Blockchain technology offers a decentralized ledger where all transactions are transparently recorded. While this transparency facilitates trustless verification, it also raises critical privacy concerns: large-scale data collection and analytics can link and trace user addresses, undermining the pseudonymity once considered sufficient. In this paper, we propose ZK-R-SNARK, a novel scheme that integrates Zero-Knowledge Proofs (ZK-SNARKs) with Ring Signatures to enhance transactional anonymity in public blockchain networks. Our approach utilizes ring signatures to ensure that the originating signer remains indistinguishable among a group of potential signers, while ZK-SNARK ensures succinct and non-interactive verification of the signature’s correctness. By combining these techniques, ZK-R-SNARK enables nodes to validate the authenticity of transactions without revealing any critical information about the sender’s identity or transaction details. As a result, even advanced data analytics or machine learning techniques have limited capability to cluster or link addresses over time. We implement our scheme in the Circom framework to demonstrate its feasibility and measure performance metrics such as proof generation time, verification overhead, and on-chain data size. Experimental results indicate that ZK-R-SNARK achieves robust privacy guarantees with acceptable computational costs for practical blockchain environments.
In today's world, the need for smart buildings along with smart interior design is quickly growing, requiring advanced AI-based methods for better area usage, power conservation as well as safety. Usual methods battle in continuing real-time adaptability, with several security concerns, and in sufficient data processing, making them inadequate for multidimensional architectural and interior design systems. For countering these challenges, this research introduces DesignTwin-SDN, a Digital Twin-based AI system in combination with CRL-Net, a Deep Learning (DL)-based clever model fusing CNN, ResNet-50, and LSTM for the creation of more precise spatial-temporal analysis and anomaly detection. Also, for possessing strong security, the system includes Adaptive Flow-Encrypted AES (AFEA), a dual-mode encryption strategy of combining Advanced Encryption Standard (AES) with Flow-Based Dynamic Encryption (FBDE). Furthermore, Starfish-Orangutan Optimization Algorithm (SOOA), which is a combination of the Starfish Optimization Algorithm (SFO) with Orangutan Optimization Algorithm (OOA), is proposed for the selection of the optimal secret key for purposes of maximizing that encryption efficiency. The Zero-Knowledge Proof (ZKP) system further secures authentication by precluding forbidden use in smart spaces. Experimental results show the suggested framework's effectiveness. These results show a 99.05% accuracy and a 98.51% recall, a large increase over the current method. DesignTwin-SDN merges DL and digital twins with SDN-IoT to change smart architecture and interior layout planning into more secure, efficient, and flexible systems with dynamic environments.
As the demand for e-voting grows, it has become particularly important to ensure the security and fairness of the voting system. Therefore, an e-voting system is studied and designed to ensure the non-tamperability of voting results. The system utilizes zero-knowledge proof to verify the identity of the voter, while ensuring the tamperability of the voting data through blockchain technology. The experimental results indicated that the system outperformed the existing schemes in terms of processing speed and verification efficiency. Specifically, when the number of voters was 200, the time consuming and single verification time of this system were 3.7 s and 6.4 ms, respectively. When the number of voters increased to 600, the time consuming and single verification time were 8.3 s and 13.3 ms, respectively. In the number of candidates/voters was 5/80, none of the system’s gas consumption exceeded the maximum limit of a single transaction in Ether. Among them, the gas consumption of Vote Control contract was 5577485, and the gas consumption of non-interactive zero knowledge contract was 3826753. Furthermore, the more candidates there were, the longer it took the system to operate, although the number of voters had less of an effect on the cost of operating the voter system. The above outcomes reveal that the e-voting system proposed in the study provides a secure and efficient solution for small-scale voting activities and provides a basis for future optimization of large-scale voting scenarios.
Kristián Košťál, Lukas Mastilak, Dušan Morháč, Adam Valach
This paper presents a decentralized short-term rental platform based on Ethereum and IOTA. The proposed framework aims to improve the privacy, security, and decentralization of the rental market by incorporating decentralized identity, reputation mechanisms, and escrow services. Specifically, the proposed framework utilizes Zero-Knowledge Proofs (ZKPs) to facilitate the creation of property listings and the secure exchange of funds between renters and tenants. Moreover, it employs an open-source smart contract to enhance trust between users and developers while preventing unilateral alterations to contract logic or platform fee structures. Ultimately, the presented prototype illustrates the viability of deploying a blockchain-enabled short-term rental platform by effectively incorporating essential functionalities, such as the creation and exchange of renters’ funds. This research underscores the transformative potential of blockchain technology in the accommodation rental industry. As blockchain technology continues to evolve, further advancements in decentralized governance and smart contract scalability will be instrumental in driving mainstream adoption.
