The public ledger characteristic of blockchain grants data immutability but simultaneously introduces privacy leakage risks, making association analysis between on-chain behaviors and real-world identities possible. Existing privacy protection schemes struggle to balance the anonymity of the querier with the traceability of malicious behaviors. On one hand, legitimate inquiry behaviors are easily reverse-tracked by third parties through on-chain records (i.e., "human flesh search" targeting the querier); on the other hand, a completely anonymous environment may lead to data abuse without the possibility of accountability.To address this issue, this paper proposes an anti-"human flesh search" privacy protection system based on blockchain and zero-knowledge proofs. Addressing the aforementioned contradictions, this paper presents a blockchain data sharing scheme that balances privacy and regulation. The scheme utilizes IPFS to implement graded encrypted storage for large files. The core innovation lies in combining the Schnorr protocol and Chameleon Hash to construct a Blockchain Designated Verifier Proof (BDVP). While verifying user query permissions through blockchain smart contracts, the system utilizes the trapdoor property of the Chameleon Hash to achieve the non-transferability of proofs, preventing third parties from reverse-tracking the querier's identity by analyzing on-chain records<sup>[<xref ref-type="bibr" rid="R2">2</xref>]</sup>. Furthermore, the system introduces a threshold private key held by regulatory agencies to ensure that, in the event of data abuse, malicious users can be de-anonymized and held accountable according to the law.
Consensus protocols used today in blockchains often rely on computational power or financial stakes - scarce resources. We propose a novel protocol using social capital - trust and influence from social interactions - as a non-transferable staking mechanism to ensure fairness and decentralization. The methodology integrates zero-knowledge proofs, verifiable credentials, a Whisk-like leader election, and an incentive scheme to prevent Sybil attacks and encourage engagement. The theoretical framework would enhance privacy and equity, though unresolved issues like off-chain bribery require further research. This work offers a new model aligned with modern social media behavior and lifestyle, with applications in finance, providing a practical insight for decentralized system development.
Civic intelligence (CI) represents the collective capacity of communities to address challenges, yet its integration with smart city infrastructure remains limited. This study bridges CI theory with technical implementation through a novel framework combining blockchain and AI technologies. Our approach maps core CI components (knowledge capital, system capital, and relational capital) to specific technical solutions: a civic engagement index for measuring participation quality, a tokenization framework for incentivizing meaningful engagement, and a governance optimization function for resource allocation. Using mixed-methods research, we developed and validated the conceptual CI governance (CIG) framework, which satisfies CI principles through smart contracts and AI-assisted interfaces. The empirical evaluation demonstrates both social and technical improvements: 40% increased civic participation rates, 85% governance efficiency maintenance, and significant gains in engagement quality metrics (knowledge sharing +32%, collective decision making +28%). While technical implementation shows promise, success requires the careful integration of social dynamics, digital literacy initiatives, and regulatory compliance. This research contributes to smart city development by providing a theoretically grounded, feasible framework that introduces the fusion of blockchain and AI technologies to enhance civic participation while preserving governance effectiveness.
Roozbeh Sarenche, Alireza Aghabagherloo, Svetla Nikova⋆, Bart Preneel
The security of Bitcoin protocols is deeply dependent on the incentives provided to miners, which come from a combination of block rewards and transaction fees. As Bitcoin experiences more halving events, the protocol reward converges to zero, making transaction fees the primary source of miner rewards. This shift in Bitcoin's incentivization mechanism, which introduces volatility into block rewards, leads to the emergence of new security threats or intensifies existing ones. Previous security analyses of Bitcoin have either considered a fixed block reward model or a highly simplified volatile model, overlooking the complexities of Bitcoin's mempool behavior. This paper presents a reinforcement learning-based tool to develop mining strategies under a more realistic volatile model. We employ the Asynchronous Advantage Actor-Critic (A3C) algorithm, which efficiently handles dynamic environments, such as the Bitcoin mempool, to derive near-optimal mining strategies when interacting with an environment that models the complexity of the Bitcoin mempool. This tool enables the analysis of adversarial mining strategies, such as selfish mining and undercutting, both before and after difficulty adjustments, providing insights into the effects of mining attacks in both the short and long term. We revisit the Bitcoin security threshold presented in the WeRLman paper and demonstrate that the implicit predictability of valuable transaction arrivals in this model leads to an underestimation of the reported threshold. Additionally, we show that, while adversarial strategies like selfish mining under the fixed reward model incur an initial loss period of at least two weeks, the transition toward a transaction-fee era incentivizes mining pools to abandon honest mining for immediate profits. This incentive is expected to become more significant as the protocol reward approaches zero in the future.
