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Consensus protocols underpin the security and correctness of decentralized blockchain systems by enabling mutually untrusted participants to agree on a shared state. Early blockchain networks relied on Proof-of-Work (PoW), which achieves strong security by making participation computationally expensive, but its high energy consumption has driven the transition toward Proof-of-Stake (PoS) and its variants. Nominated Proof-of-Stake (NPoS) addresses PoS centralization tendencies by separating stake from block production, allowing participants (known as nominators) to delegate their staked tokens to validators, aiming to enable broader, fairer, and more decentralized participation in consensus. This thesis addresses the gap between NPoS’s design intentions and its realworld effectiveness by using Polkadot as a case study: we employ multi-year on-chain data and a graph-based methodology that models the evolving relationships between nominators and validators, and introduce metrics to evaluate centralization, fairness, and inclusiveness in NPoS systems. The analysis reveals persistent structural concentration. Validator turnover is limited, with around 90% of validators remaining active across consecutive eras, and nomination patterns are highly persistent, with fewer than 20 nominators controlling up to one-third of the active validator set. Regarding fairness, while validator rewards converge to near-equality, nominator rewards remain highly unequal as an inherent consequence of stake-proportional allocation. Regarding inclusiveness, participation remains constrained: a non-trivial fraction of active validators charge 100% commission, retaining all staking rewards and distributing nothing to their nominators, which constitutes a structural barrier to meaningful economic participation for the nominators backing them. Overall, these findings indicate that NPoS still exhibits concentration dynamics that limit its effectiveness in achieving decentralization and inclusiveness.
Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holders’ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order to address these problems. The gemma-3-4b Large Language Model (LLM) in conjunction with Retrieval-Augmented Generation (RAG) powers APOLLO’s multi-agent system, which enhances contextual comprehension of proposals. The system enhances governance by merging real-time on-chain and off-chain data, ensuring adaptive decision-making. Automated proposal writing, logistic regression-based approval probability prediction, and real-time vote outcome analysis with contextual feature-based confidence scores are some of the major advancements. LLM is used to draft proposals and a feedback loop to enrich its knowledge base, reducing whale dominance and voter apathy with a transparent, bias-resistant system. This work demonstrates the revolutionary potential of AI in promoting decentralized governance, paving the way for more effective, inclusive, and dynamic DAO systems.
Traditional lucky draw systems often face trust issues due to a lack of transparency and potential for manipulation. This study addresses these challenges by developing and evaluating a blockchain-based lucky draw prototype named "LuckyDraw." A mixed-method approach was employed, combining an applied research method with the Agile Scrum framework for system development, and a quantitative survey to evaluate user acceptance. The quantitative analysis, using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique on data from 100 respondents, confirmed the instrument's validity and reliability. The results showed that Perceived Usefulness (PU) was the strongest predictor of Behavioral Intention (BIU), followed by Trust (TRT). Furthermore, Perceived Ease of Use (PEOU) had a significant positive effect on PU. These findings indicate that a transparent, trustworthy, and easy-to-use system is highly accepted by users, offering a viable solution to the shortcomings of traditional systems.
Autonomous Large Language Model (LLM)-based multi-agent systems have emerged as a promising paradigm for facilitating cross-application and cross-organization collaborations. These autonomous agents often operate in trustless environments, where centralized coordination faces significant challenges, such as the inability to ensure transparent contribution measurement and equitable incentive distribution. While blockchain is frequently proposed as a decentralized coordination platform, it inherently introduces high on-chain computation costs and risks exposing sensitive execution information of the agents. Consequently, the core challenge lies in enabling auditable task execution and fair incentive distribution for autonomous LLM agents in trustless environments, while simultaneously preserving their strategic privacy and minimizing on-chain costs. To address this challenge, we propose DAO-Agent, a novel framework that integrates three key technical innovations: (1) an on-chain decentralized autonomous organization (DAO) governance mechanism for transparent coordination and immutable logging; (2) a ZKP mechanism approach that enables Shapley-based contribution measurement off-chain, and (3) a hybrid on-chain/off-chain architecture that verifies ZKP-validated contribution measurements on-chain with minimal computational overhead. We implement DAO-Agent and conduct end-to-end experiments using a crypto trading task as a case study. Experimental results demonstrate that DAO-Agent achieves up to 99.9% reduction in verification gas costs compared to naive on-chain alternatives, with constant-time verification complexity that remains stable as coalition size increases, thereby establishing a scalable foundation for agent coordination in decentralized environments.
