The need for secure user authentication in blockchain-based applications has been growing with the increased adoption of Decentralized Identity (DID) credentials in blockchain.Zokrates, a tool designed to protect user privacy within smart contracts, had a limitation in that it could not accept authenticated user information such as credentials, only allowing the use of manually inputted data.In this paper, we propose a smart contract system that securely validates DID credentials to overcome the limitations of traditional centralized authentication systems.This system ensures the safe identification of users within blockchain-based applications by authenticating their identities in a trusted manner within the blockchain.As the demand for user authentication in blockchain rises, this paper emphasizes the significance of a blockchain-based identity verification system that guarantees both privacy and security.Leveraging the Zero-Knowledge Proof method and utilizing the Zokrates tool, this innovative approach aims to provide solutions for the digital identity verification process, thereby expanding the scope of blockchain technology applications.Moreover, we also provide a CLI for each entity.We help anyone who wants to authenticate their identity using the tool to safely verify it on-chain.
Given a JPEG pipeline (compression or decompression), this paper demonstrates how to find the antecedent of an 8x8 block. If it exists, the block is considered compatible with the pipeline. For unaltered images, all blocks remain compatible with the original pipeline; however, for manipulated images, this is not necessarily true. This article provides a first demonstration of the potential of compatibility-based approaches for JPEG image forensics. It introduces a method to address the key challenge of finding a block antecedent in a high-dimensional space, relying on a local search algorithm with restrictions on the search space. We show that inpainting, copy-move, and splicing, when applied after JPEG compression, result in three distinct mismatch problems that can be detected. In particular, if the image is re-compressed after modification, the manipulation can be detected when the quality factor of the second compression is higher than that of the first. Through extensive experiments, we highlight the potential of this compatibility attack under varying degrees of assumptions. While our approach shows promising results-outperforming three state-of-the-art deep learning models in an idealized setting-it remains a proof of concept rather than an off-the-shelf forensic tool. Notably, with a perfect knowledge of the JPEG pipeline, our method guarantees zero false alarms in block-by-block localization, given sufficient computational power.
Open access
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
A. S. (Ahmed) Al-Dulaimi, P. (Priya) Krishnan, L. M. (Lucas) Fernández
Ensuring workforce compliance has become a critical priority for global enterprises, particularly in sectors such as banking, healthcare, aviation, and logistics, where safety, security, and regulatory adherence are non-negotiable. Traditional drug testing, alcohol screening, and background verification workflows often rely on fragmented, paper-based, or siloed digital systems that introduce inefficiencies, data integrity risks, and audit challenges. These limitations hinder organizations from meeting the increasing demands of multi-jurisdictional compliance frameworks while also exposing them to reputational and legal risks. This article explores the design and implementation of a blockchain-enabled workforce compliance platform that reimagines the verification pipeline through the principles of immutability, decentralization, and cryptographic security. The proposed architecture leverages permissioned blockchain networks, integrated with smart contracts and distributed identity frameworks, to ensure that drug and alcohol testing results, criminal background checks, and employment verification records are securely stored, transparently auditable, and accessible only to authorized stakeholders. We present a comparative framework highlighting how blockchain-based compliance pipelines outperform traditional systems in areas such as tamper-proof recordkeeping, cross-border interoperability, and regulatory audit readiness. The study also emphasizes integration strategies with enterprise HR systems, third-party verification agencies, and global regulatory standards (e.g., GDPR, HIPAA, OSHA, and aviation compliance codes). Furthermore, the role of privacy-preserving technologies such as zero-knowledge proofs and decentralized identifiers (DIDs) is discussed in enabling compliance without compromising employee confidentiality. The findings demonstrate that blockchain-enabled compliance solutions deliver significant strategic value, including enhanced trust with regulators, reduced operational overhead, real-time verification capabilities, and a resilient defense against fraud or record tampering. By adopting a secure, transparent, and auditable compliance pipeline, global enterprises can build workforce ecosystems that are not only regulatory-compliant but also future-ready for emerging challenges in cross-border governance and digital workforce management.
Proofs are powerful tools for integrity and privacy, enabling the verifier to delegate a computation and still verify its correct execution, and enabling the prover to keep the details of the computation private. Both CCP and ZKP can achieve soundness and zero-knowledge but with important differences. CCP relies on hardware trust assumptions, which yield high performance and additional confidentiality protection for the prover but may be unacceptable for some applications. CCP is also often easier to use, notably with existing code, whereas ZKP comes with a large prover overhead that may be unpractical for some applications.