Approche Théorie des jeux pour l'Étude de la Robustesse des Blockchains Game-theoretical approach for the study of Blockchain's Robustness Les blockchains ont suscité un intérêt mondial ces dernières années, prenant de plus en plus d'importance à mesure qu'elles influencent les technologies et la finance. Cette thèse explore la robustesse des protocoles blockchain, en se concentrant spécifiquement sur Ethereum Proof-of-Stake (PoS). Nous définissons la robustesse en termes de deux propriétés essentielles : la sécurité, qui garantit que la blockchain n'aura pas de blocs conflictuels permanents, et la vivacité, qui assure l'ajout continu de nouveaux blocs fiables.Notre recherche aborde l'écart entre les approches traditionnelles des systèmes distribués, qui classifient les agents comme étant soit honnêtes, soit byzantins (i.e., malveillants ou défaillants), et les modèles de théorie des jeux qui considèrent les agents rationnels motivés par des incitations. Nous explorons comment les incitations impactent la robustesse en utilisant les deux approches.La thèse est composé de trois analyses distinctes. Nous commençons par formaliser le protocole Ethereum PoS, définissant ses propriétés et examinant les vulnérabilités potentielles du point de vue des systèmes distribués. Nous identifions certaines attaques qui peuvent compromettre la robustesse du système. Ensuite, nous analysons le mécanisme de fuite d'inactivité, une caractéristique clé d'Ethereum PoS, en soulignant son rôle dans le maintien de la vivacité du système lors de perturbations du réseau, mais au détriment de la sécurité. Enfin, nous utilisons des modèles de théorie des jeux pour étudier les stratégies des validateurs rationnels au sein d'Ethereum PoS, en identifiant les conditions dans lesquelles ces agents pourraient s'écarter du protocole prescrit pour maximiser leurs récompenses.Nos résultats contribuent à une meilleure compréhension de l'importance des mécanismes d'incitation pour la robustesse des blockchains et donnent des pistes pour concevoir des protocoles blockchain plus résilients.
Decentralized AI promises to unlock the potential of fragmented data across domains like healthcare and finance, but faces significant challenges in trust and incentive alignment. This paper proposes a comprehensive framework addressing these challenges through advanced privacy-preserving techniques and federated data markets. We integrate collaborative inference, split learning, and synthetic data sharing to establish trust, while leveraging federated data valuation and acquisition to foster fair and efficient data markets. Our systematic review identifies areas of progress in decentralized AI and highlights key research opportunities. This analysis focuses on overcoming barriers to collaborative AI development while preserving data sovereignty, paving the way for more inclusive and innovative AI ecosystems.
Truth and Privacy-preserving service are two key issues for data collection in Mobile Crowd Sensing (MCS). However, most of the existing data collection studies use CRH to calculate the truth value, which is difficult to guarantee the data accuracy. The proposed privacy-preserving service methods either neglect the truth-value computation or still adopt the CRH method. To solve the challenge, we propose a Reputation and Privacy-Preserving Service based Truth Data Collection (RPPS-TDC) scheme to achieve the privacy-preserving accurate data collection for MCS. RPPS-TDC scheme consists of two steps: worker trust calculation and reputation-based truth value calculation. Firstly, data requester performs the initial weights calculation on the received data using DBCRH method. Secondly, reputation center uses the initial weights to update the reputation, which is based on the trustworthy worker inference. Finally, the workers are reweighted according to their reputation. We select workers based on trust_MaxHeap, thus obtaining more accurate data. At the same time, we give workers payments based on their trust weights. All the above operations are completed under data encryption, ensuring that only the data requesters have access to the workers’ submitted data. The platform consists of mining nodes, and reputation center resides on the blockchain, thus achieving distributed computing which overcomes the shortcomings of the traditional MCS system. Extensive experiments demonstrate that RPPS-TDC is effective and outperforms previous strategies in two key performance metrics: data accuracy and compensation rationality allocation.
Mobile Crowd Sensing (MCS) has emerged as a new sensing paradigm due to its cost efficiency, mobility, and expandability. However, user selection for task allocation is a significant challenge in MCS. Most previous studies concentrate on two selection modes, opportunistic and participatory selection. Recent research has proposed a hybrid user selection mode that combines both advantages. However, existing hybrid user selection systems all rely on a centralized architecture, which is vulnerable to malicious attacks, and they do not consider the reliability of users and data availability. Moreover, they cannot ensure the individual rationality of users. To overcome these shortcomings, we propose a blockchain-based hybrid reliable user selection scheme for task allocation in MCS. Specifically, we replace the traditional central server with the blockchain and handle various sensing task operations using smart contracts on the blockchain to ensure system reliability and security. In addition, we design a user reputation calculation algorithm based on semi-Markov and a sensing data anomaly detection algorithm based on Long Short-Term Memory (LSTM) to ensure user reliability and data availability, and also a novel hybrid user selection algorithm, especially in the participatory user selection stage, where we use a user selection algorithm based on reverse auction to ensure the individual rationality of each user. Experimental results demonstrate the effectiveness of the proposed scheme through simulation experiments on GeoLife and sound-sensing public datasets.