Jiayong Chai, Jun Guo, Muhua Wei, Mo Chen · 5 authors
Blockchain systems have been widely adopted in today’s society, with consensus algorithms serving as their core component to ensure all participants in the network agree on a specific data state. Existing consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and the Practical Byzantine Fault-Tolerant Algorithm (PBFT) exhibit certain limitations in terms of scalability, security, and efficiency. To address these limitations, this paper proposes a novel Network-based Reputation Consensus (NRC) algorithm. The main research contributions of this work include the following: (1) An intelligent grouping mechanism that dynamically groups nodes based on network awareness, forming consensus groups with low internal latency and high bandwidth utilization, significantly reducing intra-group communication overhead. (2) A dynamic reputation system incorporating a “diminishing returns” reward function and a “multiplicative penalty” mechanism, effectively incentivizing honest node participation while preventing power monopoly. (3) A two-phase model of “intra-group BFT consensus + global communication committee ordering” that decomposes complex global consensus into parallel intra-group processing and coordination among a small set of elite nodes, thereby drastically improving efficiency. (4) Comprehensive simulations comparing the NRC algorithm with mainstream consensus algorithms, demonstrating its superior performance in communication overhead, throughput, latency, and tolerance to malicious nodes, thereby laying the foundation for large-scale applications.
Rollups are central to blockchain scalability, but their governance is still evolving. Existing voting models risk capture by large stakeholders or inactive delegates. This paper introduces RepRoll, a governance model that uses delegated reputation scores backed by verifiable activity logs. Reputation grows through provable contributions: fraud-proof submissions, code audits, uptime guarantees, and community moderation. These contributions are recorded through a decentralized attestation layer similar to optimistic verification. Votes in protocol upgrades weigh both token stake and reputation, reducing plutocratic influence. A simulation of 10,000 participants demonstrates that RepRoll improves proposal quality and reduces governance attacks. We deploy a prototype on an Ethereum Layer-2 testnet, showing low on-chain overhead. The paper discusses vulnerabilities such as collusion, reputation laundering, and sybil amplification, and proposes cryptographic mitigations.
Matteo Bonato, Rıza Demirer, Rangan Gupta, Abeeb Olaniran
This paper explores the role of mining activity, proxied by growth rates of electricity consumption and cost of mining, as a driver of pricing inefficiencies in Bitcoin. Utilizing alternative measures of crash risk proxied by the realized negative coefficient of skewness and realized down-to-up volatility, derived from 5-minute intraday Bitcoin data, causality tests, along with sign analysis, captured by the estimates of partial average derivatives, provide evidence that mining activity can, in general, predict an increase in the entire conditional distribution of crash risk, with the strongest impact associated over the normal (median) to moderately high (upper quantiles) levels of risk. Despite the emergence of cryptocurrencies in international transactions and as an investment vehicle, our results suggest that decentralized mining process can contribute to inefficiencies in the pricing of Bitcoin, putting further doubt into the role of these assets as a medium of exchange, alternative to conventional assets.