In educational psychology, emphasizing the situational context is clearly ‘du jour’, becoming arguably most apparent in the renaming of Eccles' Expectancy Value Model to ‘Situated Expectancy-value Model’ (SEVT), outlined in several papers she coauthored (Eccles & Wigfield, 2020, 2024; Gladstone et al., 2022). According to Eccles and Wigfield (2024), the programmatic shift was necessary to reflect the expansion of the theory since its beginnings as a framework to explain gender differences in learning motivation and educational choices of students, to a now full-fledged socio-cognitive developmental theory. As such, the model is explicit about the recursive nature of the underlying processes and acknowledges the idiosyncratic circumstances of each behavioural moment, be it students' decision what classes to take or a teacher's decision about the feedback they give each student. While this makes a lot of sense conceptually, the new framing of the model comes with two challenges. One is of epistemological nature, related to the fact that the emphasis of the ‘situatedness’ weakens the generalizability of empirical finding to other, even very similar contexts. The second challenge lies in the translation of the expanded model to adequate empirical research strategies that reflect the new model complexity or, put more simply: How do we overcome the limitations of questionnaires as the most commonly used tool to collect data in this line of research? It feels inadequate now to pack the ‘situatedness’ in the item stem, for example, ‘When doing your math homework…’ or ‘In general, I love being a science teacher’. This might logically make the response somewhat context-specific, but situation-specific enough in the sense of SEVT. Overcoming this limitation is the common theme throughout the six papers which, each in its unique way, are pushing towards a more convincing empirical approach to illustrate and understand the relevance of the situational context and to identify aspects of it that allow us to carefully generalize findings to a similar class of situations. The latter is important as ‘situatedness’ in the SEVT model is not meant to be merely a new label for otherwise unexplained variance in an analysis that uses stable teacher and student characteristics as predictors. Instead, it suggests characterizing the context in order to integrate relevant features into a predictive model. For example, Stark, Camburn & Kaler (this volume) demonstrate that teacher motivation varies across different but typical work activities. But instead of the ‘classic approach’ to rely on item construction for a cross-sectional study (‘When I teach in the classroom…’, ‘When I interact with colleagues…’, ‘When I grade papers…’, etc.), they use the ‘day reconstruction method’ (DRM) to get not only a more valid measurement of the motivational state of teachers in a given context but also a precise account of how often teachers encounter those qualitatively different, but nevertheless typical, professional situations. It is obvious that a teacher's motivational state during actual teaching is not predictive of their long-term experience of burnout, for example, if this context represents only a small fraction of the professional contexts a teacher navigates on a daily basis. They are able to demonstrate that roughly two thirds of the variance in teacher motivation lies between periods, that is, distinct situations throughout a workday, negligible variance between days (controlling for periods) and roughly a third of the variance resides (stably) between teachers. Wang, Thompson-Lee and Klassen (this volume) combine the emphasis on ‘situatedness’ with the advances made in classroom simulations as a tool for teacher training, which is steadily moving towards the use of virtual reality as a standard tool (see Huang et al., 2023). Wang et al. demonstrate that even in the reduced complexity setting of a simulation in an online training setting, the success in adequately reacting in a set of 15 situations a teacher typically encounters on a daily basis has a consistent impact on student teachers self-efficacy beliefs and their assessment of how good they see themselves aligned with the affordances of the job. Unintentionally, the exposure to the scenarios tends to have a somewhat sobering effect since self-efficacy and career intentions trended down on average. However, one could argue that this is reflective of a more accurate self-assessment of the students regarding their readiness to be a teacher. They can take this either as a call to intensify their learning efforts or as a critical appraisal of their decision to become a teacher. As long as the 15 scenarios authentically reflect the professional life of a teacher, this study implicitly reflects the situational variability of the profession, and one is invited to speculate how this might impact a teacher's motivation in the long run. Similar to Stark, Camburn and Kaler (this volume), Bross, Frenzel and Nett (this volume) consider ‘day’ the key temporal unit of observation for a longitudinal study on teacher motivation or, in this case, the emotion regulation of teachers. Emotion regulation is strongly related to teacher motivation as successful regulation of negative emotions an important predictor of maintained teacher motivation is (Wang et al., 2023). The interesting twist in their study is the use of latent profile analysis that allows them, in addition to identifying coping patterns for two emotions in different situational settings, to reveal flexibility/consistency of teachers' emotion regulation across situations as a trait-like characteristic. Even if the authors do not discuss this explicitly, their approach introduces an interesting expansion of the SEVT