Blockchain is a shared digital ledger that, once transactions are recorded and verified, cannot be altered. Since most blockchain nodes maintain a copy of the ledger, blockchain brings the benefit of decentralization but also introduces privacy concerns. This paper explores the privacy issues associated with blockchain transaction data, primarily using zero-knowledge proof technology to design a privacy protection scheme. This scheme utilizes Pedersen commitments and Bulletproofs zero-knowledge proof technology to prevent the disclosure of transaction information while ensuring the correctness and legality of transaction data. Security analysis of the scheme demonstrates that it effectively addresses problems such as double-spending, payment authorization, and dishonest miners, thereby enhancing blockchain performance while protecting the privacy of blockchain transaction data.
In this paper, we study steady flows in data streams, which refers to the flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch-based algorithm, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the memory requirement by 75%. Our theoretical proofs show that the negative impact of the reborn technique is small. Experimental results show that, compared with the two comparison schemes, SteadySketch improves the Precision Rate (PR) by around 79.5% and 82.8%, and reduces the Average Relative Error (ARE) by around$905.9\times $and$657.9\times $, respectively. Finally, we provide three concrete cases: cache prefetch, Redis and P4 implementation. As we will demonstrate, SteadySketch can effectively improve the cache hit ratio while achieving satisfying performance on both Redis and Tofino switches. All related codes of SteadySketch are available at GitHub.
In the contemporary landscape of the battlefield, the enhanced cyber warfare capabilities of adversaries mandate that routing protocols are equipped with endogenous security features. These features must be capable of detecting threats promptly and addressing them effectively to ensure both security and control. They also provide secure configuration options for the intrinsic security architecture of communication nodes within communication systems. This paper introduces a novel, inherently secure identity verification system named ORCA, which utilizes the concept of noninteractive zero-knowledge proof. ORCA streamlines the process of node identity authentication by minimizing communication exchanges, thereby safeguarding network topology integrity and establishing trust upon network formation. Leveraging this framework, the ORCA-OLSR security protocol was developed. Comparative simulations on the OPNET platform between the ORCA-OLSR and the challenge-response mechanism-based CA-OLSR security protocol demonstrate that ORCA-OLSR outperforms its counterpart in metrics such as routing overhead, end-to-end delay, and routing load.
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
With the growing need for privacy and self-sovereign identity, traditional identity management relying on centralized data registries not only represents single points of failure but also lacks transparency and control over users' identity information. With the built-in tamper-proofness and transparency, blockchain has been widely studied to accommodate the challenges in traditional identity management. Still, it usually comes with privacy concerns due to its public accessibility. Anonymous credentials take advantage of the recent progress in zero-knowledge proof, allowing the unlinkable presentation of only the necessary attributes for a service to guarantee anonymity. However, the existing anonymous credentials require a secondary issuer to verify and manage the anonymized credentials, which compromises the overall transparency and causes indirect management of the user's identity. In this paper, we propose GrAC, a blockchain-based identity management system based on a novel identity graph, which allows users and identity providers to securely store and manage identity information on the blockchain without intermediate entities. GrAC also includes an anonymous authentication protocol suite based on zero-knowledge proof, allowing users to generate one-time anonymous credentials that selectively reveal minimal information to the service provider for authentication. The analysis and evaluations show that Gr Achas a reasonable overhead and provides adequate anonymity protection while removing the need for intermediate issuers.
George Misiakoulis, Harris Niavis, StĂŠphane KĂźndig, Konstantinos Loupos
E-voting systems often face risks such as data breaches, vote manipulation, and lack of voter confidence. Balancing security and anonymity has posed significant obstacles that obscured the immense potential of electronic voting systems. This paper addresses critical challenges in e-voting, including security vulnerabilities, lack of transparency, scalability and user accessibility issues. We propose a privacy-preserving framework to tackle these challenges, enhancing the security, transparency and scalability of e-voting systems. Our framework leverages blockchain to provide a tamper-evident ledger, zero-knowledge proofs to ensure ballot secrecy and data integrity and Merkle Trees to facilitate data storage in a scalable manner. Furthermore, we present findings of the framework's performance that was conducted under an e-voting use case, while we suggest improvements towards an even more secure and transparent framework.
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Jan Kalbantner, Konstantinos Markantonakis, Darren Hurley-Smith, Carlton Shepherd
In the realm of Distributed Ledger Technology, privacy and regulatory challenges loom large for marketplaces. Regulation requires to conduct Know Your Customer (KYC) procedures to verify the identity of participants, while privacy concerns necessitate the protection of personal data. Current approaches to KYC are inefficient and are potentially even harmful to privacy due to centralization and data exposure. This paper proposes a zero-knowledge proof enabled KYC scheme, utilizing Soulbound Tokens (SBT) to create a discreet, compliant, and secure KYC process. We present a privacy-preserving mechanism that shares only essential information while adhering to Self-Sovereign Identity (SSI) principles, placing users in the full control of their data. The proposed scheme further introduces the usage of SBTs for reputation to incentivize good conduct and build trust within marketplaces.
