Igor Calzada, Géza Németh, Mohammed Salah Al-Radhi
As generative AI (GenAI) technologies proliferate, ensuring trust and transparency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms—blockchain, decentralized autonomous organizations (DAOs), and data cooperatives—as foundational tools for enhancing trust in GenAI. These mechanisms are analyzed within the framework of the EU’s AI Act and the Draghi Report, focusing on their potential to support content authenticity, community-driven verification, and data sovereignty. Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI-generated misinformation. Specifically, as a result of this analysis, it identifies and evaluates seven detection techniques of trust stemming from the action research conducted in the Horizon Europe lighthouse project called Enfield: (i) federated learning for decentralized AI detection, (ii) blockchain-based provenance tracking, (iii) Zero-Knowledge Proofs for content authentication, (iv) DAOs for crowdsourced verification, (v) AI-powered digital watermarking, (vi) explainable AI (XAI) for content detection, and (vii) Privacy-Preserving Machine Learning (PPML). By leveraging these approaches, the framework strengthens AI governance through peer-to-peer (P2P) structures while addressing the socio-political challenges of AI-driven misinformation. Ultimately, this research contributes to the development of resilient democratic systems in an era of increasing technopolitical polarization.
Saad Mutlaq Alluhaydan, Mohammed Obaid Alshammari, Yousef Jazaa Obaid Alshmilan, Ahmed Hamoud Alshammari · 12 authors
Background: The rise of AI health assistants and digital tools raises concerns about data security and consent management. Traditional systems are prone to failures and provide limited transparency in data sharing. Blockchain technology offers a decentralized, immutable, and secure solution to these issues. Aim: This narrative review critically examines the real-world implementations and security trade-offs of blockchain technology when applied specifically to health assistant audit trails and consent management, moving beyond theoretical propositions. Methods: A systematic search of peer-reviewed literature (2010-2024) was conducted across Scopus, IEEE Xplore, PubMed, and ACM Digital Library. Implementation case studies, prototypes, and theoretical frameworks were analyzed to assess technical architectures, performance metrics, and security evaluations. Results: Findings indicate an emerging landscape where blockchain proves useful for creating secure audit logs in AI decision-making and dynamic consent models using smart contracts. However, challenges persist, including performance and scalability issues, key management complexities, data linkage risks, and conflicts between immutability and regulatory requirements such as the GDPR's "right to be forgotten." Conclusion: Blockchain serves as a foundational layer to improve security and transparency in health assistant ecosystems. Its future potential relies on hybrid architectures, advanced cryptographic methods such as zero-knowledge proofs, and an awareness of the new security and operational challenges that arise. It is not merely a database but a comprehensive solution for integrity and control.
Blockchain-based voting systems have emerged as a promising solution to address issues of transparency, immutability, and trust in electoral processes. However, ensuring the security and privacy of votes remains a major challenge. This paper explores the integration of advanced cryptographic protocols, including zero-knowledge proofs, homomorphic encryption, and secure multi-party computation, to safeguard voter privacy and system integrity in blockchain voting architectures. We analyze the strengths and limitations of these protocols and propose a hybrid cryptographic framework designed to enhance security, verifiability, and scalability. A comparative analysis using simulation data illustrates the trade-offs between security guarantees and computational efficiency. Our findings underscore the critical role of cryptography in enabling trustworthy, transparent, and tamper-resistant blockchain voting systems suitable for modern democratic processes.
With the exponential growth of e-commerce and digital financial services, ensuring privacy in online transactions has become a critical concern. Cryptographic techniques offer robust solutions to protect sensitive user data, maintain confidentiality, and prevent unauthorized access during online financial exchanges. This paper reviews key cryptographic mechanisms such as end-to-end encryption, zero-knowledge proofs, homomorphic encryption, and secure multi-party computation that enhance privacy in online transactions. Additionally, it discusses the challenges in implementing these solutions at scale and their implications for regulatory compliance and user trust. The study concludes by outlining future research directions and best practices to balance privacy with usability in online financial ecosystems.
Federated Learning has emerged as a promising paradigm for collaborative machine learning while preserving data privacy. Federated Learning is a technique that enables a large number of users to jointly learn a shared machine learning model, managed by a centralized server while training data remains on user devices. In recent years, along with the blooming of Machine Learning (ML)-based applications and services, ensuring data privacy and security has become a critical obligation. ML-based service providers are not only confronted with difficulties in collecting and managing data across heterogeneous sources but also challenges of complying with rigorous data protection regulations such as the General Data Protection Regulation (GDPR) Federated Learning is very important to reduce data privacy risks. Federated Learning is a scheme in which several consumers work collectively to unravel machine learning problems, with a dominant collector synchronizing the procedure. This paper reviews recent advancements in privacy-preserving techniques for federated learning from a machine-learning perspective. This paper investigates the potential of Federated Learning for privacy-preserving machine learning in domains like healthcare, finance and IOT, where data privacy is paramount. We explore existing techniques to enhance privacy, including differential privacy, secure aggregation, homomorphic encryption, federated learning with encrypted, meta-learning, machine learning, privacy-preserving techniques, blockchain technology, decentralized learning, federated averaging, data privacy, searchable encryption and zero-knowledge proofs. This paper concludes with future research directions to address ongoing challenges & further enhance the effectiveness & scalability of privacy-preserving federated learning.
Zero-knowledge proof (ZKP) is a widely used privacy-preserving technology, where multiscalar multiplication (MSM) accounts for over 70% of the computational workload. The acceleration of MSM can enhance the overall performance of ZKP, making it a focal point of community attention. However, in practical applications involving the deployment of multiple MSM accelerators, existing designs often overlook strategies for optimizing bandwidth and area efficiency. To address this, we propose Myosotis, an efficiently pipelined and parameterized MSM architecture. By sharing input data and allocating cache effectively, it mitigates average transmission bandwidth in runtime. Myosotis also supports the use of multiple point addition (PADD) units to achieve performance gains, balancing area overhead and latency for improved area efficiency. Different parameter selection enables a tradeoff between the performance, area, and bandwidth of the MSM accelerator. When benchmarking with MSM degrees between$2^{18}$and$2^{26}$, our proposed baseline design achieves up to$3.32\times $and$6.72\times $speedups over state-of-the-art FPGA and ASIC designs. Compared to the baseline, Myosotis with two window MSMs and one PADD unit reduces bandwidth demand by 43% while maintaining similar area and latency. On the other hand, Myosotis with three window MSMs and two PADD units decreases latency by 43% and bandwidth by 17%, with only a 9% area increase.
The need for robust authentication mechanisms has become paramount in an era marked by increasing concerns over data privacy and cybersecurity. Traditional methods, such as passwords and biometrics, often need to improve in providing adequate security without compromising user privacy. In response, Zero-Knowledge Proofs (ZKPs) have emerged as a solution, allowing parties to verify information […]
Oleksandr Kuznetsov, Alex Rusnak, Anton Yezhov, Kateryna Kuznetsova · 6 authors
This chapter delves into the pivotal role of zero-knowledge proofs (ZKPs) in bolstering the realms of metaverse environments and blockchain technology, with a special emphasis on their integration into wireless communication systems. By combining theoretical insights with empirical evidence, the chapter elucidates the transformative impact of ZKPs on enhancing privacy, security, and operational efficiency in these digital ecosystems. It commences with a review of the current literature, laying the groundwork for a comprehensive understanding of ZKPs. The discourse progresses into a detailed examination of both interactive and non-interactive ZKPs, including an in-depth mathematical exposition of zk-SNARKs (zero-knowledge succinct non-interactive arguments of knowledge) and zk-STARKs (zero-knowledge scalable transparent arguments of knowledge). This foundational analysis sets the stage for investigating ZKPs' practical applications within metaverse environments, scrutinizing their influence on user authentication, secure asset transactions, and privacy-preserving data sharing. Such applications underscore ZKPs' capacity to revolutionize virtual world experiences by fortifying security and privacy. Moreover, the narrative extends into the blockchain domain, spotlighting the inadequacies of conventional data verification methods, with a focus on Ethereum's reliance on Merkle Trees. It introduces a novel ZKP-based methodology that mitigates these limitations, markedly diminishing proof size and computational demands. The efficacy of this strategy is corroborated by experimental outcomes, and its economic viability is highlighted, advocating for its real-world applicability in blockchain systems. Additionally, this chapter integrates a discussion on the application of the proposed methods to metaverse wireless communications, underscoring the criticality of ZKPs in satisfying the book's scope. It concludes by synthesizing the insights garnered and contemplating future directions, thereby enriching the scholarly dialogue and offering valuable perspectives for advancing research and development in blockchain and metaverse technologies, particularly within wireless communication frameworks.
Olumide Kumuyi, Esther Uzoka, Bisola Akeju, David Excel Ozowara
The Framework for Privacy-Focused Digital Identity Verification Supporting Financial Inclusion in Africa proposes an integrated, secure, and ethically aligned model for digital identification systems that enhance access to financial services while safeguarding individual privacy. The framework addresses the dual challenge of expanding digital financial inclusion across Africa’s underserved populations and maintaining trust through data protection and regulatory compliance. It emphasizes privacy-preserving technologies such as federated identity management, zero-knowledge proofs, and biometric encryption to authenticate users without disclosing sensitive personal information. By enabling decentralized and consent-based data sharing, the model ensures individuals retain ownership of their digital identities while allowing financial institutions to verify eligibility and compliance with Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations. The framework also integrates blockchain-based audit trails for transparent verification processes and tamper-proof recordkeeping, enhancing institutional accountability. It adopts interoperable standards to link national ID systems, mobile network operators, and fintech platforms, enabling seamless cross-border transactions and inclusive participation in the digital economy. A multilayer governance structure encompassing regulators, financial service providers, and civil society stakeholders promotes ethical oversight and equitable access. Furthermore, the framework supports context-sensitive deployment, accommodating infrastructural disparities and socio-cultural factors unique to African regions. It aligns with global data protection norms such as the General Data Protection Regulation (GDPR) and the African Union Convention on Cyber Security and Personal Data Protection (Malabo Convention), while encouraging local innovation in identity ecosystems. Ultimately, this privacy-centered digital identity verification framework establishes a resilient foundation for secure inclusion, reducing barriers for the unbanked, mitigating identity fraud, and fostering digital trust. By combining privacy engineering, inclusive design, and interoperable governance, it contributes to the broader agenda of sustainable digital transformation and equitable financial empowerment across Africa.
As blockchain technologies increasingly underpin significant economic and social interactions, the need for advanced security mechanisms to protect data exchanged across these decentralized networks becomes crucial. This paper introduces a novel cryptographic scheme designed specifically for blockchain ecosystems to ensure the security, integrity, and confidentiality of data transactions. Our proposed scheme leverages a combination of homomorphic encryption and zero-knowledge proofs, integrated seamlessly with blockchain's inherent properties, such as decentralization and immutability.
Xin Wang, Li Jiaqian, Ding Xueshuang, H. Zhang · 5 authors
The problem of data privacy protection in the information age deserves people’s attention. As a distributed machine learning technology, federated learning can effectively solve the problem of privacy security and data silos. Differential privacy(DP) technology is applied in federated learning(FL). By adding noise to raw data and model parameters, it can further enhance the degree of data privacy protection. Over the years, differential privacy technology based on federated learning framework has been developed, which is divided into central differential privacy federated learning(CDPFL) and local differential privacy federated learning(LDPFL). Although differential privacy may reduce the accuracy and convergence of federated learning models while protecting data privacy, researchers have proposed a variety of optimization methods to balance privacy protection and model performance. This paper comprehensively expounds the research status of differential privacy techniques based on the federated learning framework, first providing detailed introductions to federated learning and differential privacy technologies, and then summarizing the development status of two types of federated learning differential privacy(DPFL) techniques respectively; for CDPFL, the paper divides the discussion into first proposal of CDP and typical application examples, the impact of Gaussian mechanisms on model accuracy, optimization based on asynchronous differential privacy, and insights from other scholars; for LDPFL, the paper divides the discussion into first proposal of LDP and typical application examples, processing multidimensional data and improving model accuracy, existing methods and optimization for reducing communication costs, balancing privacy protection and data usability, LDPFL based on the Shuffle model, and insights from other scholars; following this, the paper addresses and summarizes the unique challenges introduced by incorporating differential privacy into federated learning and proposes solutions; finally, based on a summary of existing optimization techniques, the paper outlines future directions and specifically discusses three research ideas for enhancing the optimization effects of federated differential privacy: advanced optimization strategies combining Bayesian methods and the Alternating Direction Method of Multipliers (ADMM), integrating lattice homomorphic encryption techniques from cryptography to achieve more efficient differential privacy protection in federated learning, and exploring the application of zero-knowledge proof techniques in federated learning for privacy protection.
The article defines negotiation as the use of information, time, and power to influence outcomes. It underscores the importance of negotiation in both personal and professional settings, highlighting strategies like rhetoric, logic, and non-verbal communication. Various negotiation types, such as distributive (win-lose), integrative (win-win), and rational (objective-based) are discussed. The authors stress the importance of mutual benefit, the psychology of reciprocity, and ethical considerations in negotiations. Finally, the article outlines negotiation tactics and strategies, such as the "YES...BUT" tactic and stress-inducing techniques, while emphasizing that negotiation should be a deliberate choice based on one's comfort and needs. The ultimate message is that learning to negotiate effectively can significantly improve the quality of life. KEYWORDS: effective communication, ethics, manipulation techniques, morality, mutual benefit (WIN-WIN), negotiation, power and influence, social and business J.E.L CLASSIFICATION: E6, E64, E71, F62, G15, H12 1. INTRODUCTION Human beings are highly interactive socially. Talking to each other gives people an exceptionally quick, clear, and thorough way to get to know and form relationships with each other. The real world is a huge bargaining table, and whether we like it or not, we are all participants. We all come into conflict with others, such as family members, business agents, competitors, or government entities. How we approach these encounters can determine not only whether we will prosper, but whether we will enjoy a satisfying, enjoyable, and fulfilling life. Negotiation is an area of knowledge and effort that focuses on winning the favor of people from whom we want certain things. Traditionally, it is assumed that those with the greatest talent, dedication and education are rewarded. But life has disappointed those who claim that virtue and hard work will ultimately triumph. The "winners" seem to be the people who are not only competent, but also have the ability to "negotiate" how to get what they want (Vasile Tran, Irina Stănciulescu, 2001). 2. WHAT IS NEGOTIATION? In every negotiation we are involved in, in every negotiation in the world, from a geopolitical diplomatic negotiation to buying a house, three crucial elements are always present: information, time and power. Everyone's ability to negotiate determines whether we can influence the environment or not. It's about analyzing information, time and power to influence behaviour, satisfy needs and make things happen the way we want them to. What is negotiation? It is the use of information and power to affect behavior in a "warp of tension". If we think about this broad definition, we realize that we actually negotiate all the time, both at work and in our personal lives. Against whom do we use information and power to affect their behavior outside of the job? Husbands negotiate with wives and wives with husbands. We use information and power in addition to friends and relatives. Negotiations can take place with a traffic policeman ready to give us a fine, with a store that does not want to accept our personal check, with a landlord who does not provide essential services or wants to double the rent, with a merchant who wants to cheat us. We can negotiate with customers, bankers, vendors, suppliers. We negotiate more often than we realize. That's why it's important to learn to do it better, more efficiently and thus improve the quality of our lives, at work or outside of it. Regardless of where and between whom they are conducted, negotiations call on rhetoric, logic, and elements of argumentation theory. Sometimes effective communication and manipulation techniques are used. Notions such as offer, request, position, claim, objection, compromise, concession, argument, transaction, argumentation, evidence, etc., can frequently intervene in the negotiation process. At the same time, non-verbal communication elements, such as physiognomy, facial expressions, gestures, posture, clothing, general appearance can have an importance that should not be neglected. The culture of the partners and the bargaining power of the negotiating parties are other elements that must be taken into account. Elements of tactics and strategy, rhetorical traps and tricks, as well as knowledge of the psychology of perception, can play a decisive role in obtaining large advantages in exchange for small concessions. In the contemporary business world, negotiation and the negotiator acquire considerable importance. Never in history have commercial transactions been more numerous and conducted at higher values. For the manufacturer, importer or wholesale distributor, a good negotiator can do in three hours what ten or a hundred contractors do in a few weeks or months. A weak negotiator can lose just as much. A margin of a few percent on the price, the warranty period, the delivery conditions and shipping, at the payment term or a margin of a few percent on commission or interest, always remain negotiable. In large transactions, in the industrial market, where contracts worth billions are negotiated, this negotiable margin can amount to tens or hundreds of millions. From the position of each of the parties, they can be lost or won. Negotiation is a talent, an innate grace, but also a skill acquired through experience, training and learning. The job of negotiator is an elite one, in business, in diplomacy, in politics. Broadly speaking, negotiation appears as a focused and interactive form of interpersonal communication in which two or more disagreeing parties seek to reach an agreement that solves a common problem or achieves a common goal. The understanding of the parties may be a simple verbal agreement. Consolidated by a handshake, it may be a tacit consent or a letter of intent or a protocol, convention or contract, drawn up following common procedures and usages; it can also mean an armistice, an international pact or treaty, drawn up in compliance with special procedures and customs. In relation to the area of interest in which negotiations are carried out, we can distinguish between several specific forms of negotiation. By negotiation we understand any form of unarmed confrontation, through which two or more parties with contradictory but complementary interests and positions aim to reach a mutually beneficial commitment whose terms are not known from the beginning (Ștefan Prutianu, Communication and negotiation in business). In this confrontation, mainly and loyally, arguments and proofs are brought, claims and objections are formulated, concessions and compromises are made to avoid both the breakdown of relations and open conflict. Negotiation enables the creation, maintenance or development of an interpersonal or social relationship in general, as well as a business, work or diplomatic relationship in particular. It should also be mentioned that negotiations do not necessarily always aim at results manifested in the direction of an agreement. Often they are carried for their collateral effects such as: maintaining the contract, buying time, preventing the deterioration of the conflict situation. Apart from these, negotiators' meetings can be seen as a potential channel for urgent communications in crisis situations. The absence of communication can be considered as an alarming sign of the impossibility of carrying out the negotiation; its presence is an indication of the chances that negotiation will occur. At the same time, we must pay sufficient attention to the climate of discretion and thorough gradual construction. As long as the negotiation is conducted with the conscious and deliberate participation of the parties who seek together a solution to a common problem, the approach involves a certain ethics and principle. Mutual benefit (WIN-WIN) In principle, in negotiations, each side adjusts its claims and revises the initial objectives. Thus, in one or more successive rounds, the final agreement is built, which represents a satisfactory compromise for all parties: the negotiation therefore works according to the principle of mutual advantage. According to this principle, the agreement is good when all negotiating parties have something to gain and none to lose. Everyone can achieve victory, without anyone being defeated. The important thing is that when all parties win, they all support the chosen solution and abide by the agreement. The principle of mutual advantage (WIN-WIN) does not exclude, however, the fact that the advantages obtained by one of the parties may be greater or smaller than the advantages obtained by the other or the other parties in negotiations. I use it often. In the psychology of communication, there is a so-called psychological law of reciprocity, a law according to which if someone gives or takes something, the partner will automatically feel the desire to give or take something else in return. Even if we don't actually give something in return, we are still left with the feeling that we owe, that we should give. Following the subtle action of this psychological law, any form of negotiation is governed by the principle of compensatory actions. The consequence is reciprocity of concessions, objections, threats, reprisals, etc. The Latin expressions of this principle are: "Do ut des" and "Facio ut facio". In Romanian, the principle can be found in expressions like: "I give if you give", "I do if you do", "If you give more, you leave me too" or "If you make concessions, I will do it too", "If you raise demands , I will also pick up" etc. 3. MORALITY AND LEGALITY The law is the law and most respect it even beyond the principles. To avoid unpleasantness, the morality of commercial deals, where the law does not appear, often remains a matter of principle, of deontology. Strict adherence to this principle is not really possible. The control of communication ethics is relative. The legal aspects of transactions are an exception, but also from this point of view, in international negotiations, the parties must agree from the start on the rules of commercial law that they will respect. When these differ from one country to another, each party tries to remain under the legal rules of its country. This fact can generate conflicting situations, which can be overcome by adopting the norms of commercial law and international customs. The fine art of negotiation is actually not new. Two of the greatest negotiators in history lived about two thousand years ago. Neither was part of any institution of their time, neither had official authority. However, they both exercised their power. Both men dressed poorly and went about asking questions, and thereby gathering information, the one in the form of syllogisms, the sense of mastery over their situation. Each of them chose their place and manner of death. Yet by death they both gained the devotion of disciples on the face of the earth. In fact, many of us try to live our daily lives according to our values. It is about Jesus and Socrates. They were ethical negotiators, followers of the win-win theory, and they were people of power. There are several fundamental types of negotiation. The real world is a huge bargaining table, and whether we like it or not, we are all participants. Analyzing the type of negotiation we are engaging in is always important. To know and evaluate him already means to predict in broad terms the behavior that the partner will adopt and to prepare his own behavior in response. In this way, the risk of a rupture to conclude a disadvantageous agreement decreases. The specialized literature distinguishes between three fundamental types of negotiation: Distributive negotiation is either/or type, which opts between victory/defeat. It corresponds to a zero-sum game and takes the form of a transaction in which it is not possible for one party to win without the other party losing. Every concession made to the partner is detrimental to the grantor and to each other. In this perspective, negotiation pits two adversaries with opposing interests against each other and becomes a confrontation of forces, in which one of the parties must win. Any concession appears as a sign of weakness. Any successful attack appears as a token of strength. The object of the negotiation will be an agreement that will not take into account the interests of the partner and which will be all the better the harder it hits the opposing party. The negotiation tactics and techniques used in distributive negotiation are typical for resolving conflict situations. They are hard and tense. Among the usual tactics, we can mention: the polemic carried out by permanent counters and by systematic deviation from the subject; assault by force; intimidation; rhetorical maneuvers based on dissimulation, masking intentions, hiding intentions, hiding the truth and blaming the opponent; disqualification for bad faith, personal attack and disparagement. This type of negotiation is possible when the opposition of interests is strong and the imbalance of forces is significant. Another negotiation tactic is the integrative (win/win) one in which the partner's aspirations and interests are respected, even if they go against their own. It is based on mutual respect and tolerance of differences in aspirations and opinions. The advantages of this type of negotiation are that it leads to better, more sustainable solutions, the parties feel better, and the relations between the parties are strengthened. Both win and both support the settlement and agreement reached. Integrative negotiation creates, saves and strengthens long-term human and business relationships. It causes each of the negotiating parties to modify their objectives and adjust their demands in order to resolve their common interests. This approach to negotiation circumvents and avoids conflict situations. The climate of the negotiations is characterized by trust and optimism, and the agreement, once reached, has every chance of being respected. Specific tactics are based on reciprocity of concessions (shorter delivery times against immediate parties, for example). Another negotiation tactic is the rational one, in which the parties do not only aim to make or obtain concessions, consents from subjective negotiating positions, but try to resolve substantive disputes from an objective position, other than the position of one or the other among them. For this, mutual interests must be clearly defined within a framework of total transparency and sincerity, without resorting to the slightest dissimulation or suspicion. It starts with formulating the problems that need to be solved, with answers to questions like: What's not working? Where is the evil? How does this manifest itself? What are the facts that contradict the desired situation? It continues with a diagnosis of the existing situation, insisting on the causes that prevent solving the problems. Then, the theoretical solutions are sought and the measures by which at least some of them can be put into practice are determined by common agreement. The algorithm of rationality therefore means: defining problems; diagnosis of causes; searching for solutions. The negotiator seeks to understand the partner's stake, to know his feelings, motivations and concerns. Divergences that remain unresolved are regulated by recourse to objective criteria, as well as scientific references, legal norms, moral norms or by recourse to the offices of a neutral arbitrator. Anticipating the margin of negotiation is essential. Any start of negotiation requires the definition of objectives. They give us a sense of direction, a definition of what we plan to achieve, and a sense of accomplishment once they have been achieved. 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Identification, authentication, and authorization processes can be conducted in various ways. Particular attention is given to the processes implemented within the self-sovereign identity paradigm. This paper analyses the processes from a data leak perspective. A comparison is made between self-sovereign identity and a centralized identity provider scheme. An overview of the relevant implementations for these processes is provided: in both the self-sovereign and non-sovereign paradigms. It has been found that, from the data leaks perspective, the self-sovereign identity scheme could only provide superior security if zero-knowledge proof technology is applied.
The integration of Blockchain technology into Internet of Things (IoT) ecosystems has emerged as a transformative approach to address critical security and privacy challenges in connected devices. This chapter explores the innovative convergence of Blockchain and IoT, focusing on the enhancement of data security, privacy, and trust through decentralized solutions. By leveraging the immutable and transparent nature of Blockchain, IoT networks can achieve secure, efficient, and automated data exchanges. The chapter delves into key Blockchain trends that are shaping IoT security, including decentralized identity management, smart contracts, and consensus mechanisms, while addressing the specific challenges of resource-constrained IoT devices. The use of advanced cryptographic techniques such as encryption, Zero-Knowledge Proofs, and homomorphic encryption ensures that sensitive data remains private and secure. The application of Blockchain in facilitating secure data sharing, device authentication, and access control was discussed, alongside the potential of permissioned blockchains to balance privacy with scalability. The chapter concludes by identifying the future directions for Blockchain and IoT innovations, emphasizing their role in the evolution of secure, autonomous, and scalable connected environments.
This research provides a detailed analysis of multi-factor authentication (MFA) in Zero-Trust Architecture (ZTA).It focused the discussion on current practices and critical challenges encountered, sharing some insights into the future direction by finding "gaps.""The field of Cyber security is a constantly changing environment.From the beginning of "trust but verify," it has gradually changed to "always verify, never trust."In this case, MFA becomes a key and effective measure to enhance confidentiality in ZTA.ZTA requires that all entities within the system must verify their identities on an ongoing basis, often using MFA.With the widespread use of telecommuting, cloud services, and the Internet of Things, the demand for identity authentication is also increasing.The MFA uses multiple authentication steps to enhance security and trust in the system.However, implementing and applying MFA in the ZTA environment has not been smooth sailing.Some schemes directly affect the popularity of MFA in their implementation, such as poor user experience, complex integration, and poor scalability.The author first reviewed some of the existing MFA programs to get to the root cause and try to fix the problem.By analyzing these typical cases, best practices are found, and strategies for improvement are proposed.The aim is to promote a balance between ease of use and security in MFA.Finally, through literature review and case studies, as well as the exploration of emerging technologies such as adaptive MFA and zero-knowledge proof, The author explore some new approaches to improve the ease and efficiency of MFA in ZTA systems.
Password-based authentication is widely applied in Internet of Things (IoT). It allows IoT devices to identify users with passwords to resist unauthorized access. However, choices of weak passwords, especially popular ones, might violate users’ privacy and lead to large-scale network attacks. Collection of popular passwords among IoT devices to establish blocklists via a service provider can prevent use of weak passwords. To protect unpopular passwords during collection, existing privacy-preserving schemes rely on expensive cryptographic primitives (e.g., garbled circuits and zero-knowledge proofs), which would impose heavy communication and computation burdens on constrained devices and hinder wide deployment of these schemes. In this paper, we propose EAGER+, an efficient privacy-preserving scheme for weak password collection in IoT against perpetual leakage. EAGER+ is mainly built on secret sharing and symmetric encryption, thereby enabling lightweight computation and communication on IoT devices. In EAGER+, we conceive a password-locked encryption with conditional decryption mechanism to efficiently identify popular passwords, where a password is essentially locked under itself in the encryption to guarantee its security, and the password can be revealed from the ciphertext by the service provider only if a sufficient number of devices exploit it. The mechanism is integrated with a servers-aided password-hardening mechanism to resist offline dictionary guessing attacks. Moreover, EAGER+ uses a key renewal mechanism to periodically update secrets for password hardening on key servers to thwart perpetual leakage towards the secrets. We formally analyze the security of EAGER+, and conduct experimental evaluations to show that EAGER+ is more efficient than existing schemes.
Mingwei Zeng, Jie Cui, Qingyang Zhang, Hong Zhong · 5 authors
The rapid evolution of the Industrial Internet of Things (IIoT) has necessitated increased device interactions across various management domains. This entails devices from different domains collaborating on the same production task. This poses significant challenges for the dynamics of cross-domain authentication schemes. Traditional cross-domain authentication schemes struggle to support seamless switching between domains and face difficulties when accommodating devices that join and leave the same domain. Moreover, these schemes suffer from intricate interactions and suboptimal efficiency. To address these issues, we propose a dynamic group signature scheme based on a dynamic accumulator and a non-interactive zero-knowledge proof. We integrated this scheme with blockchain technology to construct an efficient revocation cross-domain authentication scheme. The proposed scheme enables cross-domain anonymous authentication with simple interactions and provides an efficient revocation function for illegal devices. This approach ensures conditional privacy-preserving and enables efficient member joining and exiting through a dynamic accumulator. It effectively addresses the dynamic requirements of devices involved in IIoT production and manufacturing processes. We prove the security of the proposed scheme using a random Oracle model and conduct thorough analyses to verify its resistance against various attacks. Furthermore, the experimental results demonstrate that the proposed scheme achieves better performance in terms of computational and communication costs.
ABSTRACT The burgeoning demand for blockchain technology in diverse sectors requires advanced optimization methods to improve the performance, security and privacy. However, today common blockchain mechanisms are effected by problems like suboptimal miner selection processes, susceptibility to abnormal transactions and types of attacks affecting non‐negligible parts of the ecosystem, performance bottlenecks and so forth, rendering them far from scalability and real‐world usage. This paper addresses the problem, by introducing a set of sophisticated methods that solve recent issues and enhances the robustness, scalability, confidentiality in blockchain networks. Firstly, we present “DeepMiner”, a deep learning‐based solution that leverages historical blockchain data samples to infer optimal miner nodes. This method improves the block generation efficiency by optimizing miner node selection in real‐time, which is an essential addition to traditional random or otherwise static methods for selecting miners. Secondly, “AnoBlock” which uses anomaly detection model to detect fraud in blockchain transactions using the statistical methods like Gaussian mixture models and isolation forests. Thirdly, “OptiChain” uses data analytics to dynamically optimize blockchain performance by continuously evaluating live network metrics and the transaction throughout. Lastly, “PrivyChain” which uses privacy preservation techniques such as zero‐knowledge proofs and homomorphic encryption to achieve transaction confidentiality while retaining blockchain transparency. Their solution addresses these issues with a dual approach to protect any sensitive transaction details from being leaked and make it feasible for computations over encrypted data, the result of which aligns blockchain technology with stringent privacy standards.
Complex-valued double-sideband direct detection (DD) can reconstruct the optical field and achieve a high electrical spectral efficiency (ESE) comparable to that of a coherent homodyne receiver, and DD does not require a costly local oscillator laser. However, a fundamental question remains if there is an optimal DD receiver structure with the simplest design to approach the performance of the coherent homodyne detection. This study derives the optimal DD receiver structure with an optimal transfer function to recover a quadrature amplitude modulation (QAM) signal with a near-zero guard band at the central frequency of the signal. We derive the theoretical ESE limit for various detection schemes by invoking Shannon’s formula. Our proposed scheme is closest to coherent homodyne detection in terms of the theoretical ESE limit. By leveraging a WaveShaper to construct the optimal transfer function, we conduct a proof-of-concept experiment to transmit a net 228.85-Gb/s 64-QAM signal over an 80-km single-mode fiber with a net ESE of 8.76 b/s/Hz. To the best of our knowledge, this study reports the highest net ESE per polarization per wavelength for DD transmission beyond 40-km single-mode fiber. For a comprehensive metric, denoted as 2<sup>ESE</sup>×Reach, we achieve the highest 2<sup>ESE</sup>×Reach per polarization per wavelength for DD transmission.
Federated learning is a widely used method for collaborative machine learning without sharing local data. In this approach, participants train models using their local data, and the model updates are aggregated into a global model. However, ensuring trustworthy model training is crucial because malicious participants may not use their actual local data or may not train the model as intended, which makes it challenging to guarantee the authenticity of the data and the integrity of the model training. To address these issues, we propose a trustworthy model training scheme (TMT-FL) with verifiable authenticity and integrity. Specifically, we leverage zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) based proofs to verify the integrity of the training execution. To deal with the performance bottleneck in generating zk-SNARK proofs, we use the Chinese Remainder Theorem to optimize the convolution operation, and present an improved zk-SNARK based proof generating scheme which significantly reduces the online proving time. Besides, we adopt matrix commitment along with bloom filter to ensure the authenticity and integrity of the training datasets. Extensive experimental results demonstrate that our improved zk-SNARK scheme performs nearly$3.1\times$faster than the state-of-the-art in online proving time. Moreover, we experimentally confirm the efficiency of TMT-FL under diverse datasets in terms of computational costs, storage costs, and communication overheads.
One revolutionary way to tackle privacy and security issues in federated learning (FL) is to include blockchain technology and zero-knowledge proofs (ZK) into machine learning frameworks. To strengthen FL's defences against threats such as model poisoning attacks, this work investigates the use of ZK proofs. This study presents a new technique that uses secure multi-party computation (MPC) to efficiently detect poisoned models, addressing the shortcomings of previous ZK systems. Data anonymization, encryption of sensitive information, and encoding of categorical data all contribute to the proposed model's privacy-preserving features. Adding a privacy-protecting layer is an integral part of ML model integration. ZK circuits employ ZK-SNARKs or Bulletproofs to generate proofs that the ML model may use to predict without disclosing the data. ZK-SNARKs are trusted, and request validation and data access rules control proof access.