The administration of data, banking, food science, the Internet of Things, and cybersecurity is just a few of the businesses and professions that benefit from blockchain technology. Even healthcare and brain research have shown a growing interest in blockchain technology. There has been an incredible uptick in curiosity about how blockchain technology may be put to use to improve the delivery of trustworthy healthcare data management. In addition, blockchain is transforming conventional healthcare methods to more trustworthy means, allowing for more precise diagnosis and more efficient treatment thanks to the secure sharing of patient data. Blockchain has the potential to be a technology that aids in providing tailored, authentic, and secure healthcare in the future by centralizing all of a patient’s up-to-date clinical data and presenting it in a modern, safe healthcare environment. In this chapter, investigators examine the recent and ongoing changes brought about by the introduction of blockchain technology to the healthcare industry. Investigators also talk about the blockchain’s potential uses, as well as its current limitations and potential in the future.
KrishnaBhargavi Yerraganti, D. Balasubrahmanyam, G. Venkat Vamsi, Raja Bhaiya Rajbhar · 5 authors
Centralized traditional social media platforms are controlled by entities that monetize user data, restrict content visibility, and erode privacy. In contrast, current blockchain-based social media platforms are decentralized but inefficient in moderating content, resulting in the proliferation of misinformation, objectionable content, and security threats. This paper aims to bridge this gap by developing a decentralized social media platform that ensures user privacy, content moderation, and scalability without relying on centralized control. To achieve this, the system takes advantage of IPFS for decentralized file collection and Ethereum-compatible smart contracts for authentication and content verification. A major innovation of this platform is the use of an on-device-operated Natural Language Processing (NLP) model for material filtering and moderation at the user level, ensuring that no user data is collected or centrally processed. The platform will support safe text messaging, image sharing (public/private accounts), and an explore page for the discovery of public content, while all users give full control over their data. The expected results are a scalable, censorship-resistant and privacy-centric social media network, where users maintain their content ownership while AI ensures a safe digital environment.
Student elections in African universities are frequently marred by violence and fraud, thereby undermining democratic participation. This paper presents the first blockchain-based voting system specifically designed for student elections at Cheikh Anta Diop University (UCAD) in Dakar, Senegal. The proposed hybrid architecture combines the Ethereum blockchain to ensure vote immutability, MySQL for user data management, and IPFS for decentralized document storage. The system integrates multi-layer biometric authentication (facial recognition and WebAuthn), an eight-phase automated smart contract, and PySpark for real-time blockchain data analysis. Implementation results demonstrate 100% voting accuracy with the automatic generation of tamper-proof electoral records. The storage of IPFS hashes on the blockchain guarantees document integrity while optimizing storage costs. This solution effectively addresses the recurring electoral violence at UCAD while establishing a reproducible framework for democratic modernization within African higher education institutions.
Abstract. The Bitcoin blockchain is a distributed ledger of transactions maintained by a network of nodes. The protocol assumes that honest nodes control a majority of the network's computing power. Conventional wisdom suggests that a minority group cannot earn revenue disproportionate to its hashing power, implying that the rational strategy is to remain honest. However, Eyal and Sirer (2013) challenged this view by introducing "Selfish Mining," a strategy that enables a minority pool to earn rewards exceeding its share of computing power. This paper replicates the original study using Monte Carlo simulations to verify the threshold at which this attack becomes profitable. The results confirm that a pool controlling more than 1/3 of the network hashrate can theoretically achieve higher returns than honest mining.
In blockchain systems operating under the Proof-of-Stake (PoS) consensus mechanism, fairness in transaction processing is essential to preserving decentralization and maintaining user trust. However, with the emergence of Maximal Extractable Value (MEV), concerns about economic centralization and content manipulation have intensified. To address these vulnerabilities, the Ethereum community has introduced Proposer Builder Separation (PBS), which separates block construction from block proposal. Later, enshrined Proposer Builder Separation (ePBS) was also proposed in EIP-7732, which embeds PBS directly into the Ethereum consensus layer. Our work identifies key limitations of ePBS by developing a formal framework that combines mathematical analysis and agent-based simulations to evaluate its auction-based block-building mechanism, with particular emphasis on MEV dynamics. Our results reveal that, although ePBS redistributes responsibilities between builders and proposers, it significantly amplifies profit and content centralization: the Gini coefficient for profits rises from 0.1749 under standard PoS without ePBS to 0.8358 under ePBS. This sharp increase indicates that a small number of efficient builders capture most value via MEV-driven auctions. Moreover, 95.4% of the block value is rewarded to proposers in ePBS, revealing a strong economic bias despite their limited role in block assembly. These findings highlight that ePBS exacerbates incentives for builders to adopt aggressive MEV strategies, suggesting the need for future research into mechanism designs that better balance decentralization, fairness, and MEV mitigation.
