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.
This chapter investigates tokenization as an innovative mechanism for managing educational research projects. Rooted in blockchain technology, it provides for transparent tracking, recognition, and motiving of individual contributions via fungible and non-fungible tokens. The proposed model integrates smart contracts (to automate much of the work), collaborative governance, and real-time monitoring. Case studies like BitDegree and ResearchHub illustrate how tokenization can enhance fairness, engagement, and accountability, and we address a number of ethical, technical, and pedagogical issues, too. These are important because they're about aligning educational research with its core values
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 solutionsâsuch as k-anonymity, differential privacy, homomorphic encryption, or decentralized architecturesâthough 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.
ABSTRACT Objective Collaborative edge computing (CEC) addresses the service quality issues that arise from the limited resources of a single node in traditional edge computing architectures by integrating resources from multiple edge nodes. However, ensuring reliable task offloading in this collaborative environment remains a significant challenge. Existing solutions often struggle to balance the intelligence and trustworthiness of offloading decisions effectively. This imbalance can lead to poor performance and reduced task success rates, especially if tasks are offloaded to malicious nodes. Methods To tackle these challenges, this paper proposes a trustâenabled decentralized task offloading scheme that combines blockchain technology and deep reinforcement learning (DRL). First, we introduce a blockchainâbased reputation mechanism within the CEC architecture to facilitate trusted collaboration among nodes, utilizing smart contracts for reputation management. Next, we propose a beta distributionâbased threeâfactor reputation update (BTRU) algorithm to enhance the accuracy of reputation evaluation. Finally, we present a decentralized and trustâenabled task offloading (DTTO) algorithm based on DRL, which uses onâchain reputation data to guide agents in learning trustworthy task offloading policies, thereby maximizing offloading trustworthiness and task success rates. Result To thoroughly assess the effectiveness and practicality of our proposed scheme, we develop a testbed for CEC task offloading based on Kubernetes and Ethereum. Experimental results demonstrate that the BTRU algorithm effectively distinguishes malicious nodes, reducing their average reputation by 97.54%, with an improvement of 9.94% compared to competitive algorithms. Meanwhile, the DTTO algorithm significantly enhances the efficiency and reliability of task offloading, raising the task success rate by at least 3.04%, especially when the proportion of malicious nodes reaches 40%, its task success rate is at least 5.41% higher than that of competitive algorithms. Conclusion The proposed trustâenabled decentralized task offloading scheme successfully combines blockchainâbased reputation management with DRL to achieve both intelligent and trustworthy task offloading in the CEC environments. The experimental validation confirms the scheme's effectiveness in identifying malicious nodes and improving task success rates under various system conditions.
Shashank Motepalli, N. Garg, Gengrui Zhang, HansâArno 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.
Jaeheon Kwak, Jaeuk Lee, Hyoji Ha, Haewon Kim · 6 authors
We present a visual analytics system to increase proposal approval likelihood in Proof-of-Stake(PoS) blockchain governance. The proposed system introduces the Contextual Alliance Index(CAI), a similarity metric reflecting contextual information based on proposal voting data. Through heatmaps and bubble heap graphs, users can explore alliances among validators and support comparison and prioritization of persuadable validators. Furthermore, a case study illustrates an analytical process for understanding voting patterns for each proposal, and for identifying alliances and the prioritization of persuadable validators when drafting new proposals. This study is expected to contribute to the in-depth analysis of proposal patterns and the development of effective proposal strategies by identifying validator alliances in PoS blockchain governance.
This paper presents CommitFit, a blockchain-based fitness platform designed to enhance user engagement and data security through financial incentives and decentralized attestation protocols. The architecture integrates modular layers comprising a secure frontend, AI-driven backend verification, and Ethereum smart contracts for automated staking and non-fungible token (NFT) rewards. Empirical evaluation demonstrates significant improvements in user retention and goal completion rates compared to traditional systems, with notable accuracy in AI-based activity verification. The platform addresses critical challenges such as privacy, scalability, and integration with wearable devices. Comparative analysis with existing fitness solutions highlights CommitFit's advancements and future potential for broader applications in secure, decentralized health data management.
Citizen Science involves the engagement of public in scientific research to augment and disseminate scientific knowledge. This nurtures the practice of sharing and contributing to data gathering and monitoring activities with the participation of scientific communities and general public. It incorporates idea generation through community formation and invites scientists for leadership, guidance and coordination. Existing citizen science programs are centralized; however, such endeavors may be supported through decentralized autonomous communities realized through blockchains and smart contracts adding transparency and security to the projects. Blockchain is a shared immutable distributed ledger technology that addresses double spending and Byzantine Generalâs Problem and enables peer to peer digital payments in absence of intermediaries. It has several applications beyond financial sector or cryptocurrencies and one such utility is the smart contract that entails a piece of script that will be automatically executed if certain conditions are fulfilled, in other words, it is a self-executing digital contract. In this paper, we propose a novel colored blockchain technique to develop decentralized autonomous communities leveraging which citizen science can be perceived in a decentralized context. We present the details of the framework that uses such colored blockchain technology to implement decentralized citizen science concept. We also propose an alternate decentralized application leveraging smart contract to implement decentralized citizen science concept.