In the contemporary global job market, the secure and efficient verification of a candidate’s academic qualifications presents a significant challenge, particularly across international boundaries. Conventional techniques frequently necessitate physical documents or PDF scans, rendering them inefficient, susceptible to falsification, and hazardous about privacy. This study presents a contemporary, scalable framework that integrates Zero-Knowledge Proofs (ZKPs), blockchain technology, and decentralized storage (IPFS) to establish a secure, privacy-oriented method for candidate verification. In this proposed system, candidates submit their academic documents, which are digitally signed by the issuing universities using cryptographic methods. The signed files are preserved on IPFS, guaranteeing their integrity and accessibility. The hash of each document is then stored on a blockchain, either Ethereum or Polygon, offering a public and immutable reference. Zero-Knowledge Proofs enable candidates to validate the legitimacy of their credentials while safeguarding sensitive information. Human Resources teams can authenticate these documents in real time, validating their integrity against the blockchain hash while preserving the candidate’s confidentiality. The evaluation results demonstrate that Ethereum offers robust decentralization and trust; nevertheless, Polygon proved to be more pragmatic because to its reduced gas price and expedited transaction times, making it suitable for high-volume recruitment. This proposed initiative addresses weaknesses in digital recruitment by guaranteeing trust, privacy, and automated credential verification procedure. It provides a customized approach for present recruitment requirements, particularly for organizations engaged in cross-border hiring, where security, scalability and protection of candidate information are paramount.
Blockchain is one of the most consequential innovations since the world wide web. Although blockchain is argued to remove, displace, or redistribute expertise, there is little understanding of the role of expertise in blockchain ecosystems, and more generally the expertise that fuels the development of new technologies by means of open, fluid, and heterogeneous knowledge contributions. An empirical study of the social organization of the Ethereum community, the second largest blockchain ecosystem after Bitcoin, reveals the contrasting tensions involved in setting up a system of decentralized expertise. The alternate community mantras “rough consensus, running code” and “wide consensus, better code?” suggest that the Ethereum community enacts expertise centralization and decentralization practices simultaneously to create a fragile balance between individualized accountabilities and a generalized sense of diffused participation. These practices unfold along a continuum of routine operations punctuated by critical events and are both essential for navigating the uncertainties of decentralized organizations. The study contributes to research on new forms of expertise occasioned by emerging technologies, and in particular to our understanding of blockchain expertise. The study’s relational perspective on expertise adds to research on the dynamics of knowledge de/centralization in online communities.
Abdullah Ayub Khan, Abdulmajeed Alsufyani, Nawal Alsufyani, Mohamad Afendee Mohamed · 5 authors
Environmental deterioration can cause major issues like air pollution, water scarcity, land degradation, and socioeconomic disruptions in heavily mined places like Sindh, Pakistan's Thar coalfields. To overcome these obstacles, a novel strategy using contemporary monitoring and prediction technology is required. This study presents a novel framework for tracking and reducing the environmental effects of mining in poor nations by combining data from blockchain technology, Temporal Convolutional Networks (TCNs), and remote sensing. To ensure stakeholder confidence and accountability, the proposed architecture recodes environmental data using Blockchain Distributed Ledger Technology (BDLT) in a secure, transparent, immutable, and secure manner. The primary potential is to periodically monitor key metrics like vegetation loss, water depletion, and air quality using the Remote Sensing (RS) approach. However, by examining temporal data, TCNs are able to predict trends in environmental degradation and take pre-emptive steps to prevent damage. With a prediction performance of up to 97.3%, metrics such as the Normalised Difference Vegetation Index (NDVI), Air Quality Index (AQI), and water table depth are assessed with great accuracy. In addition to offering politicians and regulators useful information, the proposed architecture uses chaincode to guarantee adherence to environmental regulations. Furthermore, this paper offers a scalable and adaptable solution to environmental limitations in resource-rich places. It supports international sustainability objectives and sets the standard for more ethical mining methods in underdeveloping countries.
This paper presents a secure aggregation system Armadillo that has disruptive resistance against adversarial clients, such that any coalition of malicious clients can affect the aggregation result only by misreporting their private inputs in a pre-defined legitimate range. Armadillo is designed for federated learning setting, where a single powerful server interacts with many weak clients iteratively to train models on client's private data. While a few prior works consider disruption resistance under such setting, for an aggregation on n clients they either require high cost per client (Chowdhury et al. CCS '22) or concretely many rounds that is logarithmic in n (Bell et al. USENIX Security '23). Although disruption resistance can be achieved generically with zero-knowledge proof techniques (which we also use in this paper), we realize an efficient system with two new designs: 1) a simple two-layer secure aggregation protocol that requires only simple arithmetic computation; 2) an agreement protocol that removes the effect of malicious clients from the aggregation with low round complexity. With these techniques, Armadillo runs in 3 rounds per aggregation (our round complexity is independent of n) with computationally lightweight server and clients.