Jelle Piepenbrock, Josef Urban, Konstantin Korovin, Miroslav Olšák · 6 authors
The development of strong CDCL-based propositional (SAT) solvers has greatly advanced several areas of automated reasoning (AR). One of the directions in AR is therefore to make use of SAT solvers in expressive formalisms such as first-order logic, for which large corpora of general mathematical problems exist today. This is possible due to Herbrand's theorem, which allows reduction of first-order problems to propositional problems by instantiation. The core challenge is synthesizing the appropriate instances from the typically infinite Herbrand universe. In this work, we develop a machine learning system targeting this task, addressing its combinatorial and invariance properties. In particular, we develop a GNN2RNN architecture based on a graph neural network (GNN) that learns from problems and their solutions independently of many symmetries and symbol names (addressing the abundance of Skolems), combined with a recurrent neural network (RNN) that proposes for each clause its instantiations. The architecture is then combined with an efficient ground solver and, starting with zero knowledge, iteratively trained on a large corpus of mathematical problems. We show that the system is capable of solving many problems by such educated guessing, finding proofs for 32.12% of the training set. The final trained system solves 19.74% of the unseen test data on its own. We also observe that the trained system finds solutions that the iProver and CVC5 systems did not find.
Open access
Natural Language Processing Techniques
Handwritten Text Recognition Techniques
Mathematics, Computing, and Information Processing
Condorcet voting is widely regarded as one of the most important voting systems in social choice theory. However, it has seen little adoption in practice, due to complex tallying and the need to break ties when there is a Condorcet cycle. Several online Condorcet voting systems have been developed to perform digital tallying and tie-breaking procedures, but they require voters to completely trust the server. Additionally, many end-to-end (E2E) verifiable e-voting systems require trustworthy authorities to perform complex decryption and tallying operations. We propose VERICONDOR, the first E2E verifibbolable Condorcet e-voting system without tallying authorities. VERICONDOR allows a voter to fully verify the tallying integrity by themselves while providing strong protection of ballot secrecy. We present novel zero-knowledge proof techniques to prove the well-formedness of an encrypted ballot with exceptional efficiency. VERICONDOR supports ranking candidates with strict preference, as well as indifference. The computational cost is exceptionally efficient for strict preferences at \(\mathcal{O}(n^{2})\) per ballot for \(n\) candidates, while remaining practical for indifferences at \(\mathcal{O}(n^{3})\) . In the case of ties, we show how to apply known Condorcet methods to break them in a publicly verifiable manner. Finally, we present a proof of concept implementation and evaluate its performance.
The challenge we address in this thesis is to design a peer-to-peer threshold-based group key generation protocol where there is no centralized authority and peers do not learn anything about the generated key other than their shares albeit they are involved in the key generation. To address this challenge, we propose a password-authenticated threshold-based group key agreement protocol that resembles secret sharing. However, the construction of our proposal is the opposite. In legacy secret-sharing schemes, a trusted third party shares a secret value that it initially knows. In our proposal, principals collaborate to create shares without the knowledge of the constructed group key in a peer-to-peer fashion. We employ zero-knowledge protocols for verifying the authenticity of principals' contributions to the protocol. Zero-knowledge proofs help principals verify the veracity of data without sharing the data. In addition to the threshold-based group key agreement protocol, we also propose a novel zero-knowledge proof scheme based on equal partition problems on groups and prove that this novel zero-knowledge proof scheme is secure. We present the security and performance analysis of the proposed protocols.
Tatami puzzles are pencil puzzles with an objective to partition a rectangular grid into rectangular regions such that no four regions share a corner point, as well as satisfying other constraints. In this paper, we develop a physical card-based protocol called Tatami printer that can help verify solutions of Tatami puzzles. We then use the Tatami printer to construct zero-knowledge proof protocols for two such puzzles: Tatamibari and Square Jam. These protocols enable a prover to show a verifier the existence of the puzzles' solutions without revealing them.
Zero-Knowledge Proofs (ZKPs) are cryptographic tools that enables one party, known as the prover, to prove to another party, the verifier, that a certain statement is true without revealing any information beyond the validity of the statement itself. We introduce the Neutrosophic One-Round Zero-Knowledge Proof protocol (N-1-R) ZKP, which is an extension of the One-Round (1-R) ZKP in the realm of Neutrosophic numbers. The N-1-R ZKP is the first Neutrosophic ZK protocol.
Electronic voting has been a hot research topic for decades and has recently garnered much attention due to the invention of programmable blockchains that support smart contracts. This is the ideal framework and technology for electronic voting since voting protocols implemented as smart contracts automatically inherit many desired properties from the underlying blockchain, e.g. verifiability, transparency and pseduonymity. However, the public and decentralized nature of the blockchain allows all transactions to be traced by everyone and thus voters' choices would be disclosed publicly. There are many solutions to make blockchain-based voting fully anonymous and untraceable. A recent example is Tornado Vote [1] (ICBC 2023). Such protocols often rely on zero-knowledge proofs, especially zkSNARKS, to achieve secrecy and break the link between a voter's public key and vote. However, verifying these proofs on-chain is expensive and uses a considerable amount of gas (execution fees). In this work, we propose a new approach called Blind Vote, which is an untraceable, secure, efficient, secrecy-preserving and fully on-chain electronic voting protocol based on the well-known concept of Chaum's blind signatures. We illustrate that our approach achieves the same security guarantees as previous methods such as Tornado Vote, while consuming significantly less gas. Thus, we provide a cheaper and considerably more gas-efficient alternative for anonymous blockchain-based voting.