model: While it is true that situations matter for the response of teachers, only some teachers actually vary in their response to negative emotions while the majority of teachers show very similar emotion regulation patterns. This could be understood as a situation by person interaction: Only 17.4% of the teacher sample used different combinations of flexibility across situations. The approach also reveals that the remaining three patterns consist of teachers who differ in their coping profiles but not across situations. This opens the door for further investigation beyond the emotion regulation research because it is conceivable that similar ‘meta patterns’, that is, stability of different patterns across situations for some teachers but not for others, exist for other motivational constructs as well. Moving to the papers that focus on the instructional process, we again see the need to resort to more complex statistical tools if ‘situatedness’ is of particular interest. Oschwald, Moeller, Kracke, Viljaranta and Dietrich (this volume) present probably the most fine-grained analysis of ‘situatedness’ in the context of motivational research to date, analysing the ‘micro-cycles’ of instructional quality on college students motivation in 9-min intervals (combining three ratings of 3 min). The basic idea was to illustrate that change/variation in the instructional clarity (detail, variation, consistency) has an immediate/short-term lag effect on student motivation. While the authors are very circumspect in considering methodological and conceptual shortcomings of their Null findings, I am more inclined to take them at face value: Motivational dispositions of students, as conceptualized in the SEVT context, are more inert than the study design implies. If this is true, it is good news for future research in the sense that it is not necessary to choose such a high-resolution (and hence expensive) research design. Most likely, a low-clarity teaching style simply does not dampen college students motivation immediately and maybe not even from 1 day to the next. However, if a teacher consistently over days and weeks teaches with low clarity, students become gradually frustrated, start to question their own competence, etc. The idea that zooming out the time-frame somewhat is corroborated by the Rubach and von Keyserlink paper (this volume) which used 5 weeks within the semester as the elapsed time to investigate longitudinal trends. The consistency of the student assessment of the quality of the instruction dominated observation specificity when the course was held constant. However, at a given time point, students rated different courses differently, suggesting that their assessment reflected substantial differences in their perception of the different courses. Also important is their finding that roughly 30% of variance is a stable difference between students who adds substantial noise to any statistical analysis that aims at identifying causal impact over time. Accordingly, Rubach and von Keyserlink acknowledge that their study is limited as it is a single source study, that is, students rated the instructional quality as well as their interest and expectations. But that consistency of instructional quality throughout the semester is a limiting factor to demonstrate ‘situatedness’ of student motivation comes from other research contexts as well, for example, the research on the often replicated ‘thin-slice-effect’ (Ambady & Rosenthal, 1993): Student evaluations at the end of the semester can be extremely well predicted by the assessment of the first 10 min of the first lecture of the semester. While this is often taken as proof of the importance of the first impression, our own (experimental) research suggests that this high correlation is mainly due to the consistency of teacher behaviour throughout the semester (Samudra et al., 2016). The first impression is a good indicator of the teaching quality for the teacher's behaviour/quality of the rest of the semester. A final course evaluation may well be more or less an accurate average of the experience throughout the semester and therefore a valid measure of instructional quality. With the caveat that student assessment and student motivation are different constructs, this observation would suggest for the Oschwald et al. study that the authors would find more robust effects if the time unit was not 9-min intervals, but daily or weekly aggregates of instructional quality. For both, the Oschwald et al. as well as the Rubach and von Keyserlink study, the measurement of instructional quality becomes a critical issue when we want to avoid artefacts of common-source bias or too short-cycled causal models. Göllner, Lazarides and Stark (this volume) make a foray into new territory by exploring the validity of large language models (LLMs) to assess teaching quality which, in the future, could eliminate the human factor in coding entirely. If a holistic semantic analysis could be able to capture relevant aspects of teaching quality reliably, human coding through expert or student assessment would become obsolete. Quality could even be assessed in real time as the teaching is still happening or shortly thereafter, opening the opportunity to use it as immediate feedback in teacher training. In a more rudimentary fashion, we used the same idea for specific teacher training purposes a decade ago. A voice-recording device (LENA) that distinguished teachers' and students' speaking turns identified in-class discourse segments the teachers were learning to use more frequently in their mathematics classes. Teachers received feedback within 24 h, and for some (not all), it was helpful for improving their teaching (Wang et al., 2014). Göllner et al.'s cutting-edge