/A N/A Supervision N/A N/A N/A Validation N/A N/A N/A Visualization Yes No No Figure legend Figure 1. The methodology adopted by University of the Arctic for an evaluation of intervention ideas. The Saami Council are leading the indigenous knowledge stream but are engaging with other indigenous groups in the Arctic Council. Meetings include three distinct groups: traditional knowledge holders, indigenous politicians, and indigenous experts. The meetings are in a variety of formats, including formalized workshops, town hall and council meetings in addition to awareness raising and general capacity building.
Abstract Verifiable encryption enables the decryption to be taken on properly generated ciphertexts, by making the encryptor provide a zero-knowledge proof. To meet the quantum-safe application requirements, such as key escrow, Lyubashevsky et al. proposed a one-shot verifiable encryption (LN17 scheme) based on the hardness of lattice problems. In their scheme, the FSwA-type zero-knowledge proof was obtained using rejection sampling on a discrete Gaussian distribution. In this paper, we present a construction of verifiable encryption that utilizes rejection sampling on bimodal Gaussian to get the associated zero-knowledge proof. Our new construction, while exhibiting a weaker soundness property than LN17 scheme, benefits from a smaller proof size, leading to a reduced size of the verifiable ciphertext. As for the weaker soundness property, it supports some applications such as key escrow where honestly generated verifiable ciphertexts are more useful to be decrypted out in the hope of doing some further computation tasks. We provide the efficiency comparison of the new construction by instantiating it with several sets of concrete parameters.
Nan Wang, Nan Wu, Xiangyu Hui, Jiafan Wang · 5 authors
As the demand for exercising the "right to be forgotten" grows, the need for verifiable machine unlearning has become increasingly evident to ensure both transparency and accountability. We present {\em zkUnlearner}, the first zero-knowledge framework for verifiable machine unlearning, specifically designed to support {\em multi-granularity} and {\em forgery-resistance}. First, we propose a general computational model that employs a {\em bit-masking} technique to enable the {\em selectivity} of existing zero-knowledge proofs of training for gradient descent algorithms. This innovation enables not only traditional {\em sample-level} unlearning but also more advanced {\em feature-level} and {\em class-level} unlearning. Our model can be translated to arithmetic circuits, ensuring compatibility with a broad range of zero-knowledge proof systems. Furthermore, our approach overcomes key limitations of existing methods in both efficiency and privacy. Second, forging attacks present a serious threat to the reliability of unlearning. Specifically, in Stochastic Gradient Descent optimization, gradients from unlearned data, or from minibatches containing it, can be forged using alternative data samples or minibatches that exclude it. We propose the first effective strategies to resist state-of-the-art forging attacks. Finally, we benchmark a zkSNARK-based instantiation of our framework and perform comprehensive performance evaluations to validate its practicality.
Modern vehicles accumulate fragmented lifecycle records across OEMs, owners, and service centers that are difficult to verify and prone to fraud. We propose VehiclePassport, a GAIA-X-aligned digital passport anchored on blockchain with zero-knowledge proofs (ZKPs) for privacy-preserving verification. VehiclePassport immutably commits to manufacturing, telemetry, and service events while enabling selective disclosure via short-lived JWTs and Groth16 proofs. Our open-source reference stack anchors hashes on Polygon zkEVM at <$0.02 per event, validates proofs in <10 ms, and scales to millions of vehicles. This architecture eliminates paper-based KYC, ensures GDPR-compliant traceability, and establishes a trustless foundation for insurance, resale, and regulatory applications in global mobility data markets.
The convergence of artificial intelligence (AI), multi-access edge computing (MEC), and cloud environments has transformed identity governance by enabling real-time decision-making and seamless access control across decentralized infrastructures. However, this evolution has also introduced complex challenges concerning data privacy, identity trust, and security. This review explores privacy-first security models that integrate AI for identity governance in hybrid cloud-edge architectures. It evaluates privacy-preserving techniques such as homomorphic encryption, federated learning, and zero-knowledge proofs, emphasizing their role in ensuring secure identity authentication, authorization, and auditability. The paper critically analyzes the limitations of conventional identity and access management (IAM) frameworks in dynamic, resource-constrained edge environments and proposes adaptive models that embed privacy by design. Furthermore, the review investigates the interplay between explainable AI (XAI) and policy enforcement for transparent and compliant identity governance. By synthesizing advancements in cryptographic methods, AI reasoning engines, and decentralized identity (DID) systems, the paper outlines a roadmap for building secure, scalable, and privacy-compliant identity infrastructures in the era of pervasive computing. Keywords: Privacy-Preserving Identity Governance, AI-Driven Access Control, Multi-Access Edge Computing (MEC). Federated Identity Management, Explainable AI (XAI), Zero-Knowledge Proofs.