With the rapid development of the Internet of Things (IoT), Location-Based Services (LBS) have been widely applied in smart transportation, mobile social networking, and urban sensing. However, the high sensitivity of precise location data makes it a primary source of privacy breaches. Existing privacy-preserving solutions—such as k-anonymity, differential privacy, homomorphic encryption, or decentralized architectures—though partially mitigating risks, still rely on trusted third parties for anonymous set generation, key management, or query scheduling, leading to single points of failure, centralized trust, and potential misuse. Even decentralized proposals struggle to balance service quality with strong privacy guarantees, efficient verification, and lightweight deployment. To address this, this paper proposes a lightweight blockchain-based decentralized LBS privacy-preserving framework. This solution eliminates trusted intermediaries: users locally generate privacy-constrained fuzzy regions and construct zero-knowledge proofs (ZKPs) to cryptographically verify their actual locations within these regions. The proofs are submitted to blockchain smart contracts for public verification; only upon successful validation do distributed LBS nodes respond with candidate results, which are finalized through local user filtering. Theoretical analysis and experiments demonstrate that our framework effectively resists privacy inference from semi-honest service providers and external attackers, achieving a balance among query accuracy, response latency, and computational overhead. This provides a viable path for building secure, efficient, and user-centric LBS systems.
Shashank Motepalli, N. Garg, Gengrui Zhang, Hans‐Arno Jacobsen
Geospatial decentralization is essential for blockchains, ensuring regulatory resilience, robustness, and fairness. We empirically analyze five major Proof of Stake (PoS) blockchains: Aptos, Avalanche, Ethereum, Solana, and Sui, revealing that a few geographic regions dominate consensus voting power, resulting in limited geospatial decentralization. To address this, we propose Geospatially aware Proof of Stake (GPoS), which integrates geospatial diversity with stake-based voting power. Experimental evaluation demonstrates an average 45% improvement in geospatial decentralization, as measured by the Gini coefficient of Eigenvector centrality, while incurring minimal performance overhead in BFT protocols, including HotStuff and CometBFT. These results demonstrate that GPoS can improve geospatial decentralization {while, in our experiments, incurring minimal overhead} to consensus performance.
This work focuses on the study of distributed ledger applications, presenting proposals of fair and decentralised applications to counter scenarios in which centralisation of wealth and power are the norm. The first contribution is a novel architecture for a decentralised data market, in which participants crowd-source data and receive a fair share of the reward. The market is shown to be resilient against a number of adversarial behaviours. Subsequently, an algorithm to prove one's location is presented. This algorithm is a key component necessary to the functioning of the data market. In contrast to prior approaches, the design does not require assumptions of honest participation, nor dependence on an external ground truth to identify malicious actors. It is fully peer-to-peer, robust in highly adversarial settings, and compatible with privacy-preserving techniques. The security and reliability of the algorithm are evaluated empirically and characterised mathematically. The protocol is then generalised into a consensus mechanism applicable beyond location verification. An extended mathematical model is developed for this case, and its performance under varying operational conditions is systematically characterised. Finally, a study of governance vulnerabilities in Distributed Ledger Technologies is presented. This work provides a taxonomy of formalised properties necessary for good governance, solutions to implement them and an evaluation of how the absence of these cause severe vulnerabilities. The analysis is then extended to realm of Decentralised Autonomous Organisations (DAOs), which are a class of applications implemented on Distributed Ledger Technologies. The findings anticipated several governance exploits that later materialised, incurring losses in the scale of millions for multiple DAOs. Overall, this thesis aims to contribute to the technological development of distributed ledger applications with the goal of furthering social good, presenting architectures, algorithms, and governance properties that prioritise fairness, decentralisation, and resilience.