exploratory study shows that LLMs have potential in this regard, but we have still ways to go. The semantic representations are ‘sensitive enough’ to reflect variation between segments, lessons and teacher. They also were associated with human-coded quality assessment, but a ballpark 20% of shared variance is not even close to the level where the human–AI interrater reliability could reach the level of human–human reliability after efficient coder training. However, they used a zero-shot GPT model which mean that no additional information was provided to guide the semantic analysis, and the PCA-based dimensionality reduction is indicative of the exploratory nature of the approach with its inherent difficulty to interpret the dimensions and questions of replicability. However, the prompted transcript analysis is a first step towards a use of LLMs that is closer aligned with theoretical concepts and hence a promising step to the next level. After all, the LLM can identify the strength of instructional dialogue best when it can use samples of human-identified examples of dialogue that represent the quality dimension in question (multi-shot GPT). There is no doubt that LLMs will in the near future take over a lot of (if not all) coding tasks of texts and video footage. But what and how the AI codes material will always depend on theoretical considerations about student–teacher and student–student interactions and how they facilitate academic learning. The tool does not come with a guiding theory and Göllner et al.'s contribution makes that clear. In her reflections on the situative approach to research in educational psychology, Nolen (2024) points out that the situative view leads to an emphasis on understanding the processes that underlie change. This, in turn, leads to a reflection on what kind of change is to be analysed and what kind of change is considered desirable. Academic learning in educational psychology is, for the most part, conceptualized as a cumulative process, as relatively stable gains over an observed time period, adding to the prior knowledge level. Weeks or months as temporal units of analysis seem appropriate as standard in the learning context of curriculum-based schooling, unless the learning of smaller units is the focus, like learning the content of one particular mathematics lesson. In contrast, the underlying idea in the Stark, Camburn and Kaler contribution on teacher motivation is that high teacher motivation is desirable and a potential goal for interventions. Or it stimulates a teacher's self-directed action by minimizing exposure to situations that are demotivating or to change the quality of the social interactions to avoid the demotivating impact. It is not a cumulative, but rather a protective change model. At least implicitly, the self-efficacy belief of student teachers in Wang, Thompson-Lee and Klassen similarly is a variable one would wish to be and remain high, based on the normative assumption that high self-efficacy beliefs are a characteristic of a good teacher. But different from academic learning, there is a logical ceiling for self-efficacy beliefs. Therefore, it is not a cumulative change model, but an optimization model. The environment should lead teacher to—and keep them at—a ‘5 out of 5’ level of self-efficacy. While those two papers have similar underlying change models, the theory of emotion regulation in Bross, Frenzel and Nett is based on a qualitatively different conceptualization of change: homeostasis. For a teacher, anger is arguably a dysfunctional state and it is desirable to quickly and effectively regulate it down to an emotional set point if the situational trigger cannot be avoided. It is apparent that the logical temporal unit of analysis in this context is probably minutes, if the goal is to investigate the process as such. This, of course, is not the intention of the authors as their focus lies on the coping patterns of teachers across situations encountered throughout the day. The assumption is, in fact, cumulative in the sense that exposure to a lot of anger-inducing situations paired with a suboptimal coping pattern will wear teachers down in the long run and reduce their professional motivation. The intention of the Oschwald et al. study was to demonstrate that instructional clarity has an immediate positive impact on college students learning motivation—again not as a cumulative model but with the normative goal to reach and maintain a high level of learning motivation. At least implicitly, the assumption is that a somewhat consistent lack of clarity over a longer period of time, that is, not 9 min but several weeks of low-clarity instruction, will wear a student's learning motivation down. Even if the short-term lag effect could not be shown, the long-term effect might still—and it is likely to—exist. The measurement used in the Rubach and von Keyserlink study is Likert-scale based, which means that it comes with a maximal value despite the fact that theoretically, at least, interest is logically unlimited and could therefore follow a cumulative model. If the quality of the instruction is extremely high every week I am in class, my interest might continuously grow until the end of the term. I might reach the scale's ceiling, but that would be an artefact of the measurement scale. Why are these considerations important? They identify the epistemological challenge of an overly situation-focused perspective. While it might be relevant in some research contexts to understand features of the situation and not treat it as error variance (Nolen, 2024), we will still need to transcend the insights gained from these analyses to a more general level in order to be of educational relevance. At least for the run-of-the-mill