Samuel Hand, Alexander Koch, Pascal Lafourcade, Daiki Miyahara · 5 authors
Abstract A zero-knowledge proof (ZKP) allows a prover to prove to a verifier that it knows some secret, such as a solution to a difficult puzzle, without revealing any information about it. In recent years, ZKP protocols using only a deck of playing cards for solutions to various pencil puzzles have been proposed. The previous work of Lafourcade et al. deals with a famous puzzle called Slitherlink. Their proposed protocol can verify that a solution forms a single loop without revealing anything about the solution, except this fact. Their protocol guarantees that the solution satisfies the single-loop condition, by interactively constructing a solution starting from a state that holds a simple single loop, and proceeding via steps that preserve the invariant of encoding a single loop, until the proper solution is reached. A drawback of their protocol is that it requires additional verifications to guarantee a single loop. In this study, we propose a more efficient ZKP protocol for such a puzzle with fewer additional verifications. For this, we employ the previous work of Robert et al., which addressed the connectivity property in a puzzle. That is, we verify that a solution is connected but not split, to be a single loop. Applying our proposal, we construct a card-based ZKP protocol for Moon-or-Sun, which has its specific rule of alternating pattern in addition to the single-loop condition.
Open access
Advanced Steganography and Watermarking Techniques
A common aim of epidemiological research is to estimate the association between a particular exposure and a particular outcome, with the aim to better understand potential causal mechanisms behind this association. In 2020, Li et al1 proposed a method they called ‘Inference about causation from examination of familial confounding’ (abbreviated ICE FALCON), which uses twin data (or, more generally, data on related individuals) to assess whether an association is due to a causal effect of the exposure, reverse causation, familial confounding, or a mix of the three. In brief, this method requires the analyst to first regress an individual’s outcome on her own exposure, then on her twin’s exposure, and then finally on her own exposure and her twin’s exposure simultaneously. Li et al1 argued that by examining the pattern of the estimated regression coefficients the analyst could infer the true mechanism that generated the data, and claimed the method was analogous to Mendelian randomization (MR), but with certain additional benefits. Since this methodological paper was published it has been cited 31 times according to Google Scholar, and in a later paper, Li et al2 claimed that the ICE FALCON method has ‘provided causal evidence for numerous exposure and outcomes’, citing 14 publications for this claim. In this cautionary note, we argue that the ICE FALCON method is based on highly unrealistic assumptions that were not clearly stated by Li et al,1 and several statistical errors. The note is organized as follows. We first review the key elements of the ICE FALCON method. Next, we lay out our critique against the method. We finally consider the real data example given by Li et al,1 and argue that far less can be said about underlying mechanisms in this example than promised by the ICE FALCON method. Throughout, we ignore sampling variability and focus on the large sample behaviour of estimated regression coefficients. We adopt the notation by Li et al.1 Let Xself and Yself be the exposure and outcome for a particular individual, and Xco-twin and Yco-twin be the exposure and outcome for that individual’s co-twin. Li et al1 considered the possible mechanisms illustrated by the causal diagrams in Figure 1 (identical to their Figure 1). In these diagrams, SXY is the set of family-constant confounders for X and Y, SX is the set of family-constant factors that influence X but not Y, and SY is the set of family-constant factors that influence Y but not X. Finally, U is the set of unmeasured within-individual confounders, i.e. the confounders that may have different values for the two twins in a pair. Apart from unmeasured within-individual confounding, in Figure 1a the statistical association between X and Y is due to familial confounding, in Figure 1b it is due to causation, and in Figure 1c it is due to reverse causation. Figure 1d illustrates a mixture of familial confounding and causation, and Figure 1e illustrates a mixture of familial confounding and reverse causation. Like Li et al,1 we first restrict attention to the three simpler scenarios in Figure 1a-c, and later comment on mixtures of these. Possible mechanisms underlying a statistical association between X and Y, as proposed by Li et al1 Note that, in Models 1–3, the intercepts α, α′ and α″ are generally different, as well as the coefficients βself and βself′, and the coefficients βco-twin and βco-twin′. Note also that, since X and Y are available for both twins in each pair, each twin contributes as both ‘self’ and ‘co-twin’ to the fitted models. Li et al1 argued that the mechanisms in Figure 1a-c imply the coefficient patterns summarized in Table 1 (identical to their Table 1), where ρX and ρY are the within-pair correlations in X and Y, respectively. Since each pattern (e.g. column in the table) is unique, they concluded that the estimated regression coefficients could be used to distinguish between these three mechanisms. For instance, if we observe that βself′=βself and βco-twin′=0, then, according to Li et al,1 we can rule out both familial confounding and reverse causation, since this coefficient pattern is unique for causation. Unique regression coefficient patterns implied by the three mechanisms in Figure 1, according to Li et al1 Unique regression coefficient patterns implied by the three mechanisms in Figure 1, according to Li et al1 Li et al1 provided both graphical arguments (in their main text) and mathematical