Decentralization has an important geographic dimension that conventional metrics, such as stake distribution, often overlook. Where validators operate affects resilience to regional shocks (e.g., outages, natural disasters, or government intervention) as well as fairness in reward access. Yet major blockchain protocols do not encode geographical location in their rules; instead, validator locations emerge from a combination of economic incentives, regulatory constraints, infrastructure availability, and validator deployment choices. When certain locations offer systematic advantages, validators may strategically co-locate to maximize expected rewards, as observed in Ethereum, where validators cluster along the Atlantic corridor, which exhibits favorable latency. In this paper, we propose a formal model of validators' geographical positioning incentives under Ethereum's protocol design, capturing the interaction between its two block-building paradigms, local and external block building, and the geographical distribution of validators and information sources. We analytically characterize the model under a mean-field approximation and complement this analysis with an agent-based simulation calibrated with real-world latency data to quantify how these incentives translate into geographical concentration under heterogeneous geographic and infrastructural conditions. Our results show that Ethereum's block-building architecture is not geographically neutral. Both paradigms generate location-dependent payoffs and incentives to relocate closer to payoff-relevant parties in order to reduce propagation delays, although through different underlying mechanisms. Asymmetric access to information sources further amplifies geographical centralization. We also demonstrate that consensus parameters, such as attestation thresholds and slot times, modulate latency sensitivity and can amplify these effects, acting as protocol-level levers. Finally, we discuss the implications of our findings for protocol design and outline potential mitigation directions informed by our analysis.
With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whiteboard content, and domain-specific multimedia archives. However, knowledge sharing across institutions is hindered by privacy risks, high communication overhead, and fragmented ownership of data. Federated learning promises to overcome these barriers by enabling collaborative model training without exchanging raw knowledge artifacts, but its success depends on motivating data holders to undertake the additional computational and communication costs. Most existing incentive schemes, which are based on non-cooperative game formulations, neglect unstructured interactions and communication efficiency, thereby limiting their applicability in knowledge-driven scenarios. To address these challenges, we introduce SC-NBTI, a smart contract and Nash bargaining-based incentive framework for federated learning in knowledge collaboration environments. We cast the reward allocation problem as a cooperative game, devise a heuristic algorithm to approximate the NP-hard Nash bargaining solution, and integrate a probabilistic gradient sparsification method to trim communication costs while safeguarding privacy. Experiments on the FMNIST image classification task show that SC-NBTI requires fewer training rounds while achieving 5.89% higher accuracy than the DRL-Incentive baseline.
In recent years, Femtech has emerged as a growing market category dedicated to women’s health technologies. Despite its rapid expansion, this relatively new and largely unregulated sector has experienced several concerning security breaches that compromise user privacy and intimacy. To address this critical gap between innovation and protection, we propose a novel blockchain-based consent management framework specifically designed for Femtech applications. Our solution leverages distributed ledger technology and smart contracts to create a transparent, immutable system where users can granularly control access to their sensitive health data.
