This conceptual modelling research investigates infrastructure designs supporting decentralized social media leveraging blockchain verified identities and non-fungible token (NFT) facilitated content transactions.Diagrammatic analysis following the object-oriented methodology depicts economic and software mechanisms allowing participant monetization of original posts through cryptocurrency micropayments and resale royalties.Systematic ownership protections aim to foster trust and transparency deactivated in conventional models exploiting uncompensated user data.While limitations persist in underlying platform maturity, regulatory uncertainty, and mass adoption prerequisites, envisioned participatory architectures offer renewed pathways to reconcile open exchange with personal agency and value.
Federated learning (FL) represents an advanced approach to tackling the issues linked with training machine learning (ML) models using distributed data while upholding privacy and security. It functions by enabling collaborative model training across a network of edge devices or servers, all without the need to transfer raw data. In place of sending data to a central server, which could potentially compromise privacy, federated learning empowers individual devices to conduct local training on their respective data. These updates are subsequently combined to develop an enhanced global model over multiple iteration. Additionally, as artificial intelligence (AI) becomes pervasive in novel application areas, concerns about the privacy of data and users are on the rise. This article offers an in-depth analysis of the advancements in FL, covering a wide array of topics including methodologies, applications, and challenges. By sidestepping the need to transfer raw data and instead focusing on sharing model updates or gradients, FL ensures the preservation of privacy and the efficient utilization of resources. Additionally, we investigate the diverse spectrum of application domains where FL holds significance. Instances encompass healthcare, finance, agriculture, education, Internet of Things (IoT), and industrial processes, all benefiting from the capacity of federated learning to harness data from decentralized sources without compromising data security. This article addresses complications such as model diversity, Non-IID (independent and identically distributed) data distribution, communication complexities, and security vulnerabilities. Furthermore, we discuss considerations related to regulatory compliance and ethics within the context of federated learning, particularly as data privacy regulations intensify.
Open access
Privacy-Preserving Technologies in Data
Privacy, Security, and Data Protection
Artificial Intelligence in Healthcare and Education
Septovan Dwi Suputra Saian, Irwan Sembiring, Daniel Manongga
The Indonesian population reached 270,20 million in 2020. Each resident is equipped with various secret identities. The COVID-19 pandemic has made all activities use technology as a basis, causing residents' identities to be stored digitally. Some applications that keep these identities experience data leaks. However, with the advent of Web3 and its emphasis on decentralization through blockchain, a new era of secure data management is possible. Blockchain, with its inherent security features, ensures that data stored is secure, difficult to damage or lose due to mutual consensus. Every transaction is recorded, making it easy to carry out the audit process. Therefore, this research will design and implement prototype dApps for secure population management, leveraging the superior security of blockchain technology. The initial stage of research is to conduct a literature study. Furthermore, it is to create designs such as system, infrastructure, and activity diagrams. Then do the development of the dApps prototype. The last is testing using OWASP ZAP and cost analysis. A dApps prototype was implemented on a blockchain. Every transaction is recorded and publicly viewable through the Etherscan platform. Other data stored on a blockchain have gone through an AES-256 encryption process with the data owner's account key so that the owner can only see the data. The results of the tests performed show that there is no high-level warning. The cost analysis results show that the most used costs are when deploying smart contracts and making new data. For further development, it is implementing permissionless blockchain and multi-accounts.
This paper explores the dynamics between value-added services, intermediary brands, and consumer privacy concerns in shaping attitudes toward blockchain-enabled consumer services. Grounded in the Antecedents-Privacy Concerns-Outcomes (APCO) framework, we develop a theoretical model that we test in three experimental studies with a total of 1613 participants, utilizing verbal scenarios featuring blockchain applications for international money transfer and hotel booking. Our research reveals that complete disintermediation via pure peer-to-peer blockchain transactions is unlikely. Consumers prefer blockchain applications offering supplementary services like call centers, password assistance, and cancellation options. As consumers become familiar with blockchain technology, privacy concerns intensify due to its distributed and immutable storage. The fears of data breaches are more pronounced when blockchain applications are offered by unknown startups as opposed to well-known Big Tech companies. However, privacy-conscious consumers also value the prospect of distancing themselves from big-data ecosystems by embracing blockchain solutions from startups. Our research extends the APCO framework by clarifying how privacy concerns, brand-based heuristics, and technological attributes interact. For managerial implications, blockchain applications necessitate re-intermediation to meet consumer preferences. Potential intermediaries, including Big Tech firms, startups, and industry incumbents, face unique challenges in developing and marketing blockchain-enabled consumer services.