Meenal R. Kale, Yogesh Mehta, Kathari Santosh, A. Annie Lotus · 6 authors
In fast-moving business environments, timely and reliable service delivery is required, although the traditional methods of verification are seldom accountable and transparent. Veritime addresses these issues through an automated verification system based on blockchain, smart contracts, and IoT sensors. It enables secure delivery verification, automated payment upon successful delivery, and real-time tracking of shipment by using cryptographic passphrases from Ethereum contracts and IoT-enabled containers. The key elements in Veritime involve the sender, receiver, blockchain network, IoT sensors, and the MQTT server. Developed in Python, Veritime topped the benchmark for performance and delay in power efficiency and packet delivery compared to traditional systems. Gas cost analysis showed that functions like “Register Manufacturer” and “Assign Distributor” consume 47,335 and 56,789 transaction gas, confirming the efficiency and reliability of the system.
ABSTRACT Over the years, numerous efforts have been undertaken to accurately forecast traffic conditions and thereby preventing additional congestion. However, existing crowd management techniques focus on recognizing and counting the crowd while leaving the security of crowd information. A typical crowd management system is centralized and faces challenges, such as contributor selection reliability, fair payment evaluation, privacy concerns and high deployment costs. This study investigates security concerns in crowd management and evaluates the potential of blockchain technology to improve crowd management security. Combining the power of blockchain (decentralization and security) and smart contracts, this work proposes a secure crowd management architecture named . The framework operates on blockchain, utilizes cryptographic algorithms, and incorporates reputation management along with credit distribution through smart contracts. effectively safeguards crowd data while its revenue structure entices users to actively contribute to the system. has been simulated on GoQuorum's Ethereum private blockchain, using elliptic curve signatures for secure and efficient processing. Its performance was tested with RAFT, PoA and IBFT consensus mechanisms where RAFT led in throughput, IBFT lagged and PoA offered a middle ground. PoA stands out for balancing scalability and security, supporting network growth while preserving identity‐based validation and data integrity.
This thesis advances privacy-preserving solutions essential for addressing contemporary technological challenges in smart cities, decentralized systems, and algorithmic decision-making processes. Firstly, we introduce a hybrid sensing framework integrating Internet of Things (IoT) sensors and crowdsensing techniques to overcome limitations inherent in traditional methods. The hybrid sensing model incentivizes voluntary user contributions to complement fixed-location IoT sensors, ensuring reliable and comprehensive data collection while maintaining user anonymity through a privacy-preserving protocol. We implement this model in a smart parking application, demonstrating significant improvements in data accuracy and user engagement. Secondly, we propose a decentralized anonymous crowdsourcing system leveraging blockchain technology, which removes reliance on centralized intermediaries, thereby enhancing transparency and mitigating biases. Our system integrates anonymous payments using the Zerocoin protocol framework, eliminating the need for worker identity registration and trusted setups, thus fostering genuinely anonymous participation. Empirical analyses confirm that our approach maintains practical efficiency in transaction verification and moderate blockchain gas costs. Lastly, we tackle fairness and transparency in algorithmic decision-making processes, addressing public concerns regarding inherent biases and opaque computational practices. We develop a privacy-preserving, publicly verifiable framework that combines succinct zero-knowledge proofs with blockchain infrastructure, allowing independent verification of algorithmic fairness without exposing sensitive inputs or decision-making algorithms. Our concrete instantiation employs a restricted KZG polynomial commitment scheme alongside the Sonic zk-SNARK protocol, demonstrating small proof sizes, efficient verification, and practical deployment feasibility. Collectively, this thesis contributes significantly to the field by providing robust, scalable, and privacy-conscious technologies tailored for contemporary smart city applications and decentralized computational ecosystems.
Ensuring efficient task assignment and secure payment in mobile crowdsensing while preserving worker location privacy remains a challenging problem. Existing solutions either rely on expensive encryption schemes, employ blockchain-based verification that incurs high computational and gas costs, or use differential privacy techniques that degrade spatial accuracy. This paper introduces the Privacy-preserving Task Assignment and Payment (PTAP) framework, a lightweight solution built upon secure multi-party computation (SMPC). PTAP employs additive secret sharing and a challenge-response mechanism across three semi-honest servers to achieve anonymous task allocation and payment without blockchain or zero-knowledge proofs. The framework guarantees full unlinkability between worker identities, task locations, and payment records while maintaining accurate location-based assignment and supporting traceability for dispute resolution. Experimental evaluation using the MP-SPDZ framework demonstrates scalability to over 1.5 million workers and 7 million payment tokens. The average end-to-end completion time is approximately 35.4 seconds, with zero gas cost. Compared to the state-of-the-art AVeCQ system [15], which requires about 13 minutes and 37 MWei per transaction on the Goerli network for only 1,024 users. The results confirm PTAP's efficiency, scalability, and strong privacy guarantees for large-scale mobile crowdsensing deployments.
