Marxel S. Abogado, Worasait Suwannik, Geoffrey A. Solano, Somchoke Ruengittinun
Consensus sequences are reconstructed representations of original DNA strands, generated by aligning and combining multiple fragmented reads. This paper presents zkConsensus, a privacy-preserving system that verifies the generation of consensus sequences without revealing sensitive genetic information with the use of Zero-Knowledge Proofs (ZKPs). Implemented in Circom language, the circuit validates three validation parts: the consistency between original and aligned reads, the correctness of alignment scores across all read pairs, and the support for the consensus bases through majority voting. The system takes as public inputs the original reads, their lengths, and an expected score, while treating the alignments, reverse complement indications, start positions, and final consensus as private.
In today's rapidly advancing healthcare landscape, integrating Artificial Intelligence (AI) and Machine Learning (ML) has the potential to significantly improve patient care and streamline medical processes. The utilization of confidential patient data to train and develop these technologies, however, raises significant concerns regarding authenticity, security, and privacy. In this study, we introduce MediChainAI, a safe and practical framework that allows patients full ownership over their own health data by integrating Self-Sovereign Identity (SSI), Blockchain, and sophisticated cryptography techniques. By clearly outlining the goals and parameters of this access, MediChainAI allows patients to safely and selectively share data with healthcare providers and researchers. While SSI guarantees that patients have ownership of their data, the framework uses Blockchain technology to keep things transparent and secure. Further, MediChainAI makes use of Merkle trees, which provide verified access to subsets of data without jeopardizing the privacy of the whole dataset. The encryption mechanism, which is based on smart contracts, is a distinctive feature of the framework that allows researchers and medical practitioners controlled and secure access to patient data. In order to improve the accuracy and reliability of medical diagnoses and treatment, this strategy makes sure that only confirmed, legitimate data is utilized to train medical models. A significant step toward safer and more personalized healthcare, MediChainAI encourages ethical and patient-focused innovation by effectively resolving essential issues regarding data security and patient privacy.
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Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
This research proposes a decentralized and cryptographically secure framework to address the most acute issues of privacy, data security, and protection in the ecosystem of medical insurance claim processing. The scope of this study focuses on enabling the management of insurance claims in a transparent, privacy-protecting manner while maintaining the efficiency and trust level needed by the patients, healthcare providers, and insurers. To accomplish this, the proposed system adds blockchain technology to provide an unchangeable, decentralized, and auditable claim transactions ledger which enhances overall claim-related processes and trust among all stakeholders. To protect critical patient information, the framework employs homomorphic encryption a modern form of cryptography to allow authorized insurance providers to perform necessary operations like claim adjudication and reimbursement on encrypted medical records without any decryption during the process. This method significantly reduces the third-party processing privacy risk because patient data can be kept secret even when third-party processing is done. In addition, smart contracts improve automation of the most important procedures in the claim processing pipeline, which decreases manual, operational, and susceptibility towards human blunders or deceitful acts. The integration of these two transformative technologiesblockchain and homomorphic encryption represents the core contribution of this work, enabling the coexistence of transparency and privacy which are usually viewed as competing objectives in traditional systems. As a result, these technologies are expected to foster the creation of a reliable, effective, and privacy safeguarding architecture that could transform the medical claim submission systems paradigm.
Daojing He, Ding Ke, Sammy Chan, Kim‐Kwang Raymond Choo
Smart contracts have been the target of attackers (e.g., identifying and exploiting vulnerabilities). Existing countermeasures for detecting threats in smart contracts include symbolic execution, formal verification, and fuzzing, most of which only target specific known threats. However, such approaches may not be effective in detecting unknown/unseen threats (e.g., those without predefined vulnerability patterns). Building on the principles of smart contract threats and the immutability property, we propose a path profiling-based threat detection (PPTD) approach. To achieve accurate tracking of cyclic and acyclic paths, PPTD combines the profiling all paths (PAP) algorithm with the efficient path profiling (EPP) algorithm to record contract execution paths. This incurs lower gas overhead while effectively detecting and preventing threats. PPTD obtains legal paths and achieves data flow level detection through fuzzer, and automatically protects vulnerable smart contracts from threats, avoiding manual modification of vulnerable codes. Specifically, our approach is also designed to detect threats and prevent attacks after the contract is deployed, as demonstrated in our evaluations.
