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.
Traditional blockchain protocols incentivize constant participation through resource-intensive mechanisms such as Proof-of-Work (PoW) or Proof-of-Stake (PoS). However, this model leads to excessive energy consumption and network redundancy. This paper proposes a novel consensus auxiliary protocol called Proof-of-Inactivity (PoI), wherein nodes earn trust by deliberately abstaining from consensus activities for defined, randomized periods. Inactivity is cryptographically proven via time-locked commitments and publicly verifiable absence proofs. This paradigm aims to reduce redundant communication, lower energy usage, and support lightweight node participation without compromising network integrity. The protocol is introduced alongside a security assessment, performance analysis, and possible energy savings based on simulated scenarios, including conversations about DIDs, zero-knowledge mechanism, hybrid consensus, and adaptability.
M Ramya, B S Anuvarshini, G Kanika, A Lohith · 5 authors
The identity verification is required by many digital platforms, it raises real privacy and security risks when users are required to submit identity documents multiple times. When identity documents are stored in centralized databases data breaches or misuse from within the organization holding the data may occur. To mitigate risks associated with centralized data storage models, a decentralized identity management system is being created using blockchain and distributed file storage system architecture. When users submit identity documents, the data is stored securely in a decentralized file storage system and a cryptographic hash is generated that will be used to reference the data at a later time. This hash will then be recorded on a blockchain that is immutable and creates a secure log. Identification is verified through a manual verification process by authorized reviewers who approve or reject submissions all while the user is able to monitor submission status. User identity privacy is maintained by using zero-knowledge proofs to verify distinct attributes of identity that are required without revealing the identity data itself. With proof verification attaining a success rate of $98.6 \%$, admin validation accuracy of $96.4 \%$, and gas optimization leading to a $\mathbf{3 1 \%}$ decrease in execution costs in comparison to baseline smart contract calls, the system showed outstanding operational efficiency. This will provide users with more control over their identity documents while also providing secure, transparent and privacy preserving identity verification to be used in modern digital services.
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.
Yan Xu, Jingqi Zhang, Xiyan Dong, An Wang · 6 authors
Multi-scalar multiplication (MSM) is the primary computational bottleneck in zero-knowledge proof protocols. To address this, we introduce FAMA, an FPGA-oriented MSM accelerator developed through algorithm-hardware co-optimization. By integrating a 3D-Pippenger optimization algorithm, FAMA minimizes computational complexity, while its compact dual-mode point addition (PADD) unit significantly reduces hardware overhead. Compared to the best CPU-based design, FAMA achieves over 184.20× speedup. It also outperforms state-of-the-art FPGA-based MSM accelerators, reducing resource overhead by more than 64% and boosting area-time product (ATP) by up to 37.09×.
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.
Vincent Duronio, Mohamed Firas Aguir, Hari Sai Kaja, Andrew J. Kalafut · 6 authors
The Internet of Health Things (IoHT) introduces new opportunities for remote patient monitoring but also exposes sensitive medical data to serious security risks. Traditional authentication mechanisms are often too resource-intensive for IoHT devices, which are constrained in terms of storage, computation, and energy. This paper presents a lightweight, decentralized authentication protocol tailored for IoHT environments, integrating blockchain technology, Physical Unclonable Functions (PUFs), and zero-knowledge proofs (zk-SNARKs). Our protocol employs the Proof of Elapsed Work and Luck (PoEWAL) consensus mechanism, enabling secure, energy-efficient participation in a private Hyperledger Fabric blockchain. The scheme ensures strong mutual authentication between healthcare providers and IoHT devices without exposing sensitive data. An informal analysis is done to demonstrate the protocol’s resilience against common attacks. This protocol offers a secure solution for enabling trustworthy healthcare communication in resource-limited IoHT networks.
Physical Unclonable Functions (PUFs) and Hardware Security
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.
Conventional cryptographic approaches face increasing insecurity because quantum computing and attacks enabled by AI operate at a rapid speed of technological advancement. The review analyzes IEEE research activities on progressive cryptographic methods consisting of post-quantum cryptography (PQC) and fully homomorphic encryption (FHE) in addition to zero-knowledge proofs (ZKP) and AI-augmented cryptographic models. We explore both advantages and limitations in addition to applicable uses for protecting modern digital frameworks which include blockchain, IoT and 5G networks. Standardization efforts as well as hybrid crypto framework trends receive analysis in the article to establish long-lasting secure systems.
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.
This paper proposes a framework that integrates zero-knowledge proofs (ZKPs) and verifiable credentials (VCs) to achieve the synergistic optimization of transaction privacy and trusted identity authentication. First, a pseudonymous transaction protocol based on zk-SNARKs (zero-knowledge succinct non-interactive arguments of knowledge) is designed. The R1CS constraint system is used to construct a circuit that hides the transaction amount and address, resulting in a proof that takes up only 1.5 KB. Second, the W3C standard VC is introduced to enable off-chain identity attribute authentication. Users generate a “credential validity proof” using ZKPs and upload the proof along with the transaction to the chain. Finally, smart contracts verify the dual proofs, using Merkle tree aggregation to reduce verification overhead. An innovative “attribute-bound anonymous transaction” model is proposed to dynamically decouple transaction and identity attribute verification. Deployment on the Ethereum testnet demonstrates that this scheme reduces transaction correlation to approximately 0.3 %. Groth16 achieves a throughput of 142 transactions per second (TPS) in 798 ms at a scale of 200,000 gates.
В статье рассматриваются основы технологии нулевых доказательств знания (Zero-Knowledge Proofs, ZK), её значение для развития криптовалютных систем и децентрализованных финансов. Особое внимание уделено механизмам zk-SNARK и zk-STARK, а также их практическому применению в проектах Zcash, Ethereum, Polygon и zkSync. Проведен анализ проблем масштабируемости, вычислительной сложности и регуляторных рисков, связанных с использованием ZK-технологий. Отмечаются перспективы интеграции в архитектуру цифровых валют центральных банков и развитие инфраструктуры Web3 на базе ZK-решений