Zero-knowledge Proof (ZKP), is an effective cryptographic primitive that allows one party to verify the correctness of a given statement without disclosing any additional information. It plays a central role in applications such as blockchain transactions and cryptocurrencies. However, implementations of ZKP suffer from the most time-consuming task called Multi-Scalar Multiplication (MSM). Existing works and evaluation criteria primarily emphasize speed enhancement, but overlook optimizations of area overhead. In this paper, a FPGA-based accelerator FusionMSM is designed to reduce the overall latency but also improve area overhead. We attribute the bottleneck of MSM to a three-layer pyramid, including the finite field arithmetic, point operations on elliptic curves and scheduling. For modular arithmetic, we propose an efficient and non-Montgomery modular multiplier by utilizing hybrid multiplication strategy and optimizing multi-bit LUT-based modular reduction. It obtains 1.11 x less area cost and 2.00 x speed-up versus the modular multipliers used in ZKP acceleration works. For point operations, we design a unified and fully pipelined point addition unit, which can run at 500 MHz, the highest frequency in the reported works. On top of that, we present a greedy mechanism to resolve potential collisions, which can reduce the idle cycles of the point addition unit and improve its utilization. As far as we know, FusionMSM achieves the best performance compared to other FPGA-based and ASIC-based works for the input sizes from 218 to 226. For the degree of 220, FusionMSM only needs 12.4% of time in Hardcaml, 24.54% of time in PipeMSM on FPGA, and 36.41% of time in ASIC-based work PipeZK. It also utilizes less resources, resulting in a 90.93% reduction in URAMs, 35.24% reduction in FFs and 47.59% reduction in CARRY8s. Compared to GPU-based implementations, FusionMSM delivers comparable performance but with a lower power of 24.5 W.
L. Domven, Aliyu Danladi Hina, A. M. Kwami, C. M. Miri · 5 authors
This study proposes a secure mobile voting system that integrates elliptic curve cryptography (ECC) with secure multiparty computation (SMPC) to guarantee vote confidentiality, integrity, and verifiability. Designed to enable scalable, privacy-preserving elections via mobile devices, the system authenticates voters using registered numbers and records ballots as encrypted points on an elliptic curve. Encrypted votes are published on a public bulletin board alongside zero-knowledge proofs to ensure their validity. To safeguard decryption, Shamir’s secret sharing distributes keys among trusted authorities, enabling collective tallying without exposing individual votes. The system incorporates ECC-based secret sharing, homomorphic encryption, and zero-knowledge proofs, leveraging the hardness of the elliptic curve discrete logarithm problem (ECDLP) for robust security. Both experimental and theoretical evaluations demonstrate that ECC significantly improves computational efficiency and scalability, making the system well-suited for resource-constrained environments. Overall, the integration of ECC and SMPC offers a practical, efficient, and secure framework for mobile elections, effectively balancing privacy, security, and performance.
Omar Jarkas, Ryan K. L. Ko, Naipeng Dong, Redowan Mahmud
Integrity verification and attestation are critical in containerized environments, where traditional Linux Integrity Measurement Architecture (IMA) falls short due to its lack of container-specific contextualization. These gaps undermine container autonomy, escalate privacy risks, and impede granular integrity checks. Addressing these challenges, this paper introduces the Virtual IMA (VIMA), a novel framework that refines Linux IMA’s principles to support containerized settings. Using nested Merkle trees, VIMA’s Two-Tree Architecture (2TA) enables detailed integrity assessments across system-wide monolithic trees and individual container trees. Integrating Merkle and zero-knowledge (ZK) proofs establishes VIMA as a secure, privacy-preserving verification and attestation solution. Our comparative analysis and initial prototype testing reveal that VIMA significantly improves upon traditional IMA with minimal performance overhead, offering substantial scope for optimization.