Rafael Marin Machado de Souza, A. I. S. Holm, Márcio Biczyk, Leandro Nunes de Castro
Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC.
Decentralizing crowdsourcing through blockchain technology eliminates the need for trusted third-party intermediaries that may introduce social biases in data aggregation, thereby enhancing transparency and ensuring appropriate rewards for workers. However, open permissionless blockchain platforms typically disclose all transaction data on public ledgers, which compromises the privacy and anonymity of workers and encourages free-riding. Blockchain-based anonymous crowdsourcing systems have recently emerged, offering anonymity but requiring identity registration for workers and a trusted setup for key generation. These systems in general, fail to support anonymous payments, potentially compromising worker identities. In this paper, we integrate anonymous payments into crowdsourcing, eliminating the need for identity registration and trusted setup, thus fostering open and anonymous participation from any worker. Our solution utilizes the decentralized anonymous payment system framework, such as Zerocoin, and includes staking mechanisms for participation in crowdsourcing as well as efficient one-out-of-many zero-knowledge proofs. Additionally, our empirical evaluations reveal that the system incurs moderate and practical gas costs.
A. Bendada, Mouhamed Amine Bouchiha, Mourad Rabah, Yacine Ghamri-Doudane
Current blockchain-based reputation solutions for crowdsourcing fail to tackle the challenge of ensuring both efficiency and privacy without compromising the scalability of the block chain. Developing an effective, transparent, and privacy-preserving reputation model necessitates on-chain implementation using smart contracts. However, managing task evaluation and reputation updates alongside crowdsourcing transactions on-chain substantially strains system scalability and performance. This paper introduces RollupTheCrowd, a novel blockchain-powered crowdsourcing framework that leverages zkRollups to enhance system scalability while protecting user privacy. Our framework includes an effective and privacy-preserving reputation model that gauges workers' trustworthiness by assessing their crowdsourcing interactions. To alleviate the load on our blockchain, we employ an off-chain storage scheme, optimizing RollupTheCrowd's performance. Utilizing smart contracts and zero-knowledge proofs, our Rollup layer achieves a significant 20x reduction in gas consumption. To prove the feasibility of the proposed framework, we developed a proof-of-concept implementation using cutting-edge tools. The experimental results presented in this paper demonstrate the effectiveness and scalability of RollupTheCrowd, validating its potential for real-world application scenarios.
In the domain of spatial crowdsourcing drone services, which includes tasks like delivery, surveillance, and data collection, secure communication is paramount. The Public Key Infrastructure (PKI) ensures this by providing a system for digital certificates that authenticate the identities of entities involved, securing data and command transmissions between drones and their operators. However, the centralized trust model of traditional PKI, dependent on Certificate Authorities (CAs), presents a vulnerability due to its single point of failure, risking security breaches. To counteract this, the paper presents D2XChain, a blockchain-based PKI framework designed for the Internet of Drone Things (IoDT). By decentralizing the CA infrastructure, D2XChain eliminates this single point of failure, thereby enhancing the security and reliability of drone communications. Fully compatible with the X.509 standard, it integrates seamlessly with existing PKI systems, supporting all key operations such as certificate registration, validation, verification, and revocation in a distributed manner. This innovative approach not only strengthens the defense of drone services against various security threats but also showcases its practical application through deployment on a private Ethereum testbed, representing a significant advancement in addressing the unique security challenges of drone-based services and ensuring their trustworthy operation in critical tasks.
Ahmed Alagha, Hadi Otrok, Shakti Singh, Rabeb Mizouni · 5 authors
Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its complexity, which requires expertise in training and designing DRL solutions, high computational capabilities, and sometimes access to pre-trained models. This necessitates the need for hassle-free services that increase the availability of DRL solutions to a variety of users. To enhance the accessibility to DRL services, this paper proposes a novel blockchain-based crowdsourced DRL as a Service (DRLaaS) framework. The framework provides DRL-related services to users, covering two types of tasks: DRL training and model sharing. Through crowdsourcing, users could benefit from the expertise and computational capabilities of workers to train DRL solutions. Model sharing could help users gain access to pre-trained models, shared by workers in return for incentives, which can help train new DRL solutions using methods in knowledge transfer. The DRLaaS framework is built on top of a Consortium Blockchain to enable traceable and autonomous execution. Smart Contracts are designed to manage worker and model allocation, which are stored using the InterPlanetary File System (IPFS) to ensure tamper-proof data distribution. The framework is tested on several DRL applications, proving its efficacy.