Lorenzo Fogli, Alberto Montaner, Apostolos Kapetanios, Ioannis Mandourarakis · 8 authors
This paper presents the ENPOWER Flexibility Marketplace Toolkit, an open-source platform for peer-to-peer trading of energy flexibility within energy communities. Using energy consumption and production time-series data, participants can publish flexibility needs, submit offers, and verify delivery against measured baselines. Transactions are settled automatically through blockchain smart contracts with collateral enforcement, while an Energy Data Space backbone governs data exchange, ensuring sovereignty and policy-controlled sharing. Non-fungible tokens provide immutable, auditable certificates of each fulfilled flexibility commitment. The toolkit covers the complete trading lifecycle from market creation and participant onboarding through offer matching, delivery verification, and financial settlement.
Bitcoin's block reward is scheduled to decline to zero, raising concerns about whether the network can remain secure once miners rely solely on transaction fees. This paper seeks to identify the conditions under which large-scale and persistent deviation from honest mining can arise. We analyze and compare the payoffs of honest and deviating miners in a sequential decision model, and identify a deviation threshold $G_t$ at which honest mining ceases to be privately optimal. Around the 2024 Bitcoin halving, we show that current mining behavior does not exhibit large-scale or structural deviation. However, when the block reward is removed, the $G_t$ criterion implies that deviation can arise even with a very small fraction of transaction fees. Finally, we evaluate three protocol-level mechanisms: Base Fee, Fee Floor, and an adaptive maximum block size rule, and show that their combination raises the deviation threshold and mitigates incentive breakdown in a fee-only regime. These results provide a practical benchmark for assessing Bitcoin's security as block rewards disappear.
This study introduces a novel approach to walk-forward optimization by parameterizing the lengths of training and testing windows. We demonstrate that the performance of a trading strategy using the Exponential Moving Average (EMA) evaluated within a walk-forward procedure based on the Robust Sharpe Ratio is highly dependent on the chosen window size. We investigated the strategy on intraday Bitcoin data at six frequencies (1 minute to 60 minutes) using 81 combinations of walk-forward window lengths (1 day to 28 days) over a 19-month training period. The two best-performing parameter sets from the training data were applied to a 21-month out-of-sample testing period to ensure data independence. The strategy was only executed once during the testing period. To further validate the framework, strategy parameters estimated on Bitcoin were applied to Binance Coin and Ethereum. Our results suggest the robustness of our custom approach. In the training period for Bitcoin, all combinations of walk-forward windows outperformed a Buy-and-Hold strategy. During the testing period, the strategy performed similarly to Buy-and-Hold but with lower drawdown and a higher Information Ratio. Similar results were observed for Binance Coin and Ethereum. The real strength was demonstrated when a portfolio combining Buy-and-Hold with our strategies outperformed all individual strategies and Buy-and-Hold alone, achieving the highest overall performance and a 50 percent reduction in drawdown. A conservative fee of 0.1 percent per transaction was included in all calculations. A cost sensitivity analysis was performed as a sanity check, revealing that the strategy's break-even point was around 0.4 percent per transaction. This research highlights the importance of optimizing walk-forward window lengths and emphasizing the value of single-time out-of-sample testing for reliable strategy evaluation.
We develop a model of aberrant behavior by Bitcoin miners and test it with a new 2017-2025 dataset.Miners' rewards, comprised partly of user fees, exhibit variability across blocks of transactions.When large reward disparities exist between adjacent blocks, miners have incentives to attempt alternative versions of prior blocks and claim other miners' rewards for themselves.Regression analysis shows that fee differentials are associated with these attacks and longer waiting times between blocks.These patterns imply potential destabilization of the Bitcoin blockchain as future mining rewards become more volatile due to gradual withdrawal of fixed block subsidies.