Hao Cheng, Georgios Fotiadis, Johann Großschädl, Daniel Page
Non-degenerate bilinear maps on elliptic curves, commonly referred to as pairings, have many applications including short signature schemes, zero-knowledge proofs and remote attestation protocols. Computing a state-of-the-art pairing at the 128-bit security level, such as the optimal ate pairing over the curve BLS12-381, is very costly due to the high complexity of some of its sub-operations: most notable are the Miller loop and final exponentiation. In the past ten years, a few optimized pairing implementations have been introduced in the literature, but none of those took advantage of the vector (SIMD) extensions of state-of-the-art Intel and AMD CPUs, especially AVX-512; this is surprising, because doing so offers the potential to reach significant speed-ups. Consequently, the questions of 1) how computation of the optimal ate pairing can be effectively vectorized, and 2) what execution time such a vectorized implementation can achieve are still open. This paper addresses said questions by introducing a carefully-optimized AVX-512 implementation of the optimal ate pairing on BLS12-381. A central feature of the implementation is the use of 8-way Integer Fused Multiply-Add (IFMA) instructions, which are capable to execute eight 52 x 52-bit multiplications in a SIMD-parallel fashion. We introduce new vectorization strategies and describe optimizations of existing ones to speed up arithmetic operations in the extension fields Fp4 , Fp6 , and Fp12 as well as certain higher-level functions. Furthermore, we discuss some parallelization bottlenecks and how they impact execution time. We benchmarked our pairing software, which we call avxbls, on an Intel Core i3-1005G1 (“Ice Lake”) CPU and found that it needs 1, 265, 314 clock cycles (resp. 1, 195, 236 clock cycles) for the full pairing, with the Granger-Scott cyclotomic squaring (resp. compressed cyclotomic squaring) being used in the final exponentiation. For comparison, the non-vectorized (i.e., scalar) x64 assembly implementation from the widely-used blst library has an execution time of 2, 351, 615 cycles, which is 1.86 times (resp. 1.97 times) slower. avxbls also outperforms Longa’s implementation (CHES 2023) by almost the same factor. The practical importance of these results is amplified by Intel’s recent announcement to support AVX10, which includes IFMA instructions, in all future CPUs.
Thibauld Feneuil, Matthieu Rivain, Auguste Warmé-Janville
Side-channel attacks pose significant threats to cryptographic implementations, which require the inclusion of countermeasures to mitigate these attacks. In this work, we study the masking of state-of-the-art post-quantum signatures based on the MPC-in-the-head paradigm. More precisely, we focus on the recent threshold-computation-in-the-head (TCitH) framework that applies to some NIST candidates of the post-quantum standardization process. We first provide an analysis of side-channel attack paths in the signature algorithms based on the TCitH framework. We then explain how to apply standard masking to achieve a d-probing secure implementation of such schemes, with performance scaling in O(d2), for d the masking order.Our main contribution is to introduce different ways to tweak those signature schemes towards their masking friendliness. While the TCitH framework comes in two variants, the GGM variant and the Merkle tree variant, we introduce a specific tweak for each of these variants. These tweaks allow us to achieve complexities of O(d) and O(d log d) at the cost of non-constant signature size, caused by the inclusion of additional seeds in the signature. We also propose a third tweak that takes advantage of the threshold secret sharing used in TCitH. With the right choice of parameters, we show how, by design, some parts of the TCitH algorithms satisfy probing security without additional countermeasures. While this approach can substantially reduce the cost of masking in some part of the signature algorithm, it degrades the soundness of the core zero-knowledge proof, hence slightly increasing the size of the signature.We analyze the complexity of the masked implementations of our tweaked TCitH signatures and provide benchmarks on a RISC-V platform with built-in hash accelerator. We use a modular benchmarking approach, allowing to estimate the performance of diverse signature instances with different tweaks and parameters. Our results illustrate how the different variants scale for an increasing masking order. For instance, for a masking order d = 3, we obtain signatures of around 14 kB that run in 0.67 second on a the target RISC-V CPU with a 250MHz frequency. This is to be compared with the 4.7 seconds required by the original signature scheme masked at the same order on the same platform. For a masking order d = 7, we obtain a signature of 17.5 kB running in 1.75 second, to be compared with 16 seconds for the stardard masked signature.Finally, we discuss the extension of our techniques to signature schemes based on the VOLE-in-the-Head framework, which shares similarities with the GGM variant of TCitH. One key takeaway of our work is that the Merkle tree variant of TCitH is inherently more amenable to efficient masking than frameworks based on GGM trees, such as TCitH-GGM or VOLE-in-the-Head.