The zero knowledge succinct non-interactive argu-ment of knowledge (zkSNARK) systems necessitate the generation of a common reference string (CRS) via a trusted setup that traditionally involves a trusted third party (TTP). An alternative method, employing multiparty computation (MPC), facilitates the creation of an updatable structured reference string (SRS) without the need for a TTP, ensuring security provided at least one participant remains honest. We introduce a secure way to generate CRS without any trust assumption for participants. We introduce a novel platform designed to enable participants to autonomously update the CRS at their discretion. This platform transforms the CRS into a non-fungible token (NFT), enhancing the transparency and traceability of the CRS's revision history. Our platform incorporates a protocol for the generation and utilization of the proving and verification keys, integral to the zkSNARK scheme, based on the updated CRS. Implementation of this platform on the Ethereum blockchain has demonstrated secure CRS update mechanisms and the low overhead of CRS transformation.
Prefix summation has found its applications over the years in various important domains from sorting to geographical terrain analysis. In our work, we address the privacy-preserving prefix summation problem on blockchain where multiple parties aim to aggregate their secret values through a secure multi-party computation. For the given problem, we propose a novel crypto-graphic protocol (i.e. PRFX) by integrating zero-knowledge proof and hypercube network topology. In addition, we theoretically show the applicability of the proposed protocol on the privacy-preserving delegation using Euler Tour Technique. We analyze the protocol with respect to the scalability perspectives including the communication, computation and storage overheads. The proposed protocol is also exposed to the experimental study where its performance is measured through the blockchain gas cost, the zero-knowledge proof generation time and lastly the zero-knowledge proof size.
Anh-Tu Hoang, Can Umut Ileri, William B. Sanders, Stefan Schulte
Self-Sovereign Identity (SSI) empowers holders to control their Verifiable Credentials (VCs) and demonstrate their compliance with verifiers' conditions by sending VCs to the verifiers. State-of-the-art SSI solutions from both industry (e.g., Polygon ID) and academia (e.g., zk-creds) protect holders' privacy by reducing the necessity to transmit these VCs which contain personal data. However, the solutions limit verifiers from expressing and verifying multiple conditions efficiently. This paper introduces zkSSI, which enables verifiers to ex-press complex requirements by combining multiple conditions using logical predicates and nested conditions. zkSSI utilizes zk-SNARKs to generate single or multiple zero-knowledge proofs, validating compliance with verifiers' requirements both on-chain and off-chain. Formal proofs are provided to show the privacy and security guarantees of zkSSI, and experimental results demonstrate its efficiency for both holders and verifiers, even when using consumer-grade hardware.
We present a dispute resolution protocol that can be built on the Ethereum blockchain. Unlike existing applications, it is private by design through the use of zero-knowledge proofs. The protocol is resilient to Sybil attacks and provides increased collusion resistance amongst participating parties. A resolution to the dispute is guaranteed, whilst ensuring the users have the final say on the chosen resolution. The proposed protocol rewards stakeholders through a social incentive mechanism leveraging Soulbound tokens, and rewards agents who behave honestly, as opposed to favouring the wealthy. To our knowledge, this is one of the first dispute resolution protocols to implement governance through reputation as opposed to token-based voting. Furthermore, it is fully viable, given that all its necessary components are currently operating on the Ethereum blockchain.
Elmira Ebrahimi, Michael Sober, Anh-Tu Hoang, Can Umut Ileri ¡ 6 authors
Federated learning is a distributed learning technique that enables parties to train a model collaboratively without disclosing their local data. To this end, a centralized aggregator collects local models from participating parties and aggregates them to form a global model. However, if parties are malicious, this approach is susceptible to security threats such as model poisoning and global aggregation attacks. Blockchain-based federated learning has been introduced as an alternative to the centralized aggregator to aggregate local models directly on the blockchain. However, employing blockchain-based solutions introduces challenges, including high computational costs and privacy concerns. To mitigate such challenges, this paper leverages zero-knowledge proofs (i.e., zk-SNARKs) to guarantee the privacy and verifiability of local model training and global model aggregation. The proposed framework verifies the local and global models' computational correctness without storing and revealing them on the blockchain. We evaluate our framework by utilizing a real-life dataset, with experimental results demonstrating its viability regarding both computational costs and learning model performance.