Fatou Ndiaye Mbodji, Mame Marieme C. Sougoufara, Wendkûuni A. M. Christian Ouedraogo, Alioune Diallo · 7 authors
Smart contract comment generation has gained traction as a means to improve code comprehension and maintainability in blockchain systems. However, evaluating the quality of generated comments remains a challenge. Traditional metrics such as BLEU and ROUGE fail to capture domain-specific nuances, while human evaluation is costly and unscalable. In this paper, we present evalSmarT, a modular and extensible framework that leverages large language models (LLMs) as evaluators. The system supports over 400 evaluator configurations by combining approximately 40 LLMs with 10 prompting strategies. We demonstrate its application in benchmarking comment generation tools and selecting the most informative outputs. Our results show that prompt design significantly impacts alignment with human judgment, and that LLM-based evaluation offers a scalable and semantically rich alternative to existing methods.ResourcesVideo Demo: https://youtu.be/HXS_Yiszoz4Code and Data: https://anonymous.4open.science/r/SC_code_summarization-4653
Smart contracts are trustworthy, immutable, and automatically executed programs on the blockchain. Their execution requires the Gas mechanism to ensure efficiency and fairness. However, due to non-optimal coding practices, many contracts contain Gas waste patterns that need to be optimized. Existing solutions mostly rely on manual discovery, which is inefficient, costly to maintain, and difficult to scale. Recent research uses large language models (LLMs) to explore new Gas waste patterns. However, it struggles to remain compatible with existing patterns, often produces redundant patterns, and requires manual validation/rewriting. To address this gap, we present GasAgent, the first multi-agent system for smart contract Gas optimization that combines compatibility with existing patterns and automated discovery/validation of new patterns, enabling end-to-end optimization. GasAgent consists of four specialized agents, Seeker, Innovator, Executor, and Manager, that collaborate in a closed loop to identify, validate, and apply Gas-saving improvements. Experiments on 100 verified real-world contracts demonstrate that GasAgent successfully optimizes 82 contracts, achieving an average deployment Gas savings of 9.97%. In addition, our evaluation confirms its compatibility with existing tools and validates the effectiveness of each module through ablation studies. To assess broader usability, we further evaluate 500 contracts generated by five representative LLMs across 10 categories and find that GasAgent optimizes 79.8% of them, with deployment Gas savings ranging from 4.79% to 13.93%, showing its usability as the optimization layer for LLM-assisted smart contract development.
Crowd-sensing is considered a robust model for data collection, yet with challenges related to data availability and privacy. Traditional techniques such as data encryption and anonymization may not fully mitigate these issues, since anonymized data can still be traced back to individual users, and the volume of data generated can reveal user identities. This paper introduces a system that employs smart contracts and blockchain technology to manage crowd-sensing campaigns. The smart contract oversees user subscriptions, data encryption, and decentralized storage, creating a secure data marketplace. Incentive mechanisms within the smart contract promote user participation. Simulation results validate the system’s feasibility, emphasizing the importance of user engagement for data credibility and the impact of geographical data scarcity on rewards.
Résilience à la collusion dans les mécanismes de places de marché décentralisées Les places de marché décentralisées dans le Web3 cherchent à protéger leurs utilisateurs contre la censure, les biais et les points de défaillance uniques qui peuvent exister dans leurs homologues centralisés. Pourtant, certains mécanismes ont tendance à rester centralisés, par exemple le moteur de recherche permettant de découvrir de nouvelles ressources sur le marché. De telles vulnérabilités ont été exploitées sur des places de marché décentralisées ces dernières années : il est d'autant plus essentiel de fournir des mécanismes de protection. Dans cette thèse, nous proposons des protocoles pour assurer la fiabilité et l'équité des mécanismes des places de marché, notamment par la résilience à la collusion d'acteurs malveillants. Tout d'abord, pour traiter la sélection décentralisée d'un sous-ensemble de participants parmi une population comprenant des acteurs malveillant, nous proposons un protocole basé sur la blockchain pour éviter que les acteurs malveillants n'influencent la sélection à leur avantage. Ensuite, en considérant des ensembles de participants sélectionnés qui travailleront ensemble sur des tâches dans une place de marché décentralisée de ressources cloud, dans un environnement sans accès à des informations fiables ou non confidentielles, nous présentons un mécanisme d'incitation qui punit ou récompense collectivement les participants aux tâches en fonction du résultat de leurs tâches. Nous décrivons et évaluons également la manière d'atteindre un taux de réussite cible des tâches de la place de marché : l'algorithme que nous proposons est capable d'atteindre les objectifs définis et de réduire par 5 à 10 fois le taux d'échec par rapport à un système sans protection. Par ailleurs, nous montrons comment les fournisseurs du moteur de recherche d'une place de marché décentralisée peuvent favoriser un sous-ensemble d'utilisateurs du moteur de recherche. Nous protégeons ces moteurs de recherche avec notre protocole COoL-TEE, qui permet aux utilisateurs honnêtes d'éviter les fournisseurs malveillants de ce moteur de recherche, qui retardent de manière sélective les réponses au profit des utilisateurs qui les soudoient. Les utilisateurs honnêtes collaborent avec des environnements d'exécution de confiance (Trusted Execution Environment, TEE) au sein des machines hôtes des fournisseurs du moteur de recherche, afin de sélectionner des fournisseurs proches, rapides et honnêtes. A partir de simulations d'utilisateurs envoyant des requêtes depuis le monde entier à des fournisseurs géo-distribués hébergés dans des centres de données, nous illustrons comment COoL-TEE réduit l'avantage des utilisateurs malveillants à un niveau proche d'un scénario sans attaques. Enfin, de nombreux protocoles traditionnels et basés sur les TEEs requièrent des mesures temporelles fiables pour leur logique d'exécution, y compris COoL-TEE. Cependant, des attaquants qui contrôlent le système d'exploitation sont capables d'attaquer la perception du temps du TEE et, par conséquent, de manipuler les protocoles utilisant les mesures temporelles fournies. Nous contribuons une implémentation publique du protocole d'état-de-l'art Triad, dont le code source est fermé, et nous menons des attaques sur celui-ci de manière empirique. Sa calibration peut être manipulée pour affecter la vitesse d'horloge perçue par le TEE. En outre, les attaques sur une machine compromise peuvent se propager aux machines honnêtes participant au protocole de temps de confiance de Triad. Nous discutons comment atténuer ces vulnérabilités afin d'améliorer la résilience contre de telles attaques.
Decentralized marketplaces in Web3 aim to protect against censorship, bias, and single points of failure that may exist in their centralized counterparts. Still, some mechanisms tend to remain centralized, for example the search mechanism that enables discovery of new assets in the market. Such vulnerabilities have been exploited in live marketplaces in recent years: it is all the more essential to provide protection mechanisms. In this thesis, we propose protocols to uphold the reliability and fairness of marketplace mechanisms, notably through resilience against colluding malicious actors. First, to address decentralized selection of a subset of participants among a population comprising malicious actors, we contribute a blockchain-based protocol to avoid malicious actors swaying selection to their benefit. Then, considering selected sets of participants that will work together on tasks in a decentralized computing marketplace, in an environment with no access to trustworthy or non-confidential monitoring information, we present an incentive mechanism that collectively punishes or rewards task participants based on the outcome of their tasks. We also describe and evaluate how to meet a target success rate for the marketplace's tasks: our proposed algorithm is able to meet such targets and to reduce the failure rate by 5 to 10 times compared to an unprotected system. Additionally, we show how providers of a marketplace's search mechanism can favor a subset of search consumers, granting them an unfair advantage in accessing information about the most recent state of the market. We protect decentralized marketplaces' search with our protocol COoL-TEE, which enables honest search consumers to avoid malicious search providers, who selectively delay responses to benefit colluding consumers. Honest consumers collaborate with Trusted Execution Environments (TEEs) inside the host providers, in order to select close, fast, and honest providers. Using simulations of consumers sending search requests from around the globe to geo-distributed providers hosted in datacenters, we illustrate how COoL-TEE reduces malicious advantage close to a scenario without attacks. Finally, many TEE and traditional protocols rely on trustworthy time measurements for their execution logic, including COoL-TEE. However, attackers controlling the operating system are capable of attacking the TEE's time perception and, in turn, of manipulating the protocols depending on the timestamps. We contribute a public implementation of the state-of-the-art but closed-source protocol Triad and empirically showcase attacks. Calibration can be manipulated to affect the TEE's perceived clock speed. Furthermore, attacks on a compromised machine could propagate to honest machines participating in Triad's trusted time protocol. We discuss mitigations to these vulnerabilities for higher resilience against such attacks.