proofs (in their Supplementary material) for the coefficient patterns in Table 1. However, there are two important problems with these arguments and proofs: (1) they ignore the influence of unmeasured within-individual confounders, and (2) they conflate statistical conditioning with accounting for correlated observations. Below, we discuss these problems in more detail. In the Supplementary material we show that, when these issues are acknowledged, there is no guarantee that the mechanisms in Figure 1a-c will produce distinct coefficient patterns. In particular, we give numerical examples where all three mechanisms result in identical coefficient patterns. In virtually all real family studies, unmeasured within-individual confounders U will be present. Or stated differently: there are likely to exist confounders which are not identically shared by the twins, and which we cannot perfectly adjust for in our regression models. Depending on the context, these may have larger or smaller magnitude than the familial confounders SXY, and may affect X and Y in the same or opposite direction. We have previously shown that unmeasured within-individual confounders have important consequences for the interpretation of sibling comparison studies, since their effect will be numerically different in the full cohort and when conditioning on the sibling pair.3 However, Li et al1 effectively ignored unmeasured within-individual confounders when deriving Table 1. For instance, they wrote (page 1261, second column) ‘If there is familial confounding only … (Figure 1a), there will be associations between… Yself and Xco-twin (βco-twin, Model 2). Adjusting for Xself (Model 3), there will still be a conditional association between Yself and Xco-twin (βco-twin′), but it will be attenuated towards the null compared with βco-twin’. This conclusion is not generally valid when Uself is present, since adjusting for Xself then opens the path Yself←Uself→Xself←SX→Xco-twin at which Xself is a collider, which may inflate the association between Yself and Xco-twin. As another example, they wrote: ‘If there is a causal effect from X to Y only (Figure 1b), … the conditional [on Xself] association between Yself and Xco-twin (βco-twin′) will be null…’ This conclusion is also invalid when Uself is present, since adjusting for Xself then again opens the aforementioned path, which will induce a statistical association between Yself and Xco-twin. Similar issues apply to several of the other coefficient patterns in Table 1. In their Supplementary material, Li et al1 explicitly articulated the assumption of no unmeasured within-individual confounders. However, they did this by writing ‘For simplicity we assume that … there was no within-individual confounding’ (italics added), without commenting on the implausibility of this assumption in real studies or acknowledging that the ICE FALCON method is invalid when the assumption is violated. This gives the reader the misleading impression that the assumption was made purely for mathematical convenience, and that it is not required by the ICE FALCON method. This impression is further enforced by the fact that, in the main text of the paper, the assumption is not mentioned at all; on the contrary, all the causal diagrams in the paper include within-individual confounders, and the text repeatedly refer to these as if they are not assumed absent. For instance, when comparing the ICE FALCON method to MR, they state (in their Table 4), as a relative advantage of the former, that ‘Xco-twin is theoretically unrelated to unmeasured confounders specific to an individual only [italics added]’. We have not scrutinized all 14 papers that Li et al2 cite for using the ICE FALCON method, but several of them appear to have entirely missed the crucial role of unmeasured within-individual confounders for the method. For instance, Zheng et al4 studied the association between socio-economic status and obesity among adult monozygotic and dizygotic twins, which may clearly suffer from unmeasured within-individual confounding by various life-style and (for dizygotic twins) genetic factors. The authors indeed included within-individual confounders in their causal diagrams (identical to Figure 1a-c); however, they then proceeded by analysing data with the ICE FALCON method as if unmeasured within-individual confounders were absent altogether. Then, Model 2* would still produce incorrect standard errors if fitted with ordinary linear regression instead of a GEE, since each twin contributes as both ‘self’ and ‘co-twin’ to the model. This conceptual mistake led Li et al1 to several incorrect conclusions about the coefficient patterns in Table 1. For instance, they wrote ‘If there is a causal effect from Y to X only (Figure 1c), … [then in] Model 2, there is no open path between Yself and Xco-twin—the path through SY is closed due to the fact that Yco-twin is conditioned on’. Presumably, they here referred to the path Yself←SY→Yco-twin→Xco-twin, which would indeed be closed if Yco-twin were conditioned on. However, since fitting Model 2 with a GEE does not condition on Yco-twin the path remains open. Ironically, if Yco-twin were truly conditioned on, then the path Yself←SY→Yco-twin←Uco-twin→Xco-twin at which Yco-twin is a collider would be open instead. Curiously, whereas Li et al1 claimed that Model 2 becomes conditioned on Yco-twin when accounting for correlated observations, they did not make such claims for Models 1 and 3, whereas, in fact, the issue of correlated observations is equally present for these models. Thus, by their (incorrect) logic, Models 1 and 3 would also be conditioned on Yco-twin when fitted with a GEE. In real scenarios, the true mechanism behind an observed association will likely be a mixture of confounding and causation, or between confounding and reverse causation, as in Figure 1d and e, respectively. For such scenarios, Li et al1 wrote (page 1262, first column): ‘…the result will be a