ABSTRACT This study examines how blockchain transparency and smart-contract automation, paired with anomaly-detection models, support early detection and calibrated deterrence of manipulation in cryptocurrency markets. Although transparent ledgers and rule-based execution raise the likelihood that irregular activity is flagged and investigated, they do not prevent fraud; my emphasis is detection, deterrence, and post-incident support. I analyze a long-horizon Bitcoin panel using rolling z-score screens and Isolation Forest to surface anomalies consistent with manipulative trading. I fix a false-positive budget ex ante and evaluate capacity-aware performance (Precision@k, PR-AUC, lead time), archiving time-stamped evidence bundles for auditability. Alerts cluster around episodes consistent with pump-and-dump behavior, large-holder moves, and event-driven dislocations, improving investigative triage without prevention claims. The framework provides actionable guidance for exchanges and regulators seeking to strengthen market integrity through auditable records and model-based alerts, and I release a human-in-the-loop agentic AI application that automates ingestion, screening, ranking, and auditable export. Data Availability: A replication package including the agentic AI GenApp (Streamlit code), requirements, and input templates (daily data, events, sentiment) is provided in Appendix B. The package reproduces the pipeline exactly as specified in Section IV and writes time-stamped artifacts for audit; it is intended for detection and deterrence workflows and makes no prevention claims. JEL Classifications: G12; G15; G18; G24; G14; G41; H83.
Risfiana Mayangsari, Hidayat Darussalam, Edi Mulyono
This article analyzes the communication patterns that emerge and develop from the integration of Smart Contracts in Islamic financial transactions. The adoption of Smart Contracts marks a fundamental shift from traditional sighat (ijab qabul) to automated and immutable programmed communication on the blockchain. This study finds that the communication patterns involved are divided into three main dimensions: first, formal human-to-contract communication, which is the process of coding and initial agreement of the contract (such as mudharabah or murabahah) where the sighat is represented by explicit digital input; second, fully automated system-to-system communication, where Smart Contracts communicate with external data (oracles) to verify conditions and trigger self-executing transactions; and third, contract-to-ledger communication, which results in transparent and immutable transaction recording on the blockchain. Although promising efficiency and improved Sharia Compliance through the elimination of operational gharar, this programmed communication pattern poses challenges related to contract flexibility and code error risks. Therefore, it is necessary to formulate clear Sharia code standards and digital governance mechanisms recognized by the Sharia Supervisory Board to ensure that this new communication pattern validly and ethically supports maqasid syariah (Sharia objectives).
The rapid emergence of Stable Coins has completely altered the global landscape of digital finance. The benefits of blockchain technology, along with the typical advantages of a fiat currency, in the form of a stable coin, have had a surreal effect on the world of finance. The paper investigates the evolution, comparative merits and systemic risks of Stable Coins compared to Bitcoin, also uses them for advantages in decentralized finance, liquidity and international transactions. The results clearly show that the Stable Coins have become essential infrastructures of finance because of their low volatility, transaction efficiency but also their sensitivity to such issues as regulation and transparency of reserves. The study of the literature of the BIS, IMF and ECB gives evidence of the fact that stable coins will co-exist with the Central Bank Digital Currencies (CBDC), rather than that they will replace them. The proposed method gives evidence of how a system of collaborative regulation and transparency of reserves can be achieved to facilitate innovations but also protect global economic stability.
Secure Multi-party Computation (MPC) considers the problem where a set of mutually distrusting parties want to jointly compute a function over their private inputs, without revealing any extra information about these inputs other than what it can be inferred from the output of the function. This setting is well-motivated, and it has many real-world applications such as auction, voting, etc. MPC can be also seen as a generalization of many natural cryptographic primitives. For example, zero-knowledge (ZK) can be viewed as a special case of two-party secure computation. In ZK, a party, called prover aims to convince a second party, called verifier, that the prover’s private input witness w and a public input statement x belong to a relation R. An important research direction in secure computation is to find the trade-off between the required setup (e.g., the use of the broadcast channel, the use of common reference string (CRS) / public key infrastructure (PKI), the upper bound of the parties that can be corrupted, etc.), and the security guarantees that can be achieved. The setups can be viewed as some general assumptions that the protocol needs to satisfy, and they influence the usability of the protocol in real-world scenarios. In principle, having simpler (or no) setups mean that the protocol is more general and can be more useful in real-world scenarios. At the same time, having simpler setups may lead to weaker security guarantees. Therefore, finding the trade-off between setup and security guarantees is important and meaningful. In this thesis, we target MPC and ZK, and we focus on how to minimize the setup for MPC and ZK while still providing meaningful levels of security. More specifically: Regarding MPC, we focus on the dishonest majority (i.e., the adversary can corrupt all but one party), and we aim at 1) minimizing the use of broadcast channels. 