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.
Blockchain has been increasingly adopted in various sectors to support transparent and tamper-proof data management. In the academic world, however, reputation systems remain centralized and often fail to represent the true contributions of researchers. Previous innovations such as Dynamic Smart Contracts (DSC) have enabled more flexible interaction models on blockchain, allowing logic and reward schemes to be updated without redeploying contracts. Building on this foundation, this paper introduces a blockchain-based reputation and incentive system tailored for the Decentralized Science (DeSci) ecosystem. The system records academic contributions such as publications, peer reviews, and experimental data into smart contracts that dynamically compute and update reputation scores. Each interaction is validated and permanently stored on-chain, enabling traceable, contribution-based recognition independent of centralized academic institutions. A series of tests conducted on the Ethereum testnet demonstrate that the system operates reliably, supports dynamic rule updates, and effectively tracks contribution-based reputation. This approach enhances transparency and fairness in scientific evaluation, strengthens community-driven validation, and supports the broader vision of DeSci by aligning incentives with openness, accountability, and verifiability.
In recent years, Femtech has emerged as a growing market category dedicated to womenâs 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.
Ruben Fernando Cuadros Mieses, Adolfo Jorge Prado Ventocilla, Edwin Jorge Montes Eskenazy
Traditional university research funding is frequently undermined by high transaction costs, bureaucratic friction, and information asymmetries that erode donor trust. Blockchain technology, particularly through smart contracts, offers a new paradigm for transparent and efficient fund management. This study investigates the practical viability of this paradigm by developing a decentralized funding platform and conducting a rigorous comparative performance analysis of two leading blockchain infrastructures: Ethereum's Sepolia testnet (a Layer 1 analogue) and Polygon's Amoy testnet (a Layer 2 solution). In 50 consecutive executions per network, the smart contract maintained 100% success on both, yet with Polygon maintaining an average confirmation time of 2.1 seconds versus Sepolia's 5.3 seconds. Furthermore, a preliminary usability study (N=20) confirms that users perceive the Polygon-based platform as significantly higher in performance and overall satisfaction. These findings provide robust quantitative and visual evidence that Layer 2 scaling solutions are not only viable but essential for creating decentralized applications that meet the practical requirements of speed, cost-efficiency, and positive user experience in domains like academic funding. This work contributes a critical empirical benchmark for the emerging field of Decentralized Science (DeSci) and offers a validated architectural model for transparent, automated, and globally accessible research financing.
Xinyu Zhang, Zerui Chen, Xinhua Cui, Ze Yang · 5 authors
Delegation learning, as a privacy-preserving machine learning paradigm, has been widely applied in large-scale data processing and complex computational tasks in recent years. However, existing schemes still have deficiencies in guarding against malicious behavior and verifying result correctness, and they struggle to balance security and efficiency across different adversary models. In this paper, We propose a rational delegation learning smart contract scheme (SCS-RDL). First, it combines game theory to construct a rational delegation learning framework and introduces a blockchain-based probabilistic verification method. Second, we construct a smart contract scheme and design rational utility functions that effectively incentivize participantsâ honest behavior. Finally, experiments demonstrate that SCS-RDL scheme could enhance delegation learningâs training efficiency without sacrificing accuracy and could satisfy public verifiability.
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.
The increasing use of localization devices for location-based services has led to an explosion in user location data. This raises significant privacy concerns that often conflict with the need for identification and accountability in critical scenarios like criminal investigations or public health emergencies. Research is facing the challenge of balancing privacy with data utility, guaranteeing trust in verification. This paper proposes a novel blockchain-based solution to reconcile the conflicting requirements of user privacy and accountability in localization. Our scheme leverages the transparency and immutability of blockchain to record verifiable location proofs. To ensure user privacy against routine disclosure, the solution integrates elliptic curve cryptography and Zero-Knowledge Proofs, allowing a verifier to confirm a user's presence without revealing sensitive information. Our solution also prevents the verifier from disclosing proof of a user's past presence to third parties, further enhancing privacy. Moreover, the proposed system provides a mechanism for accountability, allowing a designated authority to override privacy safeguards and access location data when legally mandated for public interest reasons, thereby reconciling privacy and identification needs.
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 systemâs feasibility, emphasizing the importance of user engagement for data credibility and the impact of geographical data scarcity on rewards.