K-12 schooling context, it would be difficult to drop the traditional positivistic rationale when we consider the practical relevance of our research: Once causal mechanisms are identified as tentative truths, they are of practical relevance only if they show long-term impact on academic learning and psychosocial development across a fairly broad class of situational contexts. The more specific the context is defined in the research, the more limited the practical implications. For example, it might be of psychological interest to demonstrate that a student's academic self-concept dips down after 20 instances of unclear instruction. But if the teacher simply was underprepared on that day and otherwise presented the material clearly and accessibly throughout the semester, treating this as a random ‘error’ is probably justified. When long-term development is the main focus of our research (here motivation), the minute-to-minute fluctuations in the clarity of instructions are unlikely to be important. The reason is that, in the back of our heads, we have a model of how motivation affects learning. A student who is—more or less—stably interested in the content of the class will be more likely to work happily on assignments, etc. in the evening and on weekends. As a general rule (non-situative), research has shown that unclear instruction has a negative impact on self-concept and interest in the long run, and we assume that this is true for a broad array of situations, student characteristics, grade levels, etc. This is why it is reasonable that teacher training works with student teachers on instructional clarity as a skill set. If well, across a of situations and contexts a teacher will Even if we that every situation is different and mechanisms are ‘situatedness’ cannot mean that educational psychology, as an of the long-term developmental that are the of educational It means to be more to of the learning environment that are necessary for the impact of on learning means to acknowledge the of but this becomes a only if the goal is to identify situational characteristics that allow The Stark, Camburn and Kaler paper a for this idea because their the teachers themselves to identify situations and their over time and how they in those contexts. While every with other teachers might be different from the they as a features that with other situations in the daily professional for example, actual instruction in the on the situations we on a daily basis to be a good point to ‘situatedness’ into a research that does not of the focus of our process of learning in
Xin Liu, Xinyuan Guo, Dan Luo, Liang Li · 9 authors
Federated learning promotes the development of cross-domain intelligent applications under the premise of protecting data privacy, but there are still problems of sensitive parameter information leakage of multi-party data temporal alignment and resource scheduling process, and traditional symmetric encryption schemes suffer from low efficiency and poor security. To this end, in this paper, based on the modified NTRU-type multi-key fully homomorphic encryption scheme, an asymmetric algorithm, a secure computation scheme of multi-party least common multiple and greatest common divisor without full set under the semi-honest model is proposed. Participants strictly follow the established process. Nevertheless, considering that malicious participants may engage in poisoning attacks such as tampering with or uploading incorrect data to disrupt the protocol process and cause incorrect results, a scheme against malicious spoofing is further proposed, which resists malicious spoofing behaviors and not all malicious attacks, to verify the correctness of input parameters or data through hash functions and zero-knowledge proof, ensuring it can run safely and stably. Experimental results show that our semi-honest model scheme improves the efficiency by 39.5% and 45.6% compared to similar schemes under different parameter conditions, and it is able to efficiently process small and medium-sized data in real time under high bandwidth; although there is an average time increase of 1.39 s, the anti-malicious spoofing scheme takes into account both security and efficiency, achieving the design expectations.
Yao Ma, Wen Yu Kon, J. O. Chu, Kevin Han Yong Loh · 6 authors
Identity verification is the process of confirming an individual's claimed identity, which is essential in sectors like finance, healthcare, and online services to ensure security and prevent fraud. However, current password/PIN-based identity solutions are susceptible to phishing or skimming attacks, where malicious intermediaries attempt to steal credentials using fake identification portals. Alikhani et al. [Nature, 2021] began exploring identity verification through graph coloring-based relativistic zero-knowledge proofs (RZKPs), a key cryptographic primitive that enables a prover to demonstrate knowledge of secret credentials to a verifier without disclosing any information about the secret. Our work advances this field and addresses unresolved issues: From an engineering perspective, we relax further the relativistic constraints from 60m to 30m, and significantly enhance the stability and scalability of the experimental demonstration of the 2-prover graph coloring-based RZKP protocol for near-term use cases. At the same time, for long-term security against entangled malicious provers, we propose a modified protocol with comparable computation and communication costs, we establish an upper bound on the soundness parameter for this modified protocol. On the other hand, we extend the two-prover, two-verifier setup to a three-prover configuration, demonstrating the security of such relativistic protocols against entangled malicious provers.