With the continuous development of blockchain technology, its applications have expanded into a wide range of fields. The consensus algorithm serves as the core of blockchain, with its performance directly influencing the overall efficacy of the blockchain system. Delegated Proof of Stake (DPoS) selects block producers through elections and offers advantages such as high performance, low energy consumption, and strong scalability. However, the election mechanism also brings several challenges, including vote bribery, low voter participation, and high degree of centralisation. To address these issues, we propose an improved DPoS algorithm based on the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) decision-making method, named BKT-DPoS, which enhances the consensus mechanism from a new perspective of multi-attribute decision-making. Specifically, a dynamic balanced clustering algorithm is introduced to constrain the voting range of certain nodes; the voting results are transformed into node influence scores using complex network theory; and the historical performance of nodes is dynamically assessed based on block production outcomes. A TOPSIS model is constructed to select the final block producers by considering node influence and historical behaviour values as decision attributes. After each round, the behavioural values of nodes are updated to incentivise honest nodes and penalise malicious ones. We conducted extensive simulations on networks ranging from 200 to 5,000 nodes over 1,00 to 10,000 rounds, and performed a comparative analysis against other improved algorithms.Experimental results demonstrate that the proposed algorithm significantly improves decentralisation, enhances resistance to vote bribery, and effectively mitigates the impact of malicious nodes.
Nirmala Raju Kanti, D. G. Narayan, Pooja Shettar, P S Hiremath
Blockchain networks rely on consensus mechanisms to maintain security and efficiency. Delegated Proof of Stake (DPoS) is a widely adopted alternative to traditional Proof of Work (PoW) and Proof of Stake (PoS) due to its scalability. However, DPoS suffers from centralization risks, collusion, and the presence of unreliable validators, compromising network integrity. One approach to mitigating these issues is the downgrade method, which reduces the influence of dishonest validators over time by penalizing their stake or voting power. While this approach improves security, it still faces challenges related to manipulation and inefficiency. To address these limitations more effectively, we introduce Game Theory-based Delegated Proof of Stake (GT-DPoS), an advanced consensus framework that integrates strategic decision-making through game theory to optimize node selection and incentivization. GT-DPoS utilizes a two-stage evaluation mechanism based on Reputation Score (RS) and Trust Score (TS) to regulate node behavior, penalize malicious actors, and reward honest participation. In Stage 1, nodes are assessed based on RS, incorporating factors like stake, transaction efficiency, block contribution, and past misconduct. Nodes failing to meet the threshold are penalized, while eligible ones advance to second stage. Stage 2 evaluates TS, considering rewards, penalties, and community votes, refining the selection through a payoff-based model that ensures rational decision-making. Unlike the downgrade method, GT-DPoS provides a more dynamic and adaptive approach, ensuring continuous security enhancement without long-term inefficiencies. Experimental results demonstrate that GT-DPoS achieves faster block creation times compared to conventional DPoS, with up to 4.6% improvement at lower transaction loads and 1.2% at higher loads. By leveraging game-theoretic principles, GT-DPoS enhances decentralization, reduces transaction latency, and strengthens network security, making it a more effective and performance-optimized alternative to both traditional DPoS and downgrade-based approaches
Decentralized Autonomous Organizations (DAOs) are digital communities that make collective decisions by voting on blockchain communication-systems, amongst other coordination media. "Token voting" is intended to, on the one hand, efficiently synthesize individuals' limited information toward collective intelligence, on the other, it is presented as an inclusive approach to the governance of organizations-allowing any member with tokens to voice their opinions. In this latter sense, DAO decision-making is described as 'decentralized governance,' a misnomer, as most DAOs exhibit extreme centralization in token allocation. Yet, this paper finds value in DAO's collective decision-making intentions: the core blockchain infrastructure, when paired with a DAO's collective voting, produces an immutable, peerproduced ledger of group decision-making. Much empirical and theoretical attention has focused on consensus, understood as the majority-supported outcome of a vote. This paper argues that such a focus is incomplete. Consensus is often treated as a signal of a collective's certainty in a chosen outcome, yet this certainty depends on the structure of the decision space. The same majority share can imply different levels of certainty depending on the number of available options and how support is distributed across non-winning alternatives. As such, collective decisions cannot be evaluated by majority size alone, but must be understood relative to how a community allocates support across the full set of choices. To this end, this paper proposes the Rényi family of entropy-based measures, along with several derived metrics, as tools to map the topology of consensus and disagreement. It applies these metrics to voting data from 277 DAOs to signal (1) whether dissenting opinions converge into coordinated blocks of collective opposition or disperse pluralistically across the decision space, and (2) the effective number of choices and relative weight of substantively engaged alternatives within the choice set. The former heuristic qualifies the type of disagreement to ask "did disagreement converge into viable minority alternatives?" The latter heuristic quantifies the number of options that were seriously considered, and to what degree?" Altogether, this paper contributes a formal information-theoretic framework for representing the topology of collective decision-making through the distribution of support across all alternatives. It provides heuristics that treat a community's decision-making quality as a design question and enables majority outcomes to be interpreted relative to how support is distributed across the full set of choices, rather than in isolation.