Hangchao Ding, Huayun Tang, Jia Chen, Yanzhao Wang
As a privacy-preserving technique, Secure Multi-Party Computation (SMPC) has been extensively applied in Neural Networks (NN) privacy-preserving schemes, including secret sharing, homomorphic encryption, and zero-knowledge proof. Federated Learning (FL) is applied to train models from scattered data, which can protect the client's data. Therefore, the leakage of gradient in Federated Learning has been a risk of privacy and security. We construct a post-quantum privacy-preserving cryptography algorithm, in which gradient can be protected by SMPC techniques. Non-interactive zero-knowledge proof, Shamir secret sharing, and homomorphic encryption techniques are applied to guarantee the security of gradient transmission. L WE-based Kyber key exchange protocol is also applied to be resistant to quantum attack.
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 âInference about causation from examination of familial confoundingâ (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 individualâs outcome on her own exposure, then on her twinâs exposure, and then finally on her own exposure and her twinâs 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 âprovided causal evidence for numerous exposure and outcomesâ, 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 individualâs 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 Yâ , SX is the set of family-constant factors that influence X but not Yâ , and SY is the set of family-constant factors that influence Y but not Xâ . 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 Yâ , as proposed by Li et al1 Note that, in Models 1â3, the intercepts Îąâ , ιⲠand Îąâł are generally different, as well as the coefficients βself and βselfâ˛â , and the coefficients βco-twin and βco-twinâ˛â . Note also that, since X and Y are available for both twins in each pair, each twin contributes as both âselfâ and âco-twinâ 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 ĎX and ĎY are the within-pair correlations in X and Yâ , 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 βselfâ˛=βself and βco-twinâ˛=0â , 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 SXYâ , 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) âIf there is familial confounding only ⌠(Figure 1a), there will be associations between⌠Yself and Xco-twin (â βco-twinâ , Model 2). Adjusting for Xself (Model 3), there will still be a conditional association between Yself and Xco-twin (â βco-twinâ˛â ), but it will be attenuated towards the null compared with βco-twinâ. This conclusion is not generally valid when Uself is present, since adjusting for Xself then opens the path YselfâUselfâXselfâSXâXco-twin at which Xself is a collider, which may inflate the association between Yself and Xco-twinâ . As another example, they wrote: âIf there is a causal effect from X to Y only (Figure 1b), ⌠the conditional [on Xself] association between Yself and Xco-twin (â βco-twinâ˛â ) will be nullâŚâ 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-twinâ . 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 âFor simplicity we assume that ⌠there was no within-individual confoundingâ (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 âXco-twin is theoretically unrelated to unmeasured confounders specific to an individual only [italics added]â. 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 âselfâ and âco-twinâ 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 âIf 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-twinâthe path through SY is closed due to the fact that Yco-twin is conditioned onâ. Presumably, they here referred to the path YselfâSYâYco-twinâXco-twinâ , 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 YselfâSYâYco-twinâUco-twinâXco-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): ââŚthe 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 madeâ. 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=0.81â , βco-twin=0.73â , βselfâ˛=0.73 and βco-twinâ˛=0.15â . The pattern of these coefficients is a mixture of âFamilial confoundingâ and âX causes Yâ in Table 1, with the attenuation from βco-twin to βco-twinⲠbeing stronger than the attenuation from βself to βselfâ˛â . Li et al1 concluded that these results are â⌠consistent with a longitudinal causation, as well as a small amount of familial confoundingâ. 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: XselfâSXYâYself and XselfâUselfâYselfâ , whereas Xco-twin and Yself are only associated though the first path, which could explain why βself> βco-twinâ . When conditioning on Xco-twinâ , the path XselfâSXâXco-twinâSXYâYself becomes open, which could explain why βself>βâ˛selfâ . Similarly, when conditioning on Xselfâ , the path Xco-twinâSXâXselfâUself âYself becomes open, which could explain why βco-twin>βâ˛co-twinâ . 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 âanalogousâ to MR, and further claimed in their key messages that a benefit over MR would be that ICE FALCON âdoes not make strong assumptionsâ. 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 âICE 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 exposuresâ. 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 1â3. 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-twinâ , 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-twinâ ) is the presence of familial confounders SXYâ . They also carried out a simulation, which indicated that the degree of familial confounding may often lie between βco-twin and βâ˛co-twinâ , 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 SjĂślander 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.