mixture [of the patterns in Table 1]… The changes in the pair of regression coefficients from comparing Model 3 with Models 1 and 2 will apply, allowing assessment of evidence for causality still to be made’. To illustrate, Li et al1 provided two real data examples. In one of their examples, the exposure was body mass index (BMI) measured at baseline, and the outcome was BMI measured at a later time point during follow-up. Data were collected on adult twins from 250 monozygotic twin pairs. From the temporal order of the BMI measures, we can rule out reverse causation a priori. However, there is clearly a high potential for both confounding (familial as well as within-individual) and causation. In particular, the members of an adult twin pair may have very different eating and exercise habits from each other, which could then be strong unmeasured within-individual confounders for BMI measures over time. Li et al1 presented the following estimated regression coefficients: βself=0.81, βco-twin=0.73, βself′=0.73 and βco-twin′=0.15. The pattern of these coefficients is a mixture of ‘Familial confounding’ and ‘X causes Y’ in Table 1, with the attenuation from βco-twin to βco-twin′ being stronger than the attenuation from βself to βself′. Li et al1 concluded that these results are ‘… consistent with a longitudinal causation, as well as a small amount of familial confounding’. We do not disagree with this conclusion, which sounds intuitively appealing given the nature of BMI and its stability over time, even before doing any study on the topic. However, since the pattern of regression coefficients in Table 1 are derived under the unrealistic assumption of no unmeasured within-individual confounding, we fail to see that the presented regression coefficients give any strong further support for this hypothesis. If we are open to the presence of unmeasured within-individual confounding, the results would also be consistent with other possible mechanisms, such as a large amount of familial confounding together with a small amount of causation, or even a total absence of causation. We demonstrate this with numerical examples in the Supplementary material where the mechanisms in Figure 1a-c all give virtually the same coefficients as above for the BMI data. In the presence of unmeasured within-individual confounding, the regression coefficients alone simply cannot discriminate between these three mechanisms. This can also be seen directly from the causal diagrams. Consider the causal diagram in Figure 1a, where causation is entirely absent. In this diagram, Xself and Yself are associated through two paths: Xself←SXY→Yself and Xself←Uself→Yself, whereas Xco-twin and Yself are only associated though the first path, which could explain why βself> βco-twin. When conditioning on Xco-twin, the path Xself←SX→Xco-twin←SXY→Yself becomes open, which could explain why βself>β′self. Similarly, when conditioning on Xself, the path Xco-twin←SX→Xself←Uself →Yself becomes open, which could explain why βco-twin>β′co-twin. In this note, we have demonstrated some inaccuracies in the derivation of the ICE FALCON method, and highlighted that the method fails to distinguish between competing causal hypotheses in the presence of within-individual confounding. Even in their title, Li et al1 proposed that ICE FALCON was ‘analogous’ to MR, and further claimed in their key messages that a benefit over MR would be that ICE FALCON ‘does not make strong assumptions’. We think the comparison to MR is overstated and misleading, as the ICE FALCON cannot be described as an instrumental variable method, uses completely different types of data, and does not lend itself to an estimation of the causal effect even when the (indeed) very strong assumptions underlying it are fulfilled. Whereas we have focused on the most important problems with the paper by Li et al,1 there are other, more technical issues with the paper as well, which we discuss in the Supplementary material. In brief, their mathematical derivations have an error that potentially invalidates their results, even in the absence of unmeasured within-individual confounding, and they used GEEs in an inappropriate way that is almost guaranteed to give bias for twin data. The latter issue does not invalidate the ICE FALCON method per se, but it does potentially invalidate their real data analysis results. Yet another issue worth mentioning is the extension of the ICE FALCON method to non-linear models. Li et al1 appear to claim that this extension is trivial, by stating (page 1266) that ‘ICE FALCON is based on regression, so the method can be applied to continuous and binary outcomes using ordinary and logistic regression, respectively, and potentially to survival data using Cox regression. There are no restrictions on the measurement scale of exposures’. This seems overly optimistic, given that their Supplementary material proofs use analytic results for linear regression coefficients that are not easily transferred to non-linear models. In particular, due to the non-collapsibility of odds ratios and hazard ratios,5 we conjecture that it is very hard to derive universal patterns of the regression coefficients of logistic regression models and Cox regression models under the mechanisms in Figure 1, even in the absence of unmeasured within-individual confounders. The idea that underlying mechanisms can be inferred for family data by comparing (changes in) regression coefficients is not new. Hudson et al6 also considered the causal diagrams in Figure 1, and discussed what can be inferred about familial confounding from Models 1–3. However, they were substantially more modest in their conclusions than Li et al.1 They concluded that familial confounding can be ruled in by a non-zero coefficient βco-twin, provided that one a priori rules out both causation and reverse causation. This fact follows immediately by noting that, in Figure 1a, the only explanation for an association between