Florian Hirner, Florian Krieger, Constantin Piber, Sujoy Sinha Roy
Zero-knowledge proofs (ZKPs) are cryptographic protocols that enable one party to prove the validity of a statement without revealing any information beyond its truth. Central building blocks in many ZKPs are polynomial commitment schemes (PCS) where constructions with linear-time provers are especially attractive. Two such examples are Brakedown and its extension Orion, which enable linear-time and quantum-resistant proving by leveraging linear-time encodable Spielman codes. However, these PCS operate over large datasets, creating significant computational bottlenecks. For example, committing to and proving a degree 228 polynomial requires around 1.1 GB of data while taking 463 seconds on a high-end server CPU.This work addresses the performance bottleneck in Orion-like PCS by optimizing their most critical operations: Spielman encoding and Merkle commitments. These operations involve Gigabytes of data and suffer from random off-chip memory access patterns that drastically reduce off-chip bandwidth. We resolve this issue and introduce inverted expander graphs to eliminate random writes and reduce off-chip memory accesses by over 50%. Additionally, we propose an on-the-fly graph sampling method that avoids streaming large auxiliary data by generating expander graphs dynamically on-chip. We also provide a formal security proof for our proposed graph transformation. Beyond encoding, we accelerate Merkle Tree construction over large data sets through a scalable multi-pass SHA3 pipeline. Finally, we reutilize existing hardware components used in commitment to accelerate the so-called proximity and consistency checks during proof generation.Building upon these concepts, we present the first hardware architecture for PCS – with linear prover time – on an Xilinx Alveo U280 FPGA. In addition, we discuss the practical challenges of manually partitioning, placing, and routing our large-scale architecture to efficiently map it to the multi-SLR and HBM-equipped FPGA. The final implementation achieves a speedup of two orders of magnitude for full proof generation, covering commitment and proving steps. When combined with Virgo as an outer CP-SNARK protocol, our accelerator reduces end-to-end latency by up to 3.85x – close to the theoretical maximum of 3.9x.
M Savitha Devi, Ningthoujam Chidananda Singh, Thoudam Basanta Singh
Abstract - Blockchain enabled systems are more and more adopted in healthcare for secured processing of data, but current smart contract usage in healthcare leaks private patient data on execution. The contributions of this paper are two-fold: (1) it proposes a new framework that combines ZKPs with healthcare smart contracts/transactions to achieve full privacy preservation and (2) it discusses the security, usability, and the efficiency of the framework at the same time. Our proposed framework is based on zero-knowledge proof systems zkSNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) and zkSTARKs (Zero-Knowledge Scalable Transparent Argument of Knowledge) tailored for computer on medical data without revealing effectively. We conduct extensive analysis and prototype implementation to show that our framework is able to achieve perfect privacy preservation at a 1.87% computational overhead increase with respect to standard smart contracts. The system processes over 10,000 medical records with sub-second verification times and that meet the HIPAA requirements. Experimental results in diverse healthcare applications attest to the efficacy of the approach in practice, and show the substantial gain of privacy preservation (99.8% retention rate) and computational efficiency over the state-of-art algorithms. This paper bridges the gap between blockchain’s transparency and healthcare’s privacy requirements, laying the groundwork for secure and privacy-preserving blockchain based healthcare applications. Key Words: Zero-knowledge proofs, Smart contracts, Healthcare blockchain, Privacy preservation, zkSNARKs, zkSTARKs, Medical data security, HIPAA compliance