B S Rajeshwari, Shamanth K Murthy, S M, S P Shashank
Cross-Chain interoperability is one of the critical challenges in Blockchain technology, which enables seamless communication and asset transfers between different chains. Existing interoperability solutions such as atomic swaps, relay chains, notary schemes and blockchain bridges, often face security vulnerabilities, scalability limitations, and trust assumptions that hinder their efficiency. Zero-Knowledge Proofs (ZKPs) offer a promising approach to enhancing the security and privacy of interoperability mechanisms by enabling verification of computations without revealing the transaction details. This research paper explores existing Cross-Chain interoperability solutions, including the problem they address, such as atomicity, privacy, and decentralization, their implementation or methodology used and their drawbacks that need to be further optimized. Furthermore, the paper explores the integration of ZKPs in interoperability protocols, evaluating their potential to achieve trustless and privacy-preserving crosschain communication and conclude with the outline of future research directions to improve and optimize the current limitations and develop better solutions.
Ovaj rad istražuje teorijske temelje i praktičnu primjenu dokaza nultog znanja u blockchain sustavima, s fokusom na Polygon zkEVM blockchain. Analiziraju se zk-SNARK i zk-STARK sustavi dokazivanja te njihova implementacija u ZK-rollup rješenjima za poboljšanje skalabilnosti blockchain mreža. Teorijska analiza pokazuje kako dokazi nultog znanja omogućavaju verifikaciju transakcija bez otkrivanja osjetljivih podataka, čime se adresiraju izazovi privatnosti i skalabilnosti. Praktični dio uključuje implementaciju decentralizirane aplikacije za glasovanje na Polygon zkEVM Cardona Testnet mreži, demonstrirajući primjenu tehnologije u realnoj situaciji. Analiza transakcijskih podataka potvrđuje značajne uštede goriva kroz batch procesiranje transakcija u odnosu na direktno izvršavanje na Ethereum glavnom lancu. Rad identificira ključne prednosti i ograničenja trenutnih implementacija te predlaže smjerove za buduća istraživanja u području post-kvantne kriptografije i hardverske akceleracije.
The traditional voting process, whether paper-based or electronic, is often criticized for its lack of transparency, susceptibility to fraud, and dependence on centralized authorities. Blockchain technology, particularly in the Web3 ecosystem, provides a decentralized, secure, and tamper-proof solution for digital voting. This paper explores how blockchain can enhance election integrity by leveraging decentralized applications (DApps), smart contracts, and cryptographic security. The proposed system employs Ethereum-based smart contracts to automate vote casting and tallying while ensuring voter privacy through zero-knowledge proofs. Decentralized Identity (DID) is integrated for secure authentication, preventing double voting and identity fraud. The paper discusses system architecture, security considerations, scalability challenges, and real-world applications of blockchain voting, highlighting how Web3 can transform democratic elections.
Sarthak Singh Rawat, Rajeev Mohan Sharma, Brian J. Peter, Mohammad Wazid · 6 authors
Secure, verifiable, and transparent elections are the most crucial to democratic procedures. Blockchain technology can be utilized as a potential solution for tamper-resistant and decentralized voting schemes. The utilization of blockchain technology in e-voting systems is of significant interest because of its capacity to improve transparency, security, and integrity in digital voting. However, privacy and voter anonymity are imperative concerns. This paper presents a novel blockchain voting system that preserves vote integrity and voter anonymity by using zero-knowledge proofs (ZKPs). In this paper, we propose the system architecture, cryptographic primitives, implementation strategies, and compare security and performance to conventional e-voting systems. The new paradigm preserves voter anonymity, correctness of votes, and ensures end-to-end verifiability. Further, the practical implementation of the proposed scheme is provided to measure the performance of important parameter, like, total votes cast, total gas used, number of mined blocks, transactions per seconds, etc.
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Henrique Hepp, Murilo V. G. da Silva, Leandro M. Zatesko
The complexity class of the problems that can be solved by a quantum algorithm in a non-adaptive collapse-free model is called naCQP. This class was introduced in 2016 by Aaronson et al. intended to be a slightly larger class than BQP: larger enough to include important NP-intermediate candidate problems, but likely not to include NP-complete problems. Aaronson et al. (2016) showed that there is an oracle A for which NPA ⊈ naCQPA; and Hepp et al. (2025) showed that relative to an oracle A chosen uniformly at random, (UP ∩ coUP)A ⊈ naCQPA with probability 1, being UP ∩ coUP a subclass of NP. Amongst the NP-intermediate candidate problems in naCQP is the entire class SZK, of the problems that admit a statistical zero-knowledge interactive proof system. The relation between QSZK, which is the class of the problems that admit a quantum zero-knowledge interactive proof system, and naCQP is unknown, with some believing that there is an oracle A for which QSZKA ⊈ naCQPA. A promise problem complete for QSZK is the trace distance distinguishability of mixed quantum states. We show that this problem, when restricted to pure quantum states, is in naCQP.
