Counterfeit medicines, fragmented record management, limited end-to-end visibility, and dependence on centralized databases create security and traceability challenges in pharmaceutical supply chains. This article presents Pharma-Chain, a blockchain-enabled pharmaceutical supply chain management system developed to record medicine registration, stakeholder interactions, ownership transfer, and authenticity verification on an Ethereum blockchain. The system integrates a React.js frontend with Web3.js, MetaMask wallet authentication, Solidity smart contracts, Truffle deployment tools, and Ganache for local blockchain development and validation. Manufacturers register medicine batches with product and lifecycle information; distributors and retailers verify blockchain records before accepting and transferring ownership; and customers retrieve medicine details and transaction history before purchase. The implementation was tested module by module and as an integrated application in a local Ethereum environment. Testing confirmed successful authentication and role-based access, medicine registration, ownership transfer, transaction validation, blockchain record retrieval, and medicine verification. Each successful supply-chain operation generated a blockchain transaction record, providing an immutable ownership history and improving transparency and auditability. The project demonstrates a practical decentralized approach for secure pharmaceutical tracking while identifying QR-code verification, IoT monitoring, AI-assisted analytics, mobile access, and deployment on public or enterprise blockchain networks as future extensions.
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Blockchain Technology Applications and Security
Pharmaceutical Quality and Counterfeiting
Physical Unclonable Functions (PUFs) and Hardware Security
Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance. Recent Sybil detection methods increasingly use deep learning and treat blockchain activity as a quasi-linguistic sequence. However, complex sequence models are computationally expensive for real-time monitoring, and their reported performance may be inflated by label leakage from high-signal smart contracts. We ask whether and how organic users, Sybil bots, and MEV bots differ in the structural complexity of their transaction histories; whether sequential models outperform tree-based tabular models once leakage is reduced; whether transaction order or timing provides the stronger behavioral signal; and whether the resulting models are practical for low-latency deployment. Our approach to leakage-aware Sybil bot detection consists of a Blind-Spot protocol and a Transaction Grammar representation of wallet behavior. The former eliminates shortcuts associated with high-signal contracts, whereas the latter models wallets using rhythm, EVM execution structure, and intent. We evaluate this approach on Ethereum actor classification by comparing Transformer and BiLSTM sequence models against XGBoost and SVM baselines. We contribute a framework for leakage-aware Ethereum actor classification and a Transaction Grammar representation of wallet behavior. Our results demonstrate that, under leakage-aware evaluation, XGBoost outperforms Transformer-based sequence models while providing lower latency and estimated energy use.
Empirical work on algorithmic collusion asks one question of the data: are prices supracompetitive? We show this can be answered "no" by a conspiracy that is nonetheless profitable. Consider bidding agents that couple only through the joint distribution of their unexplained bid components, leaving every agent's own bid law exactly at the competitive law. Any test whose input is a single agent's price or bid history then has power exactly equal to its false-positive rate, for every coupling strength up to comonotonicity. The published detection methodology is therefore blind to this conduct by construction rather than underpowered, and no sample size repairs it. Three empirical results follow. First, the mechanism appears in real language-model agents: twenty models from nineteen independent developers, three deployment prompts each, show residual correlation of $+0.053$ between two deployments of one model against $+0.0001$ across models, with a 95% interval clustered by developer of $[0.030, 0.078]$, under an auditor that sees every order feature and is fitted out of sample. Second, the coupling falls monotonically as sampling temperature rises ($p=0.002$), turning a deployment parameter into a candidate mitigation. Third, on 24 days of Ethereum block-building auction data covering 77,684 bids from 39 bidders, the honest population of bidder pairs is itself so dependent that a screen held at a 5% false-positive rate must sit above a floor of $+0.50$ to $+0.81$, which is 20 to 32 times the family-wise sampling threshold and does not fall as the audit window grows. Since lawful multi-identity operation and conspiracy are behaviourally indistinguishable here, the tractable regulatory target is not detection but counting: resolving 40 bidding identities into 23 operators raises the Herfindahl index by 247.5%, and adding behavioural clusters from public bid streams reaches 324.5%.