2) studying the MPC with pre-processing when no setup is available. • Informally, a broadcast channel guarantees that when a message is sent, this reaches all the parties, without ambiguity. It also guarantees that if an honest party receives a message from a corrupted party, then it is guaranteed that all the honest parties have received the message. To realize broadcast, parties in the protocol could run the broadcast protocol, which may require many rounds of peer-to-peer communications. An alternative way is to rely on physical or external infrastructure such as blockchain. In both cases, broadcast is expensive, as such, we want to minimize its use. In particular, this thesis presents the following results: – When assuming no setup, we give a complete characterization with respect to the use of broadcast channels, and we obtain the optimal results. – We consider the same problem for the case that we only want to allow the black-box use (i.e., do not have access to the code of the algorithm) of the oblivious transfer protocol. We also give a characterization. • In the standard definition of MPC, the parties’ private inputs are fixed before the start of the protocol. However, there is another type of MPC named MPC with pre-processing, where the protocol can pre-compute some messages without using parties’ inputs, and these messages can accelerate computations in the online phase (i.e., other computations that require parties’ inputs). Since some expensive computations can be pre-computed, the online phase could be more lightweight. Therefore, we want to remove the dependency of the input from as many rounds as possible, so that we can do some pre-processing. In this direction, we explore the protocol with no setup. We provide a compiler that can turn a big class of MPC protocol that may require the inputs already to compute the first round, into a new protocol that needs the inputs only in the last two rounds. We also propose new MPC definitions that capture this delayed-input features. Regarding ZK, we do the following: • In standard single-theorem ZK definition, the security of the ZK protocol is guaranteed to hold only when one proof is issued. In the case where multiple zero-knowledge proofs need to be issued (i.e., to prove multiple NP statements), each new zero-knowledge proof requires a freshly generated setup. In the multi-theorem ZK definition, instead, one setup is sufficient for generating multiple zero-knowledge proofs for multiple instances. We propose a multi-theorem protocol (in the format of a compiler) that follows the Fiat-Shamir paradigm and relies on correlation intractable hash functions. Moreover, our protocol remains zero-knowledge and sound even against adversaries that choose the statement to be proven (and the witness for the case of zero-knowledge) adaptively on the key of the hash function. Prior works could achieve this adaptive security only inefficiently via NP reductions. • ZK protocols are secure only when all setups are correctly generated, but in real-world scenarios, some of the setups may not be correctly generated. For instance, to run a non-interactive zero-knowledge (NIZK) protocol, the setup CRS could be chosen with bias. In this case, the security of the NIZK protocol does not hold anymore. Instead of finding a secure ZK candidate, one alternative solution is to have multiple instantiations of ZK candidates and assume that only for a subset of them the setup is generated correctly. More formally, we consider the case where only a subset of the instances are secure. In more detail, given access to n candidate instantiations of a NIZK for some language, we want to have a construction that itself implements a NIZK for the same language without relying on any additional computational assumptions. We refer to this type of construction as combiner, and the combiner is secure assuming at least t of the given candidates are secure. In this work, we provide three different constructions of robust NIZK combiners and show that combiners are impossible to realize unless the majority of the input candidates are secure.
Ravi Khile, Shravani Karvande, Pradnya Katbane, Saniya Shaikh
Water quality degradation has become a pressing global challenge due to rapid industrialization, urbanization, and population growth. Conventional water quality monitoring systems rely on manual testing or cloud-based IoT frameworks, which often face vulnerabilities such as data tampering, network latency, and security breaches. To address these limitations, this research proposes a Blockchain-Based Secure Data Framework for IoT Water Monitoring using ESP32 and LoRa communication integrated with Firebase Cloud. The proposed system ensures tamper-proof, transparent, and decentralized data management for multi-parameter water quality monitoring. IoT sensor nodes equipped with pH, turbidity, TDS, and temperature sensors collect real-time data transmitted via LoRa gateways to a blockchain-enabled cloud interface. The blockchain layer secures sensor data through cryptographic hashing, consensus validation, and distributed ledger mechanisms. Experimental validation demonstrates that blockchain integration reduces unauthorized data manipulation by 98% and enhances system trust and traceability. The framework achieves an average latency of 1.2 seconds per transaction and consumes 27% less power compared to traditional cloud-only solutions. The results highlight blockchain’s potential to revolutionize secure environmental monitoring and ensure reliable, transparent water data management for sustainable smart cities.