Xco-twin and Yself (e.g. non-zero βco-twin) is the presence of familial confounders SXY. They also carried out a simulation, which indicated that the degree of familial confounding may often lie between βco-twin and β′co-twin, when causation is present. However, they cautioned the reader that a simulation does not provide definitive evidence, and unlike Li et al, acknowledged that their particular simulation made several strong assumptions (e.g. normally distributed errors, no statistical interactions, all effects being positive, etc) that will not hold in all real studies. We have repeatedly argued that the complete absence of unmeasured within-individual confounding is very unlikely in practice, but some might counter that the absence of unaccounted for confounding is necessary to draw causal conclusions from all observational studies, and thus that this is not a unique weakness of the ICE FALCON method. To this we would reply that there is a large difference between the common practice in epidemiology where we remain open to the idea that the reported association remains partly confounded (discussing the risk and magnitude of residual confounding, often in relation to differently adjusted models), and the ICE FALCON where we on the one hand must assume that there is confounding by some factors shared identically by relatives (else the method is meaningless), and on the other hand assume that there is no confounding by anything which is correlated less than 1 between twins. Since most potential confounders are somewhere in between (genetic markers would, e.g. be correlated 0.5 in first degree relatives), we can only imagine this to be a plausible assumption when we already have so much knowledge about the research question that it is no longer meaningful to rule out alternative hypotheses for the causation. In particular, if we are interested in estimating a causal effect when we suspect there may be familial confounding but no other bias, we should use a standard sibling comparison design,7 which requires fewer models and gives us a direct estimate of the effect size. We also note that, if the ICE FALCON method is used for other types of relatives than twins (e.g. ordinary siblings or half-siblings), then one would expect the sets of within-individual confounders to be larger for less related individuals. Finally, we note that the ICE FALCON method can only, at best, indicate which underlying mechanisms are at play, but does not provide estimates of their relative importance. Such estimates can be obtained with variance decomposition through structural equation modelling; see Maes et al8 and the references therein. Since there are no new data associated with this article, there was no need for ethics approval. There are no new data associated with this article. Supplementary data are available at IJE online. All work for this paper was carried out by Arvid Sjölander and Thomas Frisell jointly. This work was supported by the Swedish Research Council [2020-01188 to A.S.]. None declared. Artificial intelligence (AI) was not used for any parts of this paper.
With the rapid spread of the use of Internet of Things devices, it has become an important situation for these devices to be provided with critical infrastructure, integrated into daily life and the creation of robust security mechanisms. The attribute-based access control (ABAC) method has emerged as a promising approach to manage access of IoT resources based on users' attributes. However, current ABAC models lack adequate privacy protections and do not address specific vulnerabilities, especially in scenarios where sensitive data is involved. The research includes a comprehensive review of the ABAC models that stand out in the context of IoT security, including the limitations and vulnerabilities that they carry. In this work, a new framework has been proposed that integrates zero-knowledge proofs (ZKP) with homomorphic encryption into the ABAC model, providing stronger security guarantees and privacy protection. While ZKPs allow users to prove that they have certain attributes or access rights without disclosing sensitive information, homomorphic encryption allows calculations to be performed on encrypted data without decryption. The proposed framework has been evaluated by theoretical analysis and simulation studies. The findings of this research are expected to contribute significantly to the field of IoT security by providing a more robust and privacy-protecting access control mechanism for IoT environments. The proposed framework has the potential to mitigate various security threats, including unauthorized access, data and privacy violations
Zero-Knowledge Proofs (ZKPs) are an emergent paradigm in verifiable computing. In the context of applications like cloud computing, ZKPs can be used by a client (called the verifier) to verify the service provider (called the prover) is in fact performing the correct computation based on a public input. A recently prominent variant of ZKPs is zkSNARKs, generating succinct proofs that can be rapidly verified by the end user. However, proof generation itself is very time consuming per transaction. Two key primitives in proof generation are the Number Theoretic Transform (NTT) and Multi-scalar Multiplication (MSM). These primitives are prime candidates for hardware acceleration, and prior works have looked at GPU implementations and custom RTL. However, both algorithms involve complex dataflow patterns -- standard NTTs have irregular memory accesses for butterfly computations from stage to stage, and MSMs using Pippenger's algorithm have data-dependent memory accesses for partial sum calculations. We present SZKP, a scalable accelerator framework that is the first ASIC to accelerate an entire proof on-chip by leveraging structured dataflows for both NTTs and MSMs. SZKP achieves conservative full-proof speedups of over 400$\times$, 3$\times$, and 12$\times$ over CPU, ASIC, and GPU implementations.