Smart contract vulnerability detection requires evaluation protocols that separate real representation signal from dataset-specific artifacts. DIVE provides lifecycle-based tabular features for Ethereum smart contracts, but benchmark performance alone cannot show whether a dominant feature group is useful or only benefits from having many columns. This study examines Opcode Distribution features using 22,330 contracts, 397 processed features, and eight DASP-aligned vulnerability labels. Five multi-label learning configurations were evaluated under 3 x 5 repeated cross-validation, followed by global feature-group ablation, size-controlled random opcode ablation, per-label degradation analysis, cumulative stability analysis, and opcode-profile group-aware robustness checking. MultiOutput LightGBM achieved the best baseline performance, with Micro-F1 of 0.91396, Macro-F1 of 0.82464, and Macro-PR-AUC of 0.90146. Removing the full Opcode Distribution group reduced Macro-F1 to 0.78745, while removing a same-sized random opcode subset produced Macro-F1 of 0.82404. The findings indicate that Opcode Distribution acts as a collective predictive representation rather than a feature-count artifact, without implying causal vulnerability mechanisms.
This study investigates two questions relating to cryptocurrency market dynamics. First, whether a composite skew measure derived from MicroStrategy (MSTR) trading activity can predict future Bitcoin (BTC) and Ethereum (ETH) volatility. Second, whether Ethereum volatility exhibits reproducible structural properties consistent with established theories of volatility persistence and cascading shock dynamics. Using rolling out-of-sample testing, autocorrelation-adjusted significance testing, regime classification, shock-decay modelling, return-interval analysis, and earthquake-inspired cascade frameworks, the study finds no evidence that MSTR composite skew provides a useful forecasting signal. More broadly, no forecasting model tested outperforms naive benchmark models beyond horizons of approximately three to five days. However, several descriptive properties of Ethereum volatility appear robust, including volatility persistence, regime structure, extreme-event clustering, non-simple shock decay, and partially transferable aftershock dynamics. In particular, while Omori-style decay and the productivity law are supported, Bath's Law fails consistently, suggesting cryptocurrency volatility cascades may differ fundamentally from those observed in traditional financial markets. The findings contribute to the understanding of volatility organisation in digital asset markets while highlighting the difficulty of extracting persistent predictive signals from historical OHLCV
Sybil attackers are Blockchain actors that adopt the characteristics of regular users to exploit airdrops or influence governance. Current methods of Sybil actor detection include constructing graphs, which requires token transfers between examined wallets. Machine learning algorithms have been employed as well, but they treat the task as a closed-set classification problem, making them vulnerable to frequent changes in attack strategies or evasion tactics. We address the following questions: can compression-based similarity differentiate Sybil bots, organic users, and arbitrage bot wallets without direct financial links? What is the effect of high-signal contracts on the discovery of Sybils, and how robust are behavioral graphs under temporal drift and adversarial perturbations? Our approach synthesizes a symbolic Transaction Grammar from EVM (Ethereum Virtual Machine) traces, capturing separately transaction rhythm, execution structure, and functional intent. The high-signal contracts are filtered with our own protocol, called the Blind-Spot Protocol. Gzip-based NCD is used to construct a behavioral graph for Sybil discovery. We validate this framework against supervised machine learning baselines, a temporal split, and synthetic camouflage stress tests. Ultimately, we contribute a leakage-aware behavioral framework for Sybil candidate discovery. Its core NCD primitive requires no supervised training and can expand suspicious seed wallets without explicit funding links. We position the method as a training-free local discovery primitive for open-world blockchain audits, rather than as a formal open-set recognition system.