In today's data-driven world, the convergence of advanced machine learning techniques with privacy concerns has prompted the development of innovative approaches to safeguard sensitive information while harnessing the power of data analytics.This research article delves into the realm of privacy-preserving machine learning algorithms, specifically focusing on methodologies that embrace the concept of local information privacy.The abstract provides a succinct overview of the key themes, methodologies, and implications elucidated within the paper.The abstract begins by contextualizing the contemporary landscape, emphasizing the proliferation of big data and the attendant privacy challenges it poses.It highlights the dichotomy between the utility of machine learning algorithms and the imperative of preserving individuals' privacy, setting the stage for exploring novel solutions.Central to the abstract is the conceptual framework of local information privacy, which forms the cornerstone of privacy-preserving machine learning algorithms discussed in the paper.The abstract delineates the theoretical foundations of this framework, elucidating how decentralized computation and differential privacy principles contribute to safeguarding sensitive data.Moving beyond theoretical underpinnings, the abstract provides insights into the methodologies employed in privacy-preserving machine learning.It outlines diverse approaches such as federated learning, secure multi-party computation, and homomorphic encryption, showcasing their utility in mitigating privacy risks while enabling collaborative model training and inference.Furthermore, the abstract underscores the practical implications of adopting privacy-preserving machine learning algorithms leveraging local information privacy.It cites examples across various sectors, including healthcare, finance, and IoT, where decentralized learning frameworks empower organizations to derive actionable insights from data while upholding privacy regulations and ethical standards.The abstract concludes by delineating potential avenues for future research and development in the field.It emphasizes the importance of scalability, efficiency, and robustness in privacy-preserving techniques, calling for interdisciplinary collaborations to address emerging challenges and navigate regulatory landscapes effectively.The abstract encapsulates the essence of the research article, providing a concise yet comprehensive overview of privacy-preserving machine learning algorithms using local information privacy.It serves as a gateway for readers to delve deeper into the nuances of the topic while highlighting its significance in addressing contemporary privacy challenges in the era of big data and advanced analytics.
Nicolas Oderbolz, Beatrix Marosvölgyi, Matthias Hafner
This paper examines the economic and security implications of Proof-of-Stake (POS) designs, providing a survey of POS design choices and their underlying economic principles in prominent POS-blockchains. The paper argues that POS-blockchains are essentially platforms that connect three groups of agents: users, validators, and investors. To meet the needs of these groups, blockchains must balance trade-offs between security, user adoption, and investment into the protocol. We focus on the security aspect and identify two different strategies: increasing the quality of validators (static security) vs. increasing the quantity of stakes (dynamic security). We argue that quality comes at the cost of quantity, identifying a trade-off between the two strategies when designing POS systems. We test our qualitative findings using panel analysis on collected data. The analysis indicates that enhancing the quality of the validator set through security measures like slashing and minimum staking amounts may decrease dynamic security. Further, the analysis reveals a strategic divergence among blockchains, highlighting the absence of a single, universally optimal staking design solution. The optimal design hinges upon a platform's specific objectives and its developmental stage. This research compels blockchain developers to meticulously assess the trade-offs outlined in this paper when developing their staking designs.