This paper presents a novel hybrid cryptographic architecture for blockchain-based document signing that addresses fundamental interoperability challenges across heterogeneous blockchain platforms. The proposed system introduces technical innovations including a unified cryptographic protocol combining classical ECDSA signatures with post-quantum Dilithium algorithms, biometric authentication mechanisms, and zero-knowledge proof systems. The architecture enables seamless document signing across Ethereum, Hyperledger Fabric, and Solana networks through standardized smart contract interfaces and novel cross-chain verification protocols. Key technical contributions include mathematical formalization of hybrid signature schemes, implementation of privacy-preserving verification using NIZK proofs, and design of platform-agnostic verification mechanisms that maintain cryptographic security across different consensus architectures while achieving sub-second verification times.
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.
Abstract This research presents the Aegis Framework, a groundbreaking secure and upgradeable digital token contract system that fundamentally addresses cryptocurrency scam prevention through innovative smart contract architecture. The framework implements a novel timelock-protected proxy pattern that eliminates instant rug pull vulnerabilities while maintaining essential upgrade capabilities. Developed through systematic implementation of advanced smart contract patterns, the Aegis Framework comprises three core components: AegisToken (a secure, pausable ERC20 token with role-based access control), AegisProxy (an upgradeable proxy system using UUPS pattern), and AegisTimelock (a revolutionary mandatory delay mechanism for all upgrades). Technical validation confirms successful compilation of all contracts with AegisToken (2782 bytes, 17 functions), AegisProxy (1771 bytes, 8 functions), and AegisTimelock (680KB comprehensive security contract). The framework's security architecture prevents single-point-of-failure risks by separating roles (Owner, Minter, Pauser, Upgrader) and enforcing minimum delay periods (configurable from 1–7 days) for all contract upgrades. Implementation results demonstrate complete prevention of instant malicious upgrades, transparent upgrade processes with community notification periods, and emergency response capabilities through instant pausable mechanisms. Compared to existing solutions, the Aegis Framework introduces the first integrated timelock security system specifically designed for token contracts, addressing critical gaps identified in recent smart contract security literature. The complete system includes Python-based deployment tools, comprehensive validation scripts, and production-ready compiled contracts, establishing a new standard for secure token implementations in decentralized finance applications.
Software services are crucial for reliable communication and networking; therefore, Site Reliability Engineering (SRE) is important to ensure these systems stay reliable and perform well in cloud-native environments. SRE leverages tools like Prometheus and Grafana to monitor system metrics, defining critical Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for maintaining high service standards. However, a significant challenge arises as many developers often lack in-depth understanding of these tools and the intricacies involved in defining appropriate SLIs and SLOs. To bridge this gap, we propose a novel SRE platform, called SRE-Llama, enhanced by Generative-AI, Federated Learning, Blockchain, and Non-Fungible Tokens (NFTs). This platform aims to automate and simplify the process of monitoring, SLI/SLO generation, and alert management, offering ease in accessibility and efficy for developers. The system operates by capturing metrics from cloud-native services and storing them in a time-series database, like Prometheus and Mimir. Utilizing this stored data, our platform employs Federated Learning models to identify the most relevant and impactful SLI metrics for different services and SLOs, addressing concerns around data privacy. Subsequently, fine-tuned Meta's Llama-3 LLM is adopted to intelligently generate SLIs, SLOs, error budgets, and associated alerting mechanisms based on these identified SLI metrics. A unique aspect of our platform is the encoding of generated SLIs and SLOs as NFT objects, which are then stored on a Blockchain. This feature provides immutable record-keeping and facilitates easy verification and auditing of the SRE metrics and objectives. The automation of the proposed platform is governed by the blockchain smart contracts. The proposed SRE-Llama platform prototype has been implemented with a use case featuring a customized Open5GS 5G Core.
This paper presents TEOM (The Evolutionary Open Machine), a decentralized operating protocol designed for smart enterprise architecture. TEOM enables secure, scalable, and flexible integration across diverse organizational functions, from AI and IoT to finance and governance. By applying a combination of Hashgraph and blockchain technologies, TEOM provides a robust framework for decentralized service delivery, real time data sharing, and multi organization collaboration. It supports the creation of autonomous, fault tolerant ecosystems that scale effortlessly, ensuring high availability and fault resistance across distributed networks. TEOM’s extensibility allows seamless integration of legacy systems and third-party platforms, promoting interoperability within smart enterprise environments. The protocol also integrates advanced AI capabilities for intelligent decision making, predictive analytics, and federated learning. Through automated service provisioning and decentralized transaction management, TEOM fosters an agile, transparent, and adaptive framework for modern enterprise architecture, empowering organizations to operate efficiently in a zero-trust environment.
Anis Ur Rehman, M. J. Sanjari, Rajvikram Madurai Elavarasan, Taskin Jamal
Transformation of the energy sector is necessary to meet climate targets and ensure universal access to reliable and affordable energy. Despite progress, more than 675 million people still lack electricity and 770 million face an unreliable power supply. Renewable energy now provides nearly 30 % of global electricity generation and represents approximately 17.9 % of total final energy consumption. This amount is insufficient for the 1.5 ∘ C pathway and requires a tripling of renewable capacity by 2030. Energy efficiency also lags with average annual gains of 1.6 % compared with the 4 % required for climate-aligned energy scenarios. Therefore, this paper reviews pathways toward decentralized low-carbon solutions that can accelerate global energy transformation. The review paper examines how technologies such as microgrids, virtual power plants, energy storage systems, and vehicle-to-grid (V2G) solutions are reshaping modern energy systems. It highlights that digitalization, smart grids, and sector integration are key to building flexible and consumer-focused networks. However, achieving sustainable energy access requires more than new technologies. Strong governance, fair financing, and social inclusion are equally important to ensure a just and balanced energy transition. Case studies from Asia, Africa, and Latin America show how policy, innovative financing, and regional cooperation can drive progress despite challenges such as underinvestment, fossil fuel dependency, and energy poverty. The review demonstrates that an integrated approach, combining technological innovation, financial mechanisms, and inclusive policies, can collectively build low-carbon, resilient, and equitable energy systems. • Research gaps in sustainable energy supply on technology, policy, and equity are identified. • Sustainability-aligned pathways toward decentralized low-carbon solutions are reviewed. • Governance and planning are key for sustainable energy transitions. • A comprehensive framework of technical, economic, and social insights for sustainable transition is introduced.
This paper surveys the growing empirical literature on decentralized finance (DeFi), emphasizing how protocol design and incentive structures shape economic outcomes in blockchain-based financial systems. We review evidence on tokens, decentralized exchanges, lending platforms, yield farming, derivatives, governance, infrastructure, and regulation. Across these domains, research highlights mechanisms of liquidity provision, price discovery, leverage, systemic fragility, and investor behavior, as well as vulnerabilities stemming from arbitrage frictions, liquidation dynamics, and maximal extractable value. We also examine the roles of audits, oracle networks, settlement mechanisms, and transparency tools in substituting for traditional oversight. The findings indicate that DeFi replicates many functions of traditional finance while introducing new risks linked to pseudonymity, smart contracts, and composability. The survey concludes by outlining open questions for research and policy on market efficiency, governance, systemic risk, and long-term sustainability.
Existing retrieval-augmented generation (RAG) systems typically use a centralized architecture, causing a high cost of data collection, integration, and management, as well as privacy concerns. There is a great need for a decentralized RAG system that enables foundation models to utilize information directly from data owners who maintain full control over their sources. However, decentralization brings a challenge: the numerous independent data sources vary significantly in reliability, which can diminish retrieval accuracy and response quality. To address this, our decentralized RAG system has a novel reliability scoring mechanism that dynamically evaluates each source based on the quality of responses it contributes to generate and prioritizes high-quality sources during retrieval. To ensure transparency and trust, the scoring process is securely managed through blockchain-based smart contracts, creating verifiable and tamper-proof reliability records without relying on a central authority. We evaluate our decentralized system with two Llama models (3B and 8B) in two simulated environments where six data sources have different levels of reliability. Our system achieves a +10.7\% performance improvement over its centralized counterpart in the real world-like unreliable data environments. Notably, it approaches the upper-bound performance of centralized systems under ideally reliable data environments. The decentralized infrastructure enables secure and trustworthy scoring management, achieving approximately 56\% marginal cost savings through batched update operations. Our code and system are open-sourced at github.com/yining610/Reliable-dRAG.
This paper presents a comparative analysis of three cryptographic approaches to securing fiber-optic data transmission: classical RSA/AES, Quantum Key Distribution (QKD) based on the E91 protocol, and Secure Dynamic Stream Encryption (SDSE). These are evaluated in terms of latency, key renewal frequency, computational load, energy efficiency, and resilience to quantum attacks. In addition to these core comparisons, we extend SDSE with a blockchain-based governance layer. This enhancement introduces auditable key management through distributed ledger technology (DLT), ensuring transparent tracking of key rotations, revocations, and policy enforcement without storing any secret material. The blockchain acts as a trust anchor for multi-node synchronization, particularly useful in federated or multi-domain environments. The combined SDSE + Blockchain framework provides a lightweight, quantum-resilient, and scalable encryption mechanism suitable for real-world deployment across conventional optical infrastructure.