Relating low-level executable code to a high-level account of its behavior has been a central concern of programming-language research for decades. From formally verified compilers to translation validators, certifying compilers, and proof-carrying code, each approach chooses between laborious but foundational mechanized proofs and automation that costs completeness, generality, and an increased trusted base. Recently, large language models (LLMs) have begun to change the economics of formal verification. Agentic proof development is now capable of producing machine-checked proofs at a scale and speed that were previously out of reach. In this paper, we evaluate the capabilities of LLMs to produce foundational, machine-checked proofs of refinement between executable code and its high-level specification, as post hoc, per-artifact certificates. We study this in the context of the Ethereum Virtual Machine (EVM), a low-level virtual machine that executes smart contracts on the Ethereum blockchain. We build EquiVM, a foundational framework in Lean comprising an executable EVM semantics and a specification language that characterizes the intended behavior of smart contracts, but commits to no source language or compilation toolchain. In EquiVM, refinement is stated for deployed bytecode of arbitrary provenance, interaction with unknown code is part of the semantics, and each proof is a replayable, machine-checked certificate. No previous technique achieves this combination. Using frontier commercial LLMs, twenty-three real-world contracts are proved end to end with minimal human guidance, among them most of the MakerDAO stablecoin system, at up to a hundred million tokens and a hundred hours of proof time per contract. We conclude that foundational mechanized proofs can now be bought at the price of tokens, and that this shift can reshape how verification frameworks are architected.
Every widely followed Bitcoin cycle indicator (Pi Cycle, MVRV, Mayer, Puell) called turns precisely for a decade, then degraded in one sequence: precise, then early, then silent. This is one structural phenomenon. Across the four halving epochs (2011-2026), the per-cycle maxima of five top-calling oscillators decline monotonically while minima end higher, so any threshold calibrated on past cycles must stop firing; short-horizon indicators decay toward zero and several invert sign; yet Bitcoin's time structure stays fixed, with mature-cycle tops 525/546/534 days after their halvings and bottoms 406/364/366 days after their tops. Turns are identified retrospectively by a fixed mechanical rule, not a real-time record. Timing-free nulls put the joint clustering at 5e-6 to 1e-3 across every variant. A harder empirical null (block-bootstrapped paths under the identical rule) never reproduces the top cluster under its deterministic construction (0 of 10,000); the bottom cluster is largely intrinsic to the drawdown process (31-43% of paths), so the evidence concentrates in top phase-alignment. In block height (the exact 210,000-block unit) the top null stays 0 of 10,000 and partial bottom structure emerges; shape and volatility overlays do not improve. A causal power law in time-since-genesis (exponent near 5.6) is the only signal whose sign is stable across mature epochs, replicates on a second source and Ethereum, and whose timing edge over buy-and-hold turns positive in the current cycle (one holdout, suggestive not decisive). We rest nothing on per-epoch significance: a rotation null shows HAC inference over-rejects here (size 0.33 at nominal 0.05; p=0.21). Macro drivers (M2, yield curve) show the same instability and lose a joint horse race. We pre-register falsifiable windows: a 2026 bottom (Oct 5-Nov 16) and a next top 525-546 days after the following halving.