In recent years, blockchain technology has evolved significantly, enabling a decentralized network application model that offers both user anonymity and transparency. This unique characteristic of blockchain has led to its adoption in various sectors, including healthcare, finance, and transportation. The advancement of modern zero-knowledge proof technology has further enhanced blockchain's applications across these fields, bolstering privacy protection. Zero-knowledge proofs have become a key mechanism in blockchain smart contracts, offering a balance between transparency and privacy. Moreover, the integration of zero-knowledge proof technology with blockchain is facilitating technical advancements in areas facing challenges, such as autonomous driving technology. It is also addressing security concerns in more established technologies like the Internet of Things. This synergy between zero-knowledge proof and blockchain technologies is paving the way for innovative solutions across a wide range of applications.
The digital transformation of the real estate industry is being significantly influenced by blockchain technology and smart contracts, which promise enhanced efficiency, transparency, and security in transactions. This study aims to develop a secure and efficient smart contract management protocol that balances the benefits of blockchain with robust data privacy practices. The methodology involves descriptive analytics of transaction data from the Ethereum blockchain, feasibility studies using synthetic transaction data, and a regulatory compliance analysis to map the impact of different regions' regulations on blockchain adoption in real estate. The findings reveal that while smart contracts can automate various processes and reduce reliance on intermediaries, challenges related to data privacy and regulatory compliance persist. Higher privacy features in smart contracts are associated with increased execution costs, indicating a trade-off between privacy and cost efficiency. Smart contracts with privacy level 3 had an execution cost of 0.025 ETH, compared to those with privacy level 1 at 0.02 ETH. Integrating permissioned blockchains and zero-knowledge proofs offers a promising solution, though their complexity limits broader adoption. Zero-knowledge proofs maintained high privacy (achieving privacy levels of up to 0.76) at a reasonable computational cost (proof generation time of 1.9 seconds). Thus, the integration of permissioned blockchains and zero-knowledge proofs offers a promising pathway to address these challenges. However, the complexity of these techniques requires specialized knowledge, limiting broader adoption. The study concludes with recommendations to develop specialized training programs, collaborate on regulatory frameworks, invest in advanced cryptographic research, and implement targeted strategies to overcome adoption barriers. These efforts will contribute to the digital transformation of asset management, fostering innovation and enhancing the overall efficiency of real estate transactions.
Sandip Das, A. Lahiri, Soumen Nandi, Sagnik Sen · 5 authors
An ( n , m ) -graph is characterized by n types of arcs and m types of edges. A homomorphism of an ( n , m ) -graph G to an ( n , m ) -graph H , is a vertex mapping that preserves adjacency, direction, and type. The ( n , m ) -chromatic number of G , denoted by χ n , m ( G ) , is the minimum value of | V ( H ) | such that there exists a homomorphism of G to H . The theory of homomorphisms of ( n , m ) -graphs have connections with graph theoretic concepts like harmonious coloring, nowhere-zero flows; with other mathematical topics like binary predicate logic , Coxeter groups; and has application to the Query Evaluation Problem (QEP) in graph database. In this article, we show that the arboricity of G is bounded by a function of χ n , m ( G ) but not the other way around. Additionally, we show that the acyclic chromatic number of G is bounded by a function of χ n , m ( G ) , a result already known in the reverse direction. Furthermore, we prove that the ( n , m ) -chromatic number for the family of graphs with maximum average degree less than 2 + 2 4 ( 2 n + m ) − 1 , including the subfamily of planar graphs with girth at least 8 ( 2 n + m ) , equals 2 ( 2 n + m ) + 1 . This improves upon previous findings, which proved the ( n , m ) -chromatic number for planar graphs with girth at least 10 ( 2 n + m ) − 4 is 2 ( 2 n + m ) + 1 . It is established that the ( n , m ) -chromatic number for the family T 2 of partial 2-trees is both bounded below and above by quadratic functions of ( 2 n + m ) , with the lower bound being tight when ( 2 n + m ) = 2 . We prove 14 ≤ χ ( 0 , 3 ) ( T 2 ) ≤ 15 and 14 ≤ χ ( 1 , 1 ) ( T 2 ) ≤ 21 which improves both known lower bounds and the former upper bound. Moreover, for the latter upper bound, to the best of our knowledge we provide the first theoretical proof.