The rise of Web3 social ecosystems signifies the dawn of a new chapter in digital interaction, offering significant prospects for user engagement and financial advancement. Nonetheless, this progress is shadowed by potential privacy concessions, especially as these platforms frequently merge with existing Web2.0 social media accounts, amplifying data privacy risks for users. In this study, we investigate the nuanced dynamics between user engagement on Web3 social platforms and the consequent privacy concerns. We scrutinize the widespread phenomenon of fabricated activities, which encompasses the establishment of bogus accounts aimed at mimicking popularity and the deliberate distortion of social interactions by some individuals to gain financial rewards. Such deceptive maneuvers not only distort the true measure of the active user base but also amplify privacy threats for all members of the user community. We also find that, notwithstanding their attempts to limit social exposure, users remain entangled in privacy vulnerabilities. The actions of those highly engaged users, albeit often a minority group, can inadvertently breach the privacy of the larger collective. By casting light on the delicate interplay between user engagement, financial motives, and privacy issues, we offer a comprehensive examination of the intrinsic challenges and hazards present in the Web3 social milieu. We highlight the urgent need for more stringent privacy measures and ethical protocols to navigate the complex web of social exchanges and financial ambitions in the rapidly evolving Web3.
This study proposes a framework to enhance privacy in Blockchain-based Internet of Things (BIoT) systems used in the healthcare sector. The framework addresses the challenge of leveraging health data for analytics while protecting patient privacy. To achieve this, the study integrates Differential Privacy (DP) with Federated Learning (FL) to protect sensitive health data collected by IoT nodes. The proposed framework utilizes dynamic personalization and adaptive noise distribution strategies to balance privacy and data utility. Additionally, blockchain technology ensures secure and transparent aggregation and storage of model updates. Experimental results on the SVHN dataset demonstrate that the proposed framework achieves strong privacy guarantees against various attack scenarios while maintaining high accuracy in health analytics tasks. For 15 rounds of federated learning with an epsilon value of 8.0, the model obtains an accuracy of 64.50%. The blockchain integration, utilizing Ethereum, Ganache, Web3.py, and IPFS, exhibits an average transaction latency of around 6 seconds and consistent gas consumption across rounds, validating the practicality and feasibility of the proposed approach.
Tanusree Sharma, Vivek Nair, Henry Wang, Yang Wang · 5 authors
Key management has long remained a difficult unsolved problem in the field of usable security. While password-based key derivation functions (PBKDFs) are widely used to solve this problem in centralized applications, their low entropy and lack of a recovery mechanism make them unsuitable for use in decentralized contexts. The multi-factor key derivation function (MFKDF) is a recently proposed cryptographic primitive that aims to address these deficiencies by incorporating commonly used authentication factors into the key derivation process. In this paper, we implement an MFKDF-based Ethereum wallet and perform a user study with 27 participants to directly compare its usability against traditional cryptocurrency wallet architectures. Our results show that MFKDF-based applications outperform conventional key management approaches on both subjective and objective metrics, with a 37% higher average SUS score (p < 0.0001) and 71% faster task completion times (p < 0.0001) for the MFKDF-based wallet.
Cryptocurrency wallets come in various forms, each with unique usability and security features. Through interviews with 24 users, we explore reasons for selecting wallets in different contexts. Participants opt for smart contract wallets to simplify key management, leveraging social interactions. However, they prefer personal devices over individuals as guardians to avoid social cybersecurity concerns in managing guardian relationships. When engaging in high-stakes or complex transactions, they often choose browser-based wallets, leveraging third-party security extensions. For simpler transactions, they prefer the convenience of mobile wallets. Many participants avoid hardware wallets due to usability issues and security concerns with respect to key recovery service provided by manufacturer and phishing attacks. Social networks play a dual role: participants seek security advice from friends, but also express security concerns in soliciting this help. We offer novel insights into how and why users adopt specific wallets. We also discuss design recommendations for future wallet technologies based on our findings.
The advent of Web3 technologies promises unprecedented levels of user control and autonomy. However, this decentralization shifts the burden of security onto the users, making it crucial to understand their security behaviors and perceptions. To address this, our study introduces a comprehensive framework that identifies four core components of user interaction within the Web3 ecosystem: blockchain infrastructures, Web3-based Decentralized Applications (DApps), online communities, and off-chain cryptocurrency platforms. We delve into the security concerns perceived by users in each of these components and analyze the mitigation strategies they employ, ranging from risk assessment and aversion to diversification and acceptance. We further discuss the landscape of both technical and human-induced security risks in the Web3 ecosystem, identify the unique security differences between Web2 and Web3, and highlight key challenges that render users vulnerable, to provide implications for security design in Web3.