The breakneck pace of digital transformation in sectors around the world have driven developments in cybersecurity, AI and cloud technology. But with great progress comes great responsibility, and with generating such evolution it gives rise to lots of issues when it comes to data privacy, system to system connectivity, leveraging knowledge and infrastructure scalability. This paper provides an integrated solution that can be harnessed to secure, operate and make digital ecosystems more agile, by amalgamating present day practices and technologies that many organizations face in their current environments across security, operation and agility when it comes to digitalization. It covers proactive cybersecurity approaches like DevSecOps and Zero Trust Architecture, AI based intelligent threat analysis and real-time automation, and cloud-native and edge computing models for scalable and resilient infrastructure. The study at the same time showcases advancements in data processing and encryption, legal compliance, providing enterprises with a roadmap toward safer, AI-infused and cloud supported infrastructure. By bringing these columns together, the research offers strategic recommendations for businesses wishing to future-proof their digital business as they negotiate an ever more volatile and risk-filled technology environment.
Open access
Economic and Technological Systems Analysis
Advanced Research in Systems and Signal Processing
The secure management of Electronic Health Records (EHRs) in a cloud environment poses many challenges, and guaranteeing the scalability of a secure solution to manage the huge amounts of data and its privacy remains an open problem. Although the existing encryption methods offer strong security, the widespread adoption of asymmetric-key protocols is limited because of the lack of computational efficiency compared with practical applications. That is, the efficiency of computational time required for an encryption or decryption step, and the privacy-preserving verification of the computation result, are not balanced due to the volume of the data. In order to overcome the drawbacks of existing encryption methods, we propose a novel approach to integrating Zero-Knowledge Proofs (ZKPs) with a Multi-Layer Merkle Tree (MLMT) to achieve a scalable, privacy-preserving, guarantee-of-integrity and publicly-verifiable solution for managing the EHR while ensuring patients’ privacy. It proposes the utilization of MLMT to build a hierarchical data verification structure for massive data, significantly improving the computational efficiency. It also employs ZKP to enable verifier to verify the validity of data without revealing any record information, which is vital for the data management in the healthcare sector. The authors compared the proposed model with existing approaches which adopt AES-256 encryption and typical Merkle Tree-based solutions and demonstrate its superior scalability and privacy-preserving ability while ensuring controllable computational overhead. The results demonstrate that ML MMT ZKP provides the best balance between privacy, integrity and scalability reaching lower overheads and shorter verification times than other traditional approaches. This work constitutes a step forward in the development of cryptographic solutions for EHRs and provides a framework for real-time verifiable information in the healthcare domain.
The cryptographic protocol developments are transforming digital trust is the capacity to verify without revealing any underlying information. Traditional authentication and authorization systems are usually prone to leakage of sensitive data, resulting in compromise of privacy and low scalability in distributed systems. The root of these problems is eliminated through the so-called zero-knowledge techniques that allow demonstrating to one party ownership of some information without exposing it. This paper explores the origin and development of zero-knowledge protocols in light of its efficiency, trustless design, and privacy focus to illustrate why the application is worth the hype. Particular attention is paid to such structures as zk-SNARKs, zk-STARKs, and bulletproofs, as well as their application to constructing transparent, scalable systems. Blockchain aptitudes used anywhere in confidentiality of transactions, decentralized identity systems allow a self-sovereign identity without exaggerating personal information, and the healthcare and finance industries enjoy the ability to share information securely without any effect on compliance aspects. The next discussion points are implementations, the scalability issue, cryptographic assumptions, and integration issues. This survey outlines evaluations of deployments from 2021 to 2025 to determine the following top benefits, barriers, and trends in building systems that safeguard privacy without compromising their performance or trust to the client. Future requirement conclusions provide some insights about future requirements in terms of efficient construction of proofs, standardizations, and ease of usability to expand the adoption of infrastructures built on zero-knowledge into a constantly more integrated digital world.
Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks. Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements. Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures. To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method. Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure. Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16 KB or more, outperforming state-of-the-art methods.