Tim K. Mackey, Alec J. Calac, Tiana McMann, Ken Miyachi · 10 authors
Background: Historic and ongoing problematic practices regarding the collection, storage, and use of Indigenous health data have led to the need to ensure principles of Indigenous Data Sovereignty (IDS) are followed in research practices and technology development. Objective: This project, a partnership between UC San Diego and the Native BioData Consortium (NativeBio), sought to explore the practical application of blockchain technology and its potential to facilitate Indigenous-led research collaboration. Methods: This project first undertook purposeful relationship building with NativeBio to form a Community Advisory Board (CAB) for identifying community and technology needs for a blockchain research collaboration platform with an initial focus on genomic data. Over a 2-year project period, a series of public meetings and presentations at Indigenous-led conferences introduced the concept of exploring compatibility between blockchain and IDS principles, followed by iterative prototyping and co-design of a blockchain platform with NativeBio, using Ethereum as the underlying protocol. Results: Direct engagement with NativeBio and the CAB informed the initial design and development of a "b-IDS" proof-of-concept (POC) blockchain platform. The POC consists of three main components: (1) the web front-end layer, (2) the Ethereum network that executes the smart contract and blockchain storage aspects of the framework, and (3) the back-end database that stores off-chain interactions and data for future use with external genomic data repositories. After refinement of the POC, a community-based participatory research (CBPR) use case aligned with IDS principles was identified as a practical workflow and incorporated into the design of the POC for implementation. Conclusions: The findings from this project demonstrated the potential use of operationalizing IDS through blockchain technology with proactive and sustained engagement with Indigenous partners. Blockchain technology may have certain advantages over other data governance approaches and systems, facilitating timely oversight, shared decision-making and consent structures, and direct involvement of Indigenous communities in technology design, respecting the core principles of IDS and CBPR. Future development of the blockchain-IDS POC will need to incorporate other research practices and ethics frameworks to expand its use to other public health and biomedical research use cases.
Industrial supply chains involve multiple stakeholders, complex logistics operations, and financial transactions that require transparency, traceability, and secure coordination.Traditional supply chain systems suffer from limited transparency, the risk of data manipulation, and insufficient trust among participants.To address these challenges, this paper proposes a decentralized industrial supply chain management system implemented on an Ethereum-compatible blockchain network.The proposed architecture integrates smart contracts to automate workflows, including stakeholder registration and verification, multi-item order processing, shipment tracking, simulated delivery verification (SDV), and escrow-based conditional payment settlement.The system adopts a hybrid on-chain/off-chain storage architecture in which transactional records are maintained on-chain, while raw material and product images are stored off-chain using the InterPlanetary File System (IPFS).This design reduces blockchain storage overhead while preserving data integrity through cryptographic hash references.To improve operational efficiency and reduce overhead from repeated transactions, the proposed system supports multi-item batch transactions during procurement and ordering, while the logistics and settlement stages maintain per-item execution to preserve traceability and accountability.Experimental evaluation was conducted on the Celo Sepolia network to measure gas consumption and transaction fees for both batch-based and functionally equivalent per-item execution workflows under controlled conditions.The evaluation included multiple predefined workload configurations, and statistical analysis using mean and standard deviation was performed to assess execution stability.The results indicate that transaction aggregation reduces gas consumption by approximately 40-43% for raw material order creation and by 40-48% for raw material operations (addToMultipleCart).Product aggregation workflows also demonstrated measurable gas-efficiency improvements.These findings demonstrate the efficiency benefits of multi-item transaction aggregation within the proposed implementation while preserving lifecycle traceability and escrow-enabled settlement correctness.The reported results represent controlled implementation-level efficiency measurements within the proposed blockchain-based supply chain architecture.
Decentralized manufacturing faces a pre-contractual impasse: a Provider cannot price a service accurately without inspecting the design file, yet the Consumer cannot share that file without exposing intellectual property. We introduce the Optimistic Verifiable Claim (OVC), a blockchain protocol that lets a Consumer publish a verifiable claim about a concealed design (such as the material it consumes) and a Provider price and bid on it without seeing the design. The claim is committed when the service is posted and stands unless the selected Provider challenges it; a challenge triggers a deterministic on-chain check that exposes any dishonesty, and the design is disclosed only to settle a dispute, never on the honest path. We implement four checks (authorized key access, delivery-channel integrity, syntactic conformance, and declared material consumption) in Solidity and measure them on a real 6.41 MB G-code file, the 3DBenchy, across Ethereum, Arbitrum, and opBNB. Every service incurs the cost of posting the encrypted design, with or without a dispute. For the 3DBenchy, the no-dispute outcome costs \$7,207 in up to 9 hours on Ethereum, \$288 in 3 min on Arbitrum, and \$2.87 in 2 min on opBNB, and a fully contested dispute costs \$49,660 in up to 57 hours on Ethereum, \$1,988 in 19 min on Arbitrum, and \$19.73 in 13 min on opBNB. Costs and times grow with size: for a 50 MB industrial design, an undisputed service reaches \$56,173 and up to 3 days on Ethereum against \$22.36 and 16 min on opBNB, and a fully contested dispute reaches \$488,440 over up to 18 days on Ethereum against \$195 and 1.6 hours on opBNB. Of the four, the material-consumption check is the costliest, its predicate being the most expensive to evaluate on-chain. OVC makes confidential, claim-based bidding economically feasible on Arbitrum and opBNB, but not on Ethereum at industrial scale.
Ethereum, one of the largest blockchain platforms, manages millions of dollars in digital assets daily and depends on consistent community standards and cohesive implementations to maintain network consensus. This study introduces a novel methodology for identifying and measuring temporal inconsistencies in opcode mappings across Ethereum clients and the Solidity compiler. Using this methodology, we uncover significant and previously undocumented discrepancies both among different components of Ethereum’s implementation and between theoretical specifications and practical execution behavior. We further demonstrate the potential consequences of these inconsistencies by illustrating their impact on the detection of vulnerabilities in Solidity smart contracts. Such discrepancies can substantially undermine the accuracy and reliability of vulnerability analysis, highlighting the critical need for strict consistency in Ethereum’s operational semantics. Ensuring this consistency is essential for preserving transaction security and the overall robustness of the platform.
Vabuk Pahari, B. Chandrasekaran, Johnnatan Messias, Krishna P. Gummadi · 5 authors
A decentralized autonomous organization (DAO) is a governance entity that allows its stakeholders to manage blockchain-based protocols through smart contracts. The DAO explicitly specifies how stakeholders make and enforce decisions concerning a protocol's operation in a smart contract, aptly referred to as its governance contract. The design of this governance contract, therefore, has far-reaching implications for the security (trust) and privacy (transparency) of the smart contracts managed by the DAO and its stakeholders. In this work, we (i) explicate the trust and transparency trade-offs of the design choices in implementing a DAO and (ii) highlight how poor choices introduce critical vulnerabilities, using real-world examples as case studies. To this end, we analyze $48$ public, actively used Ethereum-based DAOs that control a vast capital. We classify the design choices into a handful of key dimensions that succinctly capture how a DAO's stakeholders initiate a protocol change, vote on it, and, based on the voting outcome, execute that change. Our analyses crucially uncover a new class of attacks, which we call governance attacks, that directly exploit the fundamental design of a DAO's governance mechanisms, even if we assume bug-free implementations.
Blockchains rely on transparency and immutability to ensure trust, but these guarantees come at the cost of an ever-growing ledger that increasingly threatens decentralization by making it more expensive to store and maintain the full transaction history. In this work, we introduce state-invariant transactions, defined as transactions whose inclusion or removal does not affect the resulting blockchain state beyond transaction fees. We argue that these transactions constitute a form of on-chain spam because they consume execution, bandwidth, storage, and blockspace without contributing to the final ledger state. We present the first large-scale measurement of state-invariant transactions across Ethereum, Optimism, and Base, identifying nearly 1.4 billion such transactions. While only 2.6% of Ethereum transactions are state-invariant, they account for 24% of transactions on Optimism and 37% on Base, representing a significant source of unnecessary resource consumption on Layer-2 blockchains. We show that speculative Maximal Extractable Value (MEV) is the dominant source of state-invariant transactions on Optimism and Base, accounting for 57% and 68%, respectively, but is not the only source as previously assumed. Moreover, despite its popularity, speculative MEV is not the most profitable strategy once the costs of state-invariant transactions are considered. Beyond MEV, we identify substantial malicious activity, with address poisoning campaigns accounting for 53% of non-reverted state-invariant transactions on Ethereum. Our findings suggest that mitigating state-invariant transactions could substantially reduce blockchain resource consumption and transaction costs while limiting phishing campaigns and other forms of blockchain abuse.
Abstract A secure platform for exchanging and storing agricultural data is provided via a blockchain-powered framework. By integrating edge computing, blockchain technology, and the Internet of Things (IoT) the production of crops can be boosted while using fewer natural resources. In the sector of agriculture, sensors and equipment gather various data about the landscape, which can subsequently be delivered to a server in a cloud environment. Due to its extreme fragility, these data must be securely stored and guarded from unwanted access. The core aim of this work is to propose a hybrid Reconditioned Random value-based Wombat Optimization with Adaptive Multi-scale Vision Transformer-based EfficientNet (RRWO-AMViT-ENet) model integrated with Ethereum smart contracts for secure pest detection and smart irrigation in IoT environments. The gathered agricultural images are initially stored and managed using the Ethereum blockchain. Then, node authentication is performed using the Smart Contract-based Adaptive Deep Support Vector Machine (SC-ADSVM). A Reconditioned Random value-based Wombat Optimization (RRWO) is utilized to optimize the variables of the developed SC-ADSVM. In order to perform pest detection and smart irrigation, the Adaptive Multi-scale Vision Transformer-based EfficientNet (AMViT-ENet) is used. The proposed model is implemented on the IP102-Dataset, where it obtained an accuracy of 96.39% in the pest detection operation. Thus, the proposed model provides effective results for pest detection and the smart irrigation process. From the attained results, it is concluded that the recommended strategy can provide intelligent service to the farmer.
Chibueze Ubochi Nwamouh, Okereke Aru-Eze, Wilson C. Ahiara, Isaac Adewale Samuel
One potential remedy for the problems with online voting methods is blockchain technology. Online voting has several benefits, such lower costs and higher voter turnout, but its broad adoption has been hampered by security and manipulation issues. With features like non-repudiation and end-to-end verification, blockchain&s;s decentralized and secure architecture provides an alternative to electronic voting. In addition to examining the present status of research and development in this area, this article gives an overview of blockchain-based electronic voting systems. The objective is to pinpoint current issues and predict upcoming developments in blockchain-based voting. Paper clarifies the core features of blockchain technology that are pertinent to voting and highlights the conceptual framework of blockchain-based electronic voting systems. According to the report, blockchain technologies might help with some of the problems that conventional election systems have. Transaction speed and privacy protection, however, are two significant obstacles that stand out. Blockchain-based electronic voting systems must be implemented sustainably, which requires resolving scalability issues with transaction speed and guaranteeing the security of distant participation. Current frameworks must be improved in order to properly utilize blockchain technology in voting systems. This entails strengthening privacy safeguards and figuring out ways to speed up transactions. Blockchain-based electronic voting systems have the potential to be a safe and effective alternative for next elections if these issues are resolved.
Blockchain technology provides a decentralized and secure data-management system. However, PoW consensus remains vulnerable to the risk of 51% attacks, where a single miner or pool controls over half of the network and can manipulate blocks and execute double-spending. This paper proposes a defense mechanism called Consensus-Level Restriction (CLR) for Ethereum Classic. It limits the number of sequential blocks from a single miner to reduce the feasibility of 51% attacks. We evaluated the effectiveness of CLR by using BlockSim simulator. Results show that CLR reduces the success rate of 51% attacks while maintaining a decentralized structure. This work strengthens blockchain security without compromising PoW principles.
The aim of this study is to analyze the price dynamics of blockchain-based carbon credit tokens, namely Base Carbon Tonne (BCT), Moss Carbon Credit (MCO2), and KlimaDAO (KLIMA) as well as mainstream crypto assets such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Solana (SOL) and the speculative asset Carboncoin (CARBON). In addition, the Fear & Greed Index, which represents investor sentiment, has been incorporated into the model in line with the role of sentiment-driven effects in price formation processes in cryptocurrency markets, as highlighted in the literature. The study utilized daily closing prices from the period October 21, 2021, to November 1, 2025; correlation analyses were performed on raw daily price series using the Pearson correlation method, which was chosen to examine the direction and strength of the linear relationship between variables. Prior to modeling, the dataset was cleaned, Min-Max normalization was applied, and it was split into a 70% training set and a 30% test set while preserving chronological integrity. While the assumption of stationarity in time series is important from the perspective of classical econometric approaches, this study focuses on deep learning-based methods within the scope of nonlinear modeling frameworks. The data used in the study were obtained from Yahoo Finance and the AI Key API. The findings indicate that there are strong internal linkages among carbon credit tokens. In particular, while a strong positive relationship was observed between BCT and MCO2, it was determined that these tokens exhibit a weak negative correlation with Bitcoin. This suggests that carbon credit tokens are only marginally linked to the broader crypto market but form a more cohesive structure within their own ecosystem. Additionally, it was observed that the CARBON asset exhibits relationships ranging from weak to moderate with major crypto assets. The Fear & Greed Index, meanwhile, showed moderate relationships with BTC, ETH, and SOL, and weaker relationships with carbon credit tokens. During the modeling process, LSTM, GRU, Transfer-LSTM, and Transfer-GRU architectures were used; the data was split into 70% training, 30% validation, and 30% test sets while maintaining chronological integrity; the models were evaluated using MSE, RMSE, MAE, MAPE, and R² metrics. The results show that the GRU architecture generally offers the highest prediction accuracy, while transfer learning models perform relatively better in predictions for the KLIMA and Fear & Greed (F&G) Index. Overall, the study demonstrates that deep learning and transfer learning approaches are effective in modeling price behavior in tokenized carbon credit markets. Here, it is assessed that transfer learning does not automatically provide an advantage in every scenario, but offers strategic contributions for specific asset groups. In conclusion, the study demonstrates that AI-based models can be used as a decision-support mechanism in the pricing of sustainable financial instruments in the digital economy.
Abstract: Product traceability within supply chains is a critical concern that has gained increasing attention in recent years. One promising solution to this problem is the use of Public Blockchain (PBC) technology, which offers an immutable, transparent, and decentralized ledger for storing key information such as ownership transfers and distribution records. These blockchain-based systems significantly enhance traceability by ensuring that data, once recorded, cannot be altered. However, a key challenge arises from the fact that information stored on public blockchains is freely accessible to anyone, potentially exposing sensitive distribution data. In this paper, I introduce a novel method that preserves the privacy of distribution data while maintaining high traceability in supply chain systems leveraging PBC. The proposed method utilizes encryption to safeguard sensitive data and Zero-Knowledge Proofs (ZKPs) to allow supply chain participants to authenticate themselves without revealing private information, such as their blockchain addresses. The solution was implemented using Ethereum smart contracts and evaluated for its cost-effectiveness. The results show that the transaction fee per supply chain participant is capped at USD 2.6, demonstrating that the approach is practical for real-world deployment.
Reshma D’Souza, S Sheela, H S Sameena, S Jyothi · 6 authors
Abstract: In this era where technology is used to create unidentical counterfeit products, Finding the original objects is a very tedious task for the users. These Counterfeit Products affect the health of the user in the case of medical and skin care products also. This project implements Blockchain, a new Technology which is used to overcome this problem. Blockchain technology is the distributed, and immutable technology that provides data consistency and security. Here a QR code is generated for each product that is linked to the database which in turn is mapped to the chain nodes. By scanning this QR code the user can detect the original products amongst the fake ones. It highlights the need of cryptocurrency in the broader vision of supply chain security, elaborating on how Blockchain network, particularly using Ethereum Framework, provides a decentralized and transparent ledger for tracking and validating products.
In Ethereum, transaction inclusion is rarely in question; what matters is the delay until inclusion. Currently, block builders could exercise censorship across consecutive blocks, threatening time-critical applications, such as on-chain auctions. To mitigate this risk, existing proposals such as FOCIL, scheduled for deployment in late 2026, assign a committee to list transactions for mandatory inclusion. However, no committee member is held accountable for the actual inclusion of the transactions: an adversary can bribe the entire committee to omit any transaction for less than 2 Euro per block under current conditions. We argue that accountability, i.e., requiring all exclusion decisions to be publicly disclosed and verifiably complete, with violations attributable to a specific party, substantially raises censorship costs. To this end, we propose Fair Forward Inclusion Lists (FairFIL) as an accountable censorship resistance mechanism for Ethereum. In FairFIL, every builder must publish all transactions the builder chooses to censor, subject to a protocol-anchored policy; a committee verifies the completeness and validity of this disclosure. The subsequent builder must include these transactions, forfeiting the full block reward upon any omission. Therefore, under FairFIL, extending censorship beyond a single slot requires an assembler to forfeit a full block reward. We show that compliance is rational for all participants within our behavior model. Our empirical evaluation on Ethereum mainnet indicates that multi-block censorship costs one order of magnitude more than under existing proposals, while leaving the builder's MEV extraction freedom largely intact. Initial measurements further suggest that the mempool consistency FairFIL requires is met in practice.
Direct user-specified research topic: Autonomous agent wallets spend under English mandates like 'only stablecoin swaps under $200 daily, never bridge, never touch unaudited pools', yet deployed policy engines (Safe Transaction Guards, ERC-7579 modules, session-key allowlists) enforce only stateless numeric and selector limits and cannot express 'unaudited' or 'per day', while a naive base-model prompt over raw hex calldata cannot recover function, recipient or token flow and confabulates verdicts. Evaluate a tool-augmented structured-decoding LLM judge that fetches ABIs from Sourcify and Etherscan, decodes calldata including multicall and Permit2 payloads, simulates via eth_call state overrides for token-flow and approval deltas, attaches counterparty features (contract age, verification), and emits constrained JSON: in_policy, violated_clause quoted verbatim, offending_calldata_field. Read Ethereum and Base: ERC-20 Transfer/Approval logs, Uniswap/1inch routers, Across/Stargate bridges, Permit2 at 0x000000000022D473030F116dDEE9F6B43aC78BA3. Measure macro-F1 and clause-attribution precision on 600 hand-labeled mandate/transaction pairs plus replay accuracy on transactions whose approvals owners later revoked, beating a naive raw-hex prompt and a Safe Guard numeric-allowlist baseline. Deliver as the prototype a minimal runnable Python MCP server (stdio) exposing the priced AI tool screen_transaction_against_mandate that invokes a language or ML model over onchain data to produce its output, with a typed input/output schema, an x402-style pay-per-call metering stub that records a per-call price in USDT and emits a settlement receipt, and one smoke test that exercises the tool end to end.. Investigate this topic end-to-end: survey the state of the art, identify a concrete tractable research question within it, design and run an experiment, and report results.
Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
In this chapter, you will learn about the privacy limitations of public blockchains such as Bitcoin and Ethereum, and how these limitations have led to the development of privacy-focused cryptocurrencies. You will study the motivations for privacy coins and the risks posed by government-issued digital currencies. The chapter introduces and compares three major privacy coins: Zcash, Dash, and Monero. For each, you will explore their underlying technologies, including zero-knowledge proofs (zk-SNARKs), CoinJoin-style mixing, and ring signatures with RingCT and stealth addresses. You will also learn about consensus protocols, supply models, and the trade-offs each project makes between privacy, usability, and scalability. Finally, you will analyze the comparative strengths and weaknesses of these systems and understand the broader implications of privacy on blockchains.