Abstract The focus of this review article is on the societal problems and end user acceptance of blockchain technology. The paper begins by outlining the importance of blockchain in modernizing trust and data management systems and highlighting its rapid spread across numerous industries. In‐depth analysis of the adoption‐influencing aspects is done, which also lists the advantages and typical end‐user problems. It examines the privacy implications, restrictions on pseudonymity, and function of technologies that improve privacy, such as zero‐knowledge proofs, while also exploring the legal and regulatory environment around blockchain, putting a focus on digital identity, intellectual property, and data ownership. It also evaluates blockchain security features, such as flaws and risks associated with smart contracts, discusses best practices for boosting security, discusses the societal effects of blockchain, and makes suggestions for legislators, companies, and scholars. The use of blockchain technology and its effects on privacy, rights, and security are discussed in real‐world case studies as well.
Recently, big data related to human movement, air quality, and meteorology have been generated in urban computing through sensing technology and the computing infrastructure. However, security problems arise as data utilization increases. If the sensing data from internet of things devices are constantly exposed, the users’ private information can be determined, a critical security risk that could result in privacy breaches. This paper proposes a secure data processing system using the blockchain and differential privacy for data security and privacy protection in urban computing. When a service provider requests information, the system generates it from urban computing data using machine learning. We apply differential privacy to these data to protect privacy. However, if a query repeats, differential privacy may provide insufficient privacy protection. Therefore, we reduce the total privacy cost by reusing noise for the same data and privacy parameters using the blockchain. Machine learning accuracy may decrease when noisy data are used for training. Thus, we increase accuracy by storing and appropriately using the model parameters generated by the same data in the blockchain. We design, simulate, and analyze the results of an experimental environment for reusing noise for differential privacy and parameter utilization of machine learning using the blockchain. The proposed approach reduces privacy costs compared to the existing mechanism while protecting data privacy. We demonstrate that, through parameter utilization, the accuracy improves compared to conventional mechanisms.
This paper explores the evidentiary significance of blockchain records and the procedural implications of integrating this technology into the U.S. judicial system, as several states have undertaken legislative measures to facilitate the admissibility of blockchain evidence. We employ a comprehensive methodological approach, including legislative analysis, comparative case law analysis, technical examination of blockchain mechanics, and stakeholder engagement. Our study suggests that blockchain evidence may be categorized as hearsay exceptions or non-hearsay, depending on the specific characteristics of the records. The paper proposes a specialized consensus mechanism for standardizing blockchain evidence authentication and outlines strategies to enhance the technology’s trustworthiness. It also highlights the importance of expert testimony in clarifying blockchain’s technical aspects for legal contexts. This study contributes to understanding blockchain’s integration into judicial systems, emphasizing the need for a comprehensive approach to its admissibility and reliability as evidence. It bridges the gap between technology and law, offering a blueprint for standardizing legal approaches to blockchain and urging ethical and transparent technology use.
Current attempts to regulate blockchain technology are mainly based on securities law framework, which considers crypto tokens and digital assets as either securities, currencies or derivatives thereof. The main limitation of such approach lies in its inability to accommodate the diverse legal rights, obligations and assets that blockchain technology can virtually reproduce. Already in 2017–2018 there were attempts to tokenize rights outside of securities law framework, these initiatives served more as makeshift solutions to circumvent securities regulations than as thorough frameworks for managing real-world assets and commercial activities. This article conducts a comparative and historical analysis of blockchain regulatory initiatives in Europe and the US, positing that the regulation of blockchain technology through a securities law lens is driven by reactionary opportunism. Such a basis is deemed inappropriate and insufficient, as securities laws being a field of public law were not designed to govern real-world assets and commerce, which fundamentally rely on the principles of laissez-faire and freedom of contract inherent in private law. A regulatory stance focused solely on public law overlooks the full potential of blockchain technology, and risks stifling innovation and practical applications. To illustrate this, the article presents case study of tokenization of contractual rights demonstrating that securities law-focused legal regulations, such as the EU Regulation 2023/1114 on Markets in Crypto-Assets (MiCA) and Regulation 2022/858 on Distributed Ledger Technology (DLT), inadequately address the field of private commerce. Based on the analysis, the article concludes that comprehensive legal framework for blockchain technology shall combine public and private law regime akin to the regulation of traditional rights, obligations and assets.
Web services that use a blockchain and crypto-assets (Web3 services) improve user privacy by anonymous logins using wallet addresses. However, since many users list their account identities (IDs) on social networking service (SNS) profile pages and reuse their account IDs for self-branding and curation purposes, which increases the risk of de-anonymization on Web3 services by linking these accounts. If such high-risk SNS accounts hold large amounts of crypto-assets, they are subject to account hijacking and spoofing attacks for financial gain. In this study, we proposed a method to discover highly relevant SNS accounts from a seed account on Web2 and Web3 SNSs and estimate their account ownership. We applied our method to 480 seed accounts of 9 different SNSs and discovered 1,233 new accounts. We found that SNSs with multiple URL input forms on their profile setting pages linked more accounts and revealed that 207 out of 253 (81.8%) users reused their IDs across different SNSs. We identified 26 accounts linked to personal and crypto-asset information that are at risk of de-anonymization. Our user study using crowdsourcing services showed that as many as 232 (40.8%) out of 568 respondents do not understand the traceability of blockchain transaction histories. We examined the security and privacy risks caused by account listing and ID reuse, and made recommendations for service providers and users based on our findings.
Sulemana Awal, Diyawu Mumin, Arnold Mashud Abukari, Abukari Aziz Danaa · 5 authors
Online services currently rely heavily on centralized authentication methods to manage user identification and authentication. However, these methods are vulnerable to account hacking, which can compromise user data and lead to attacks. A potential solution to this issue is the use of distributed ledger technology, such as blockchain, to decentralize credential ownership and provide a secure, immutable platform for verifying user identity. This paper aims to analyze the drawbacks of centralized authentication systems and propose an alternative that uses blockchain technology for authentication, ensuring robustness, transparency, and security. The proposed system is tested on web applications using the Ethereum testnet and an authentication provider (backend server).
Beginning with a comprehensive definition of Decentralized Applications (DApps) and their developmental trajectory, this treatise delves into their inception around 2010. It is intriguing to note that by 2020, DApps had already found preliminary applications in diverse sectors, ranging from finance to archaeology. Yet, there remains vast untapped potential awaiting exploration and refinement within the realm of DApps. The discourse then navigates the intricate web of DApps' system architecture, illuminating the cardinal aspects of their design, evolution, and eventual deployment. Herein, the essence of systematic planning during the design phase is underscored, underpinning its pivotal role in shaping the efficacy of the application. Further shedding light on DApps' expansive utility, the paper underscores their transformative influence in areas such as authentication systems and real-time operational control. However, the journey of DApps is not without its challenges. The document elucidates the complexities associated with crafting robust smart contracts, mitigating scalability concerns, and nurturing user acceptance and integration. In light of these hurdles, a clarion call is made for persistent research and avant-garde innovation, propelling DApps to their true potential in the evolving digital landscape.
The blockchain technology can become a solution in data privacy and protection, being considered a public and decentralized database, in which the identity of a user is masked in the form of an address. Based on this technology, several decentralized protocols such as Bitcoin or Ethereum have been created, with the help of which decentralized applications can be created. In this article, we examine how these technologies can be used to ensure the transparency of a software application, but also the privacy and protection of user data. Other facilities like Non-Fungible Token - NFT and Smart Contract are used. The paper proposes the design and the implementation (including the source code) of a web platform for education / courses incorporating blockchain technology. The results and conclusions are showed. Finally, the contributions of the paper, the limitations and future works are also highlighted.
This scoping review examines individual and societal use cases of Bitcoin in the peer-reviewed literature. Arksey and O’Malley’s scoping review methodology was used, and a comprehensive search strategy was employed using Web of Science and Engineering village databases. Articles were screened at the title and abstract and full-text levels by the authors. One author conducted data extraction to summarize the data. In total, 17 relevant articles were included in this review. Investment and savings were the most widely reported use cases at an individual level, with payments and international transfers less frequently reported in the studies. Only two studies reported on societal use cases of legal tender; however, only one country, El Salvador, executed its intention. Our study suggests that Bitcoin is being used by individuals around the world with little report of societal (e.g., country adoption) uses cases. For example, there is evidence on the internet and on a grass-roots level that Bitcoin is being used in circular economies; however, the peer-reviewed literature may not yet capture the extent and full benefits and challenges. As such, we provide ideas for future research to more comprehensively explore Bitcoin uses and its impacts on individuals and society.
Patikiri Arachchige Don Shehan Nilmantha Wijesekara
Access Control (AC) in networking attempts to make sure that only authorized devices perform actions formed upon privileges defined for them with a view to prevent malicious users' entry and interaction in the communication grid. Blockchain solutions contain an arrangement of related blocks that naturally safeguards the trustworthiness, defending the incontestability, defend masked-identity of its transactions/transfers due to scattered consensus strategies and cryptographic solutions. Our survey comprehensively reviews BC-formed AC in broad scope of networking considering AC techniques while breaking down into 4 propositions and assessing them in terms of blockchain roles, AC technique and approach, network elements, and rest. We stockpiled a primary sample of 79 bibliographic references by weeding out them for screening criteria sought from scientific information reservoirs exploiting a qualitative and extensive strategy. Formed upon this survey, in blockchain-formed AC, blockchain can be exploited as an AC manager to administrate network devices and access information, implement automatic AC by means of smart contracts, secure storage of AC related data to reinforce overall AC security, and for safe data exchanging in the operation of AC. Minute assessment highlights that from blockchain-formed AC, 52.5% provide AC using blockchain itself or using smart contracts, 92.5% exploit sequential blockchain, 35% exploit PBFT consensus, provide 100% fine-grained and host-formed AC, 85% decentralized AC, 87.5% have single-factor authentication, 92.5% provide dynamic AC, and 45% have opted for IoT. Finally, we evaluate the chances and difficulties of the principle of blockchain-formed AC and then giving recommended actions to beat them.
Beulah Jayakumari, S. Lilly Sheeba, Maya Eapen, L. Jani Anbarasi · 7 authors
With the invention of Internet-enabled devices, cloud and blockchain-based technologies, an online voting system can smoothly carry out election processes. During pandemic situations, citizens tend to develop panic about mass gatherings, which may influence the decrease in the number of votes. This urges a reliable, flexible, transparent, secure, and cost-effective voting system. The proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system, and it is carried out in three phases: the registration phase, vote casting phase and vote counting phase. A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting phases. Using smart contracts, third-party interventions are eliminated, and the transactions are secured in the blockchain network. Finally, to provide accurate voting results, the practical Byzantine fault tolerance (PBFT) consensus mechanism is adopted to ensure that the vote has not been modified or corrupted. Hence, the overall performance of the proposed system is significantly better than that of the existing system. Further performance was analyzed based on authentication delay, vote alteration, response time, and latency.
Abstract Technologies of self-sovereign identity (SSI) and Web3 tools that strongly protect individual autonomy, combined with the ethics of Asian altruism, can effectively guide the governance of the emerging cyber civilization. In contrast, governance in Western industrial civilization stresses the pursuit of individual self-interest and struggles to balance the benefits of big tech with the protection of individual dignity and the preservation of the common good. We demonstrate, with reference to a local community in Japan, that SSI is successful in encouraging communal collaboration and well-being while providing individuals with greater control over their personal data. We also show that Web3 tools provide incentives for altruistic behaviors while safeguarding SSI. Integration of SSI and social protection demonstrates the potential for building an information society grounded in altruistic values, honoring individual dignity, and recognizing the government’s role in protecting social welfare. Ultimately, this research unveils how altruistic values can be fostered through SSI and Web3.