Georgios Birmpas, Philip Lazos, Evangelos Markakis, Paolo Penna
In this paper, we investigate the impact of reward schemes and committee sizes motivated by governance systems over blockchain communities. We introduce a model for elections with a binary outcome space where there is a ground truth (i.e., a "correct" outcome), and where stakeholders can only choose to delegate their voting power to a set of delegation representatives (DReps). Moreover, the effort (cost) invested by each DRep positively influences both (i) her ability to vote correctly and (ii) the total delegation that she attracts, thereby increasing her voting power. This model constitutes the natural counterpart of delegated proof-of-stake (PoS) protocols, where delegated stakes are used to elect the block builders. As a way to motivate the representatives to exert effort, a reward scheme can be used based on the delegation attracted by each DRep. We analyze both the game-theoretic aspects and the optimization counterpart of this model. Our primary focus is on selecting a committee that maximizes the probability of reaching the correct outcome, given a fixed monetary budget allocated for rewarding the delegates. Our findings provide insights into the design of effective reward mechanisms and optimal committee structures (i.e., how many DReps are enough) in these PoS-like governance systems.
Mobile crowdsourcing aims to recruit enough workers holding mobile devices to collect data. Nevertheless, the platform will have cold start problems when the number of workers is limited. Existing studies have proposed solving this problem by propagating tasks to social networks for social recruitment. However, they neglect to verify workers’ propagation, leading to malicious workers reducing the platform's utility. Furthermore, during propagation verification, it is imperative to protect the privacy of social relationships among workers, as it can significantly influence the propagation. Therefore, this paper proposes Zero-knowledge Propagation Verification based on Social Relationship Encryption (ZPV-SRE) to improve the platform's utility. Specifically, we transform the propagation verification problem into a problem of computing the solution of the function. Then, the Zero-knowledge proof is used to prove the propagation, in which the worker's social relationship is protected through homomorphic encryption. Considering that ZPV-SRE will incur a significant time cost, we propose Trust-guided Zero-knowledge Propagation Verification based on Social Relationship Encryption (TZPV-SRE), which updates the worker's trust based on the verification results and selects suspicious workers for verification. The experimental results show ZPV-SRE improves the platform's utility as high as 104.05% over the state-of-the-art methods, while TZPV-SRE reduces time costs and ensures improvement.
Blockchains implement decentralized monetary systems and applications. Recent advancements enable what we call tethering a blockchain to a primary blockchain, securing the tethered chain by nodes that post primary-chain tokens as collateral. The collateral ensures nodes behave as intended, until they withdraw it. Unlike a Proof of Stake blockchain which uses its own token as collateral, using primary-chain tokens shields the tethered chain from the volatility of its own token. State-of-the-art tethered blockchains either rely on centralization, or make extreme assumptions: that all communication is synchronous, that operators remain correct even post-withdrawal, or that withdrawals can be indefinitely delayed by tethered-chain failures. We prove that with partial synchrony, there is no solution to the problem. However, under the standard assumptions that communication with the primary chain is synchronous and communication among the tethered chain nodes is partially synchronous, there is a solution. We present a tethered-chain protocol called Aegis. Aegis uses references from its blocks to primary blocks to define committees, checkpoints on the primary chain to perpetuate decisions, and resets to establish new committees when previous ones become obsolete. It ensures safety at all times and rapid progress when latency among Aegis nodes is low.
Securing data generated from diverse sensors poses a critical challenge in contemporary applications, particularly due to the escalating volume of data and its vulnerability to security breaches. Blockchain technology has emerged as a promising solution, yet the implementation of blockchain applications entails significant costs. Assessing the feasibility of such implementations through simulation is imperative but has been hindered by a lack of details survey about the simulation tools leading to wrong choice of tools by the researchers, as every application is unique and requires specific blockchain platform as per use. This paper covers an extensive survey of 32 simulation tools used in blockchain, focusing on their advantages, limitations, platform used, and capability to evaluate performance metrics of blockchain applications. This enables the researchers to choose the correct simulation tool for their work. The work allows the programmer to test their application using the correct simulation tool, saving implementation costs and time. Furthermore, we discussed CBlockSim, a simulation tool designed specifically for blockchain applications, elucidating its functionality through a comparative study of Bitcoin and Ethereum. Our work contributes to advancing the understanding and application of simulation tools in blockchain-based sensor data security, offering insights that can significantly enhance the security posture of sensor networks.