As outlined in the Paris Agreement, the global commitment to achieving net-zero emissions by 2050 necessitates a multifaceted approach encompassing clean energy initiatives and carbon taxation.Higher education institutions, recognizing their role as key contributors to sustainability, are increasingly focusing on reducing their carbon footprint.The teaching laboratories, essential for various disciplines, contribute significantly to the university's carbon footprint.In this study, we applied the common practices of Life Cycle Analysis (LCA) in the industry to the Unit Operations Laboratory, which resembles the industrial settings yet focuses on teaching and learning that may not have set production scopes nor define operation conditions and processes (i.e. for learning purposes and study impact of various factors on common chemical processes).As learning is the main objective in undergraduate laboratories, LCA methodologies related to laboratory equipment and incorporating technical information on global climate initiatives, clean energy, and the Paris Agreement need to be followed, but some modifications to make such calculations possible.To illustrate the feasibility of this approach, a case study on bioethanol production through yeast fermentation and subsequent distillation processes is employed as a proof-ofconcept.This case study serves as a platform for estimating LCA and redesigning experiments with the aim of reducing the carbon footprint.Since not all chemical process units are designed the same (i.e.sizes, power/production capacity), this project is a collaborative effort internationally amongst universities with similar equipment but different sizes.The carbon footprint approaches, and the preliminary data collected can enable fine-tuning and test the robustness of the approaches and models.The Unit Operations Laboratory emerges as a valuable platform for students to assess their carbon footprint and actively engage in practical LCA applications.This research contributes to the broader goal of embedding sustainability principles within the educational framework, fostering a generation of professionals equipped with the knowledge and skills necessary to address environmental challenges.
In this study, we introduce a novel zero-knowledge identification scheme based on the hardness of the subgroup distance problem in the Hamming metric. The proposed protocol, named Subgroup Distance Zero Knowledge Proof (SDZKP), employs a cryptographically secure pseudorandom number generator to mask secrets and utilizes a Stern-type algorithm to ensure robust security properties.
Ryan Lavin, Xuekai Liu, Hardhik Mohanty, L. E. J. Norman · 6 authors
Zero-knowledge proofs (ZKPs) enable computational integrity and privacy by allowing one party to prove the truth of a statement without revealing underlying data. Compared with alternatives such as homomorphic encryption and secure multiparty computation, ZKPs offer distinct advantages in universality and minimal trust assumptions, with applications spanning blockchain systems and confidential verification of computational tasks. This survey provides a technical overview of ZKPs with a focus on an increasingly relevant subset called zkSNARKs. Unlike prior surveys emphasizing algorithmic and theoretical aspects, we take a broader view of practical deployments and recent use cases across multiple domains including blockchain privacy, scaling, storage, and interoperability, as well as non-blockchain applications such as voting, authentication, timelocks, and machine learning. To support consistent comparison, we provide (i) a taxonomy of application areas, (ii) evaluation criteria including proof size, prover and verifier time, memory, and setup assumptions, and (iii) comparative tables summarizing key tradeoffs and representative systems. The survey also covers supporting infrastructure, including zero-knowledge virtual machines, domain-specific languages, libraries, and frameworks. While emphasizing zkSNARKs for their prevalence in deployed systems, we compare them with zkSTARKs and Bulletproofs to clarify transparency and performance tradeoffs. We conclude with future research and application directions.
In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates the sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further, we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files (greater than 50 MB) or Big data files (greater than 1 TB) require greater computational complexity, we leverage the sharding concept to enhance both space and time complexity in cloud storage. Hence, the large files are divided into shards and stored in different database servers. We also employ a blockchain smart contract to enhance secure retrieval of the file and also a secure access method, which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data’s authenticity is shared with the verifier at the destination cloud, rather than the actual data themselves. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies required to store data. Since the environment maintains the privacy of the user data and the raw data of the user is not stored anywhere, the entire environment is set up as a Zero trust model.
In this study, a new data-sharing method is proposed that uses a private InterPlanetary File System—a decentralized storage system operated within a closed network—to distribute data to external entities while making its authenticity verifiable. Among the two operational modes of IPFS, public and private, this study focuses on the method for using private IPFS. Private IPFS is not open to the general public; although it poses a risk of data tampering when distributing data to external parties, the proposed method ensures the authenticity of the received data. In particular, this method applies a type of zero-knowledge proof, namely, the Groth16 protocol of zk-SNARKs, to ensure that the data corresponds to the content identifier in a private IPFS. Moreover, the recipient’s name is embedded into the distributed data to prevent unauthorized secondary distribution. Experiments confirmed the effectiveness of the proposed method for an image data size of up to 120 × 120 pixels. In future studies, the proposed method will be applied to larger and more diverse data types.