Multiple internet services rely on the DiffieâHellman (DH) algorithm for security. In spite of this, a study from October 2015 reveals that many DH-based internet applications are not protected adequately from highly resourced attackers, including the security services of some nations. To improve the security of data while it is in transit over an unsecured network, a novel method for encrypting and decrypting voice signals is presented in this research. The DiffieâHellman algorithm, a specific form of asymmetric key cryptography, is the basis of the presented technique. This method&s;s primary value is that it lets users encrypt and decode their conversations using an encrypted session key. To begin, the dispatcher uses the agreed-upon secret key to encrypt the incoming speech signal using this technique. Second, the encrypted voice signal is transmitted through a public network to reach its final destination. To begin, we implemented a client-side encryption system to better protect sensitive information while still allowing for secure communication between client and server. Key exchange is protected from man-in-the-middle and discrete logarithm attacks with the help of the modified DiffieâHellman method and from unauthorized access with the help of the modified Zero Knowledge Proof (ZKP) method. Criteria for evaluation include file size, time to generate keys, time to encrypt and decode data, and time to execute the algorithm.
Ammar Almomani, Ahmad AlâQerem, Mohammad Al Khaldy, Mohammad Alauthman ¡ 6 authors
This chapter analyzes the most sophisticated cryptography methods which are used to protect blockchain-based cryptocurrency transactions against attacks by botnets. First, a background will explain blockchain, cryptocurrencies, and threat space for bots is going to sing in. Core cryptographic building blocks such as hash functions and digital signatures are outlined. The chapter furthermore outlines methods to confront botnet threats in blockchain ecosystems, including two processes to sign the user charting, a sign policy standard, code signing, attestation, multi-party computation for private transactions, and zero-knowledge proofs. Finally, we present some obstacles and ideas for research along the way. Conclusively, the systematic incorporation of cryptographic defenses would be the providing layer of security as well as the guarantee of the integrity of cryptocurrency transactions. Continual improvements in scalable cryptographic algorithms and their implication in blockchain platforms will make secure mechanisms against botnets possible.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Amjad Aldweesh, Mohammad Alauthman, Ahmad AlâQerem, Abdelraouf Ishtaiwi ¡ 6 authors
Blockchain's decentralized trust models are spurring rapid advances in applied cryptography. This chapter explores the accelerating convergence between cryptographic innovations and next-gen blockchain platforms. The authors provide background on modern cryptography and the distributed ledger tech behind blockchains. They then analyze how blockchain architectures are driving progress in zero-knowledge proofs, digital signatures, and secure multiparty computation. In turn, these advancing cryptographic mechanisms enable critical blockchain capabilities around privacy, scalability, accountability, and governance. The authors highlight gaps in existing cryptography-blockchain integration and present post-quantum research directions that fulfill decentralized ledgers' unique security needs. Their work frames a symbiotic blockchain-cryptography ecosystem where progress in both fields builds on each other to overcome limitations. This interdisciplinary synergy will catalyze more secure, functional, and efficient decentralized technologies.
Zero Knowledge Proofs (ZKPs), cryptographic protocols that allow a party to authenticate a transaction to another without disclosing additional information beyond the authenticity of the transaction, continue to have a significant impact on privacy, security, and integrity in applications. It addresses constraints such as computing costs, trust dimensions, and integration complexity, and proposes possible methods and techniques for future research. It emphasizes the importance of ZKP for improving privacy and security in digital systems highlights, the article emphasizes the importance of continuous innovation and further development of their standardization efforts. Proofs (ZKPs) have emerged as a powerful tool in cryptography, offering innovative solutions to privacy, security, and authentication challenges. This article provides an in-depth review of ZKPs, exploring their progress, challenges and future prospects in cryptography. It examines the basic concepts of ZKPs, their applications, and their impact on cryptographic protocols.
Authentication is a key technology that provides trusts in cyberspace. Anonymous credentials are aimed at providing user authentication and protecting users' privacy meanwhile. Existing anonymous credential protocols mostly rely on specific hardware to protect their credential secrets, the inconvenience of which and the fact that losses or damages of the hardware can make their identities unavailable significantly hamper large-scale deployments of these schemes. In this paper, based on Zhang et al.'s password-based credential techniques (NDSS'20), combined with cryptographic primitives such as non-interactive zero knowledge proofs and homomorphic encryption, we propose a secure and portable anonymous credential protocol which does not need tamper-resistant hardware. Our anonymous credentials are designed to be encrypted using passwords which can be implemented and deployed in software only in the user terminal. The structure of our (encrypted) credentials and the design of our verifier-designated authentication tokens ensure the resistance of our protocol against serious offline dictionary attacks. We further balance security and user privacy, and propose a concept of maximum authentication failure time limit combined with hash computation and zero-knowledge proofs against online dictionary attacks, which are more difficult to defend against in the anonymous authentication setting. Our protocol supports not only blind issuance of anonymous credentials but also anonymous authentication of users. From the perspective of the dominating exponentiation computations in anonymous credential protocols, our anonymous authentication protocol outperforms most existing schemes except the ones that do not provide blind issuance.
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