Preet Deepankumar Vyas, Devang Dhorada, Kevin Bhanderi, Akshaybhai J. Patel · 9 authors
In order to protect the ocean ecosystem, the pursuit of sustainable and self-powered photodetectors is critical for revolutionizing underwater optical communication (UOC) used for environmental hazard sensing. This step enables energy-efficient and real-time detection of marine ecosystem threats such as chemical contamination, oil spill, and eutrophication. Although layered metal dichalcogenides (LMDCs) with exceptional optoelectronic properties and chemical stability are the most suitable materials, their integration into UOC technology remains largely unexplored. To address this, the present study demonstrates and evaluates seawater-immersed photoelectrochemical photodetectors (PEC-PDs) based on SnSe<sub>2</sub>, an emerging member from the LMDC family. Direct vapor transport-grown SnSe<sub>2</sub> is well characterized in its thin-film form by X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, atomic force microscopy, Raman spectroscopy, and PL spectroscopy, followed by utilization as photoelectrodes in the PEC-PD devices. Fabricated PEC-PDs exhibit a responsivity of 505.74 ± 4.65 μA/W at zero bias and 10.34 ± 0.16 mA/W at 0.4 V bias; they outperform conventional Na<sub>2</sub>SO<sub>4</sub>-based devices by 21-fold and 82-fold, respectively. To the best of our knowledge, this is the first report presenting an SnSe<sub>2</sub>-based PEC-PD utilizing seawater electrolyte and its performance evaluation. A proof-of-concept UOC demonstration of the present study paves the way toward the next-generation green optoelectronic devices for self-sustainable marine technologies.
Santiago Germino, Martín N. Menéndez, Ariel Lutenberg
Essential infrastructure and services depend on critical systems. To ensure that critical systems function properly, regular testing and monitoring are necessary. Establishing direct, dedicated data connections for remote testing can be expensive, while using public cellular, satellite, or fiber Internet connections can introduce privacy and security risks. Securing the medium often requires placing trust in third parties. The novel proposal introduced in this work suggests using zero-knowledge proofs, a modern cryptographic technique, to conduct secure remote testing and monitoring of critical systems over affordable public networks, which can include email or instant messaging apps. This approach guarantees both the integrity and confidentiality of the transmitted data, as well as the integrity of the processes involved in preparing the data for transmission. We will present this approach and demonstrate its implementation through a real-world use case: the remote testing of an electronic railway interlocking system.
Существующие типовые модели аутентификации с использованием цифровых удостоверений носят абстрактный характер. Для конкретизации модели аутентификации с использованием цифровых удостоверений предлагаются: алгоритм эмиссии цифровых удостоверений; алгоритм аутентификации на основе доказательства с нулевым разглашением. Производится количественная оценка раскрытых данных удостоверений в результате: предложенного алгоритма аутентификации на основе доказательства с нулевым разглашением; аутентификации с полным раскрытием атрибутов; аутентификации с частичным раскрытием атрибутов. Полученные результаты оценок анализируются и делаются соответствующие выводы. Existing standard authentication models using digital credentials tend to be abstract. To refine the authentication model using digital credentials, the following are proposed: a digital credential issuance algorithm and an authentication algorithm based on zero-knowledge proof. A quantitative assessment is conducted on the amount of disclosed credential data resulting from the proposed zero-knowledge proof-based authentication algorithm, authentication with full attribute disclosure, and authentication with partial attribute disclosure. The assessment results are analyzed, and relevant conclusions are drawn.
Account-based anonymous blockchain systems can provide robust privacy protection for users. However, they become highly inefficient when handling high-frequency micro-payment scenarios. This paper presents systematic optimizations for batch processing and micro-payment transactions in account-based anonymous blockchain systems to enhance both privacy and efficiency. Building on BlockMaze, the first account-based anonymous blockchain system fully protecting transaction privacy, we propose innovations in batch transfers, batch receipts, and micro-payment handling. By reducing redundant data, improving circuit design, and optimizing zk-SNARK proof generation, we achieve up to 55.90% and 23.02% reductions in overall time consumption for batch transfers and receipts, respectively, significantly cutting computational cost and memory use. For micro-payments, a solution encapsulating the payment deadline reduces transaction delays and fund freezing. Experimental results show only slight increases in proof generation time—1.41 seconds for transfers and 1.02 seconds for payments—while maintaining privacy protection. This research lays a foundation for practical applications of account-based anonymous blockchain systems, enhancing privacy, processing efficiency, and transferability to other systems. • Optimized batch processing and improve transaction efficiency in account-based anonymous blockchain systems. • Optimized circuit design reduces redundant data and shortens zero-knowledge proof times. • Time consumption decreased by up to 55.90% in batch transfer function and 23.02% in batch receipt function. • Highly transferable to other account-based anonymous blockchain systems, offering strong flexibility and application potential. • Offers future research directions to improve blockchain efficiency and privacy protection.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques