The proliferation of Non-Fungible Tokens (NFTs) has revolutionized digital asset ownership and trading, creating unprecedented opportunities for creators and collectors. However, existing NFT marketplaces face significant challenges, including limited user discovery mechanisms, inadequate recommendation systems, security vulnerabilities, and poor user experience design. This paper presents a new way to run an NFT marketplace using Blockchain and Artificial Intelligence. The system keeps everything secure by storing asset information on a distributed online ledger. With built-in AI, it helps users find content they'll like by giving personalized suggestions. It also uses multiple authentication steps to make sure the marketplace stays safe for everyone. The design uses decentralized storage through the InterPlanetary File System (IPFS). It employs smart contract automation for transaction processing and incorporates machine learning algorithms for fraud detection and user behavior analysis. We demonstrate the effectiveness of our approach with implementation results that show improved user engagement, reduced transaction costs, and better security compared to traditional NFT platforms. The system achieves a 47% improvement in user retention and a 63% increase in successful transactions through personalized recommendations. This research contributes to the growing field of blockchain-based digital asset management and provides a scalable framework for next-generation NFT marketplaces.
Cloud storage uses proofs of ownership to avoid redundant uploads while keeping file contents secret. Many existing schemes need extra round trips, or rely on predictable sampling. These choices reduce security when an adversary knows part of the file. We present MiS-PoW, a zero knowledge and non-interactive proof of ownership. The protocol derives a synchronized challenge seed from the existing HTTPS/TLS session. The seed binds a discretized time window and the file identifier. Both parties compute the same challenges locally, and the protocol adds no new messages. MiS-PoW samples blocks with a stratified policy without duplicates. The policy enforces coverage across partitions and reduces the advantage of contiguous knowledge and near duplicate files. The proof layer uses STARKs with simple AIR constraints. The constraints check that indices come from the seed, lie in range, are unique, and meet per partition counts. We analyze security and show seed unpredictability, resistance to replay, and bounds under partial knowledge with limited grinding. A prototype shows that verification time does not grow with file size, and proof and bandwidth costs remain modest. MiS-PoW is deployable, privacy preserving, and scalable for cloud storage.
To address the limitations of blockchain data storage capacity and uneven dis-tribution, this paper proposes a Chord dual-ring distributed storage method based on virtual nodes. Building upon the original Chord protocol, this approach introduces virtual rings to construct a “storage ring-virtual ring” dual-ring structure. Target virtual nodes are located through routing table lookups, and data is distributed across the storage ring via a name mapping mechanism. Sim-ulation experiments validate the proposed scheme's effectiveness by evalu-ating load balancing and query success rate across varying sharding granulari-ties. Results demonstrate that this approach not only efficiently achieves shard-ed storage for blockchain data but also ensures balanced distribution of block data.
The InterPlanetary File System (IPFS) has been successfully established as the de facto standard for decentralized data storage in the emerging Web3. Despite its decentralized nature, IPFS nodes, as well as IPFS content providers, have converged to centralization in large public clouds. Centralization introduces BGP routing-based attacks, such as passive interception and BGP hijacking, as potential threats. Although this attack vector has been investigated for many other Web3 protocols, such as Bitcoin and Ethereum, to the best of our knowledge, it has not been analyzed for the IPFS network. In our work, we bridge this gap and demonstrate that BGP routing attacks can be effectively leveraged to censor content in IPFS. For the analysis, we collected 3,000 content blocks called CIDs and conducted a simulation of BGP hijacking and passive interception against them. We find that a single malicious AS can censor 75% of the IPFS content for more than 57% of all requester nodes. Furthermore, we show that even with a small set of only 62 hijacked prefixes, 70% of the full attack effectiveness can already be reached. We further propose and validate countermeasures based on global collaborative content replication among all nodes in the IPFS network, together with additional robust backup content provider nodes that are well-hardened against BGP hijacking. We hope this work raises awareness about the threat BGP routing-based attacks pose to IPFS and triggers further efforts to harden the live IPFS network against them.
Hao Cheng, Georgios Fotiadis, Johann Großschädl, Daniel Page
Non-degenerate bilinear maps on elliptic curves, commonly referred to as pairings, have many applications including short signature schemes, zero-knowledge proofs and remote attestation protocols. Computing a state-of-the-art pairing at the 128-bit security level, such as the optimal ate pairing over the curve BLS12-381, is very costly due to the high complexity of some of its sub-operations: most notable are the Miller loop and final exponentiation. In the past ten years, a few optimized pairing implementations have been introduced in the literature, but none of those took advantage of the vector (SIMD) extensions of state-of-the-art Intel and AMD CPUs, especially AVX-512; this is surprising, because doing so offers the potential to reach significant speed-ups. Consequently, the questions of 1) how computation of the optimal ate pairing can be effectively vectorized, and 2) what execution time such a vectorized implementation can achieve are still open. This paper addresses said questions by introducing a carefully-optimized AVX-512 implementation of the optimal ate pairing on BLS12-381. A central feature of the implementation is the use of 8-way Integer Fused Multiply-Add (IFMA) instructions, which are capable to execute eight 52 x 52-bit multiplications in a SIMD-parallel fashion. We introduce new vectorization strategies and describe optimizations of existing ones to speed up arithmetic operations in the extension fields Fp4 , Fp6 , and Fp12 as well as certain higher-level functions. Furthermore, we discuss some parallelization bottlenecks and how they impact execution time. We benchmarked our pairing software, which we call avxbls, on an Intel Core i3-1005G1 (“Ice Lake”) CPU and found that it needs 1, 265, 314 clock cycles (resp. 1, 195, 236 clock cycles) for the full pairing, with the Granger-Scott cyclotomic squaring (resp. compressed cyclotomic squaring) being used in the final exponentiation. For comparison, the non-vectorized (i.e., scalar) x64 assembly implementation from the widely-used blst library has an execution time of 2, 351, 615 cycles, which is 1.86 times (resp. 1.97 times) slower. avxbls also outperforms Longa’s implementation (CHES 2023) by almost the same factor. The practical importance of these results is amplified by Intel’s recent announcement to support AVX10, which includes IFMA instructions, in all future CPUs.
Ethereum enables the creation and execution of decentralized applications through smart contracts, that are compiled to Ethereum Virtual Machine (EVM) bytecode. Once deployed in the blockchain, the bytecode is immutable; hence, ensuring that smart contracts are bug-free before their deployment is of utmost importance. A crucial preliminary step for any effective static analysis of EVM bytecode is the extraction of the control-flow graph (CFG): this presents significant challenges due to potentially statically unknown jump destinations. In this paper we present a novel approach, based on Abstract Interpretation, aiming to build a sound CFG from EVM bytecode smart contracts. Our analysis, which is implemented in our static analyzer EVMLiSA, is based on a parametric abstract domain that approximates concrete execution stacks at each program point as an l -sized set of abstract stacks of maximal height h ; the results of the analysis are then used to resolve the jump destinations at jump nodes. Furthermore, EVMLiSA includes a checker for reentrancy detection, working on the constructed CFG. Our experiments show that, by fine-tuning the analysis parameters, EVMLiSA is able to build sound CFGs for all real-world smart contracts in the considered benchmark suite. Moreover, EVMLiSA successfully detects all reentrancy vulnerabilities in EVM bytecode smart contracts, while producing a small number of false positives.
This research explores the distributed database security storage and access control scheme based on IPFS and blockchain for the privacy issues such as sensitive data leakage and account security under the rapid development of Internet technology. The research background focuses on the contradictory status quo of data value enhancement and black-market data trading in the fields of intelligent medical care and unmanned driving, etc. Although the existing database security technology has made progress in encryption algorithms, dynamic protection, etc., it is still faced with the challenges of performance bottleneck and fine-grained access control of centralized architecture. The research aims to integrate the advantages of IPFS distributed storage and the tamper-proof characteristics of blockchain to construct a new type of secure storage system. Through theoretical analysis of IPFS peer-to-peer file system architecture, blockchain six-layer model (data layer, network layer, consensus layer, etc.) and AES/SM4 encryption algorithms, a system solution integrating blockchain smart contract and IPFS storage is designed: SM4 encrypts the original data and then stores it in IPFS, and achieves traceability through the blockchain record hash, and introduces the proxy re-encryption based on the identity technology to Realize dynamic access control. Experiments comparing the performance of MongoDB and IPFS show that in 5000 transactions, the delay of IPFS mode 12 nodes is reduced by 1.71 times compared with 6 nodes, which is significantly better than that of MongoDB's by 1.22 times; in the throughput test, IPFS increases linearly with the increase of nodes, while MongoDB decreases after the peak value. The study confirms that the combination of IPFS and blockchain can effectively reduce transaction latency by 31%, improve throughput by 30%, and safeguard the security of the whole data lifecycle through cryptographic technology. The results provide a decentralized security framework for distributed databases, with both theoretical innovation and engineering application value, which is of great practical significance for highly sensitive data fields such as healthcare and finance.
We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities. Current centralized platforms lack mechanisms for proper attribution, quality assurance, or fair compensation for prompt creators. PromptChain addresses these limitations through a novel integration of IPFS for immutable storage, smart contracts for governance, and token incentives for community curation. Our design includes: (1) a comprehensive metadata schema for cross-model compatibility, (2) a stake-weighted validation mechanism to align incentives, and (3) a token economy that rewards contributors proportionally to their impact. The proposed architecture demonstrates how decentralized systems could potentially match centralized alternatives in efficiency while providing superior ownership guarantees and censorship resistance through blockchain-anchored provenance tracking. By decoupling prompts from specific AI models or outputs, this work establishes the foundation for an open ecosystem of human-AI collaboration in the Web3 era, representing the first systematic treatment of prompts as standalone digital assets with dedicated decentralized infrastructure.
Victoria L. Lemieux, Rosa Gil, Faith Molosiwa, Qizheng Zhou · 8 authors
As archives turn to artificial intelligence to manage growing volumes of digital records, privacy risks inherent in current AI data practices raise critical concerns about data sovereignty and ethical accountability. This paper explores how privacy-enhancing technologies (PETs) and Web3 architectures can support archives to preserve control over sensitive content while still being able to make it available for access by researchers. We present Clio-X, a decentralized, privacy-first Web3 digital solution designed to embed PETs into archival workflows and support AI-enabled reference and access. Drawing on a user evaluation of a medium-fidelity prototype, the study reveals both interest in the potential of the solution and significant barriers to adoption related to trust, system opacity, economic concerns, and governance. Using Rogers' Diffusion of Innovation theory, we analyze the sociotechnical dimensions of these barriers and propose a path forward centered on participatory design and decentralized governance through a Clio-X Decentralized Autonomous Organization. By integrating technical safeguards with community-based oversight, Clio-X offers a novel model to ethically deploy AI in cultural heritage contexts.
Ethereum blockchain uses smart contracts (SCs) to implement decentralized applications (dApps). SCs are executed by the Ethereum virtual machine (EVM) running within an Ethereum client. Moreover, the EVM has been widely adopted by other blockchain platforms, including Solana, Cardano, Avalanche, Polkadot, and more. However, the EVM performance is limited by the constraints of the general-purpose computer it operates on. This work proposes offloading SC execution onto a dedicated hardware-based EVM. Specifically, EVMx is an FPGA-based SC execution engine that benefits from the inherent parallelism and high-speed processing capabilities of a hardware architecture. Synthesis results demonstrate a reduction in execution time of 61% to 99% for commonly used operation codes compared to CPU-based SC execution environments. Moreover, the execution time of Ethereum blocks on EVMx is up to 6x faster compared to analogous works in the literature. These results highlight the potential of the proposed architecture to accelerate SC execution and enhance the performance of EVM-compatible blockchains.
Patrick Spiesberger, Jan Droll, Hannes Hartenstein
In the Ethereum system, the exclusion of specific transactions is currently feasible with minimal effort due to a power imbalance among entities. This censorship opportunity threatens the dependability of time-sensitive services deployable on Ethereum. In this paper, we look at this threat from an access control perspective and attribute it to a lack of accountability for censorship, a lack of policy definition and enforcement, as well as to the lack of disincentivization of policy violation. We propose an approach to enforceable policies in Ethereum. Furthermore, we demonstrate how a specific policy can address the shortcomings of existing censorship mitigation techniques, particularly Inclusion Lists. Under the assumption that block assemblers are unwilling to incur significant financial penalties as well as that the local view on the network messages is sufficiently consistent, the proposed approach guarantees the inclusion of a transaction in a block within 27 seconds in a non-saturated network. The empirical validation of sufficiently consistent views on outstanding transactions is currently in progress.
Abstract—File storage platforms face inherent challenges such as censorship, limited transparency, vulnerability to single points of failure, and restricted user control over data. To address these limitations, this paper proposes a decentralized file-sharing system that integrates the Ethereum blockchain with the InterPlanetary File System (IPFS). Our design leverages smart contracts to securely manage file metadata and enforce access controls, providing an immutable and tamper-resistant record of data ownership and permissions. IPFS is utilized for efficient, distributed file storage, enhancing scalability and availability. User authentication is handled through wallet-based cryptographic verification, eliminating reliance on centralized identity providers. Additionally, the system supports micropayment- based monetization via smart contracts, enabling direct and transparent transactions between content creators and consumers. The proposed platform delivers a secure, censorship-resistant, and user-empowered file-sharing environment consistent with the principles of Web3. Keywords- Blockchain, IPFS, Smart Contracts,
The European Union's (EU) data strategy aims to create a single market for seamless data flow while ensuring proper governance, privacy, and data protection. In this paper, we present SEDIMARK, an EU project, that builds on this strategy by developing a fully decentralised, secure data marketplace. The goal of SEDIMARK is to build a complete toolbox that enables users to purchase and process data assets. The toolbox includes tools for data cleaning, decentralised machine learning models and secure data exchange. SEDIMARK offers users full control over data assets by enabling them to keep their data locally and thus removing the need for central servers. With customisable pipelines and tools, SEDIMARK supports a wide range of users, from novices to experts, promoting seamless collaboration and fair access to high-quality datasets across Europe. The decentralised connectivity in SEDIMARK is achieved with the use of Distributed Ledger Technology (DLT). Furthermore, SEDIMARK's architecture features a unique Connector component using Self Sovereign Identities (SSI), fostering trust and secure interactions. Transactions in SEDIMARK are stored in a Registry, a decentralised, immutable, non-repudiable and permissionless database. Together the technologies used in SEDIMARK ensure privacy, trust and data quality for secure management, sharing, and monetisation of assets in data spaces.
By bringing digital artworks to one’s everyday shopping list, NFTs have revolutionized a large group’s understanding of art. In this article, I challenge the stance presented by authors such as Taylor and Sloane that NFTs shift the consumption of objects to the liquid consumption of the non-material by drawing on the aesthetic identity of NFTs as images. Firstly, I analyse the evolution of blockchain products’ function from that of strictly fungible tokens to non-fungible goods. NFTs rely on art’s specificity and acquire their new identity by virtue of entering the art market, which does not happen with ordinary cryptocurrencies. NFTs thus benefit from the contextualization of art as a socially and technologically mediated immaterial process: they emerge as instances of the artification of a non-artistic immaterial experience and become art-like. However, an NFT as a commodity is not pure code; it does have an image attached. This distinguishes NFTs from particular fungible tokens that hold added value due to their unique historical context. I further point out that the fact that NFTs have, as commodities, taken the form of digital images is an expression of a pervasive need for interaction with objects in today’s everyday digital experience. Also, the shift of NFTs from being just “specific” phenomena as art objects to “singular” phenomena construed as authors of the ways in which they will be perceived and understood is discussed. NFTs create their own impact, on the one hand, independently of their often poor artistic or aesthetic quality, and on the other hand, as entirely dependent on their aura as mathematically and ontologically unique, visually perceptible entities.
Abstract Digital identity data management is a significant challenge for businesses and individuals who wish to interact online in the increasingly digital economy, government, and society. Most recently, non-fungible tokens (NFTs) have been proposed as a solution to represent digital identities and underlying data. NFTs are stored on a blockchain based data management system and seem useful for representing digital identity data stored in independent data systems. The idea of using NFTs to represent digital identities and their associated data is promising, but it also raises significant concerns regarding data privacy and compliance. This article examines the NFT-enabled digital identity data representation and management, highlighting associated privacy risks and mitigation strategies. This study employs a qualitative desk research approach, reviewing industry reports, academic papers, and policy documents to analyze trends, technological advancements, and regulatory considerations. It concludes with recommendations for leveraging programmable privacy to address these challenges, providing valuable insights for researchers and practitioners in privacy-preserving digital identity and NFT-enabled identity management.
Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks.Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements.Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures.To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method.Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure.Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16KB or more, outperforming state-of-the-art methods. CCS Concepts Security and privacy File system security; Management and querying of encrypted data.
This paper explores the dynamics of multibranch forging algorithms within Proof-of-Stake (PoS) systems, focusing on the tails switching effect and its associated risks, such as the Nothing-at-Stake (N@S) attack. The study introduces a series of modifications to the traditional single-branch approach, drawing inspiration from quantum mechanics to enhance system security and efficiency. By analyzing the impact of smooth and direct measure functions on the cumulative measure of blockchain branches, the paper demonstrates that direct measures effectively reduce tails switching and stabilize the network. Additionally, the introduction of single-branch nodes is shown to stabilize the best chain, reducing the likelihood of history rewrites. The results suggest a promising pathway for improving multibranch PoS systems, emphasizing the need for further investigation into delayed propagation, transaction processing, and measure combinations.
Esam Mahdi, Carlos Martín-Barreiro, Xavier Cabezas
In this article, we introduce a novel deep learning hybrid model that integrates attention Transformer and Gated Recurrent Unit (GRU) architectures to improve the accuracy of cryptocurrency price predictions. By combining the Transformer's strength in capturing long-range patterns with the GRU's ability to model short-term and sequential trends, the hybrid model provides a well-rounded approach to time series forecasting. We apply the model to predict the daily closing prices of Bitcoin and Ethereum based on historical data that include past prices, trading volumes, and the Fear and Greed index. We evaluate the performance of our proposed model by comparing it with four other machine learning models: two are non-sequential feedforward models: Radial Basis Function Network (RBFN) and General Regression Neural Network (GRNN), and two are bidirectional sequential memory-based models: Bidirectional Long-Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The performance of the model is assessed using several metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), along with statistical validation through the nonparametric Friedman test followed by a post hoc Wilcoxon signed rank test. The results demonstrate that our hybrid model consistently achieves superior accuracy, highlighting its effectiveness for financial prediction tasks. These findings provide valuable insights for improving real-time decision making in cryptocurrency markets and support the growing use of hybrid deep learning models in financial analytics.
With the rapid growth of the Internet of Things (IoT), maintaining data integrity, confidentiality, and authentication is now an imperative challenge. Most conventional cryptographic solutions cannot satisfy the specific constraints of IoT environments, which include limited computational resources, energy efficiency, and scalability. This study proposes a lightweight hybrid cryptographic framework combining Authenticated Encryption with Associated Data (AEAD) and Verifiable Random Functions (VRF) with Elliptic Curve Digital Signature Algorithm (ECDSA). The hybrid framework is intended to offer robust data integrity, secure authentication, and efficient encryption mechanisms with minimal computational overhead. Our solution makes use of AEAD (AES-GCM or ChaCha20-Poly1305) in order to establish both confidentiality and integrity within a single encryption process and with much less processing time than in traditional approaches such as AES-CTR with HMAC. Use of VRF guarantees that cryptographic algorithms result in verifiable randomness that increases replay attack and unauthorized entry security. ECDSA is utilized for lightweight digital signatures, providing non-repudiation without the computational overhead being higher than RSA-based integrity mechanisms. To ensure the efficacy of our methodology, we performed thorough benchmarking tests comparing AEAD + VRF + ECDSA with conventional cryptographic methods like AES-CTR + HMAC and integrity verification based on RSA. It is revealed by our benchmarks that our hybrid solution considerably cuts down encryption time, minimizes CPU utilization, and maximizes memory usage, thus being very suitable for resource-poor IoT devices. In contrast to AES-CTR + HMAC, which needs independent encryption and authentication phases, AEAD's hybrid approach has the least storage footprint and computational overhead. Furthermore, avoiding a dedicated verification step (necessary in HMAC-based designs) adds to system responsiveness. Our work adds to the literature through a scalable, effective, and secure cryptographic framework optimized for IoT use cases such as secure messaging, sensor data encryption, and access control in distributed systems. Real-world deployment in IoT platforms, post-quantum cryptographic augmentation, and implementing zero-knowledge proofs (ZKPs) for improved privacy-preserving authentication are next steps. By solving major problems in IoT security, our hybrid approach provides an efficient yet reliable alternative to state-of-the-art cryptographic solutions to guarantee end-to-end data confidentiality and integrity within contemporary IoT infrastructures.
The decentralized finance (DeFi) ecosystem continues to evolve, allowing crypto holders greater control over their assets. This research examines key aspects of token accessibility, liquidity provisioning, and holder distribution. The study focuses on evaluating whether holders can check their ranking and percentage ownership, the availability of the token on decentralized exchanges (DEXs), the feasibility of liquidity pool creation, and opportunities for holders to acquire at least 0.1% of the total supply. In present paper, Coredaovip token has been considered as example to evaluate the crypto holder accessibility, liquidity and participation in decentralized ecosystem.
Hina Binte Haq, Syed Taha Ali, A. G. Sal'Man, Patrick McCorry · 5 authors
The Bitcoin mempool plays an integral role in transaction processing and propagation through the network. Frequent transaction congestion events, as well as spam and dust attacks can clog the mempool, leading to dropped transactions, processing delays, and increased transaction fees. Moreover, increasing transaction loads on the network result in higher resource costs to operate full nodes, thereby restricting Bitcoin's network footprint and negatively impacting its overall health and performance. In this paper, we present Carbyne, a novel mempool optimization scheme, which uses counting bloom filter constructions to adapt to increased transaction flows, thereby making nodes resilient to congestion and spam and dust attacks. We implement Carbyne in C++ and benchmark its performance using a novel data set of Bitcoin mempool activity over a 90-day period. We dramatically reduced the mempool's memory consumption by up to two orders of magnitude (from 300 MB to 3 MB) while verifying and forwarding transactions with 99.9% fidelity and a slight increase in computational load. We simulate extensive spam attacks on Carbyne and demonstrate that mempool loads of 1 GB can be accommodated in as little as 10 MB. Carbyne does not necessitate a hard fork, it will help deploy high-functioning nodes on resource-constrained platforms, and it may also be adapted to other cryptocurrencies.
N. Hafidi, Zakaria Khoudi, Mourad Nachaoui, Soufiane Lyaqini
Accurate cryptocurrency price forecasting is crucial for investors and researchers in the dynamic and unpredictable cryptocurrency market. Existing models face challenges in incorporating various cryptocurrencies and determining the most effective hyperparameters, leading to reduced forecast accuracy. This study introduces an innovative approach that automates hyperparameter selection, improving accuracy by uncovering complex interconnections among cryptocurrencies. Our methodology leverages deep learning techniques, particularly Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, in conjunction with the Genetic Algorithm (GA) to optimize hyperparameters. We propose and compare two architectures, LAO and LOEE, utilizing these methods to enhance forecast accuracy and address the challenges of the cryptocurrency market. This cutting-edge approach not only improves forecasting capabilities but also provides valuable insights for managing cryptocurrency investments and conducting research. By automating hyperparameter selection and considering interconnections between cryptocurrencies, our approach offers a practical solution for accurate cryptocurrency price prediction in a dynamic market environment, benefiting both investors and academics.
We present the design and implementation of a fourth-year undergraduate computer science elective course on cryptocurrency. The course introduces the basics of cryptocurrencies and blockchains using Bitcoin. Programming distributed applications for blockchains is explored using Ethereum via the Solidity programming language. Advanced theoretical concepts are discussed, using different cryptocurrencies to demonstrate how they are implemented in practice. Cryptocurrency applications are presented, along with a responsible examination of present-day cryptocurrency scams. The assignments build upon each other, with the students implementing a web-based blockchain application by the end of the semester. The course was well received, and student feedback was positive. All of the course materials are available online under public copyright licenses.
This paper proposes a method of emulation of \verb|OP_RAND| opcode on Bitcoin through a trustless interactive game between transaction counterparties. The game result is probabilistic and doesn't allow any party to cheat, increasing their chance of winning on any protocol step. The protocol can be organized in a way unrecognizable to any external party and doesn't require some specific scripts or Bitcoin protocol updates. We will show how the protocol works on the simple \textbf{Thimbles Game} and provide some initial thoughts about approaches and applications that can use the mentioned approach.