DeFi (Decentralized Finance) is one of the most important applications of today's cryptocurrencies and smart contracts. It manages hundreds of billions in Total Value Locked (TVL) on-chain, yet it remains susceptible to common DeFi price manipulation attacks. Despite state-of-the-art (SOTA) systems like DeFiRanger and DeFort, we found that they are less effective to non-standard price models in custom DeFi protocols, which account for 44.2% of the 95 DeFi price manipulation attacks reported over the past three years. In this paper, we introduce the first LLM-based approach, DeFiScope, for detecting DeFi price manipulation attacks in both standard and custom price models. Our insight is that large language models (LLMs) have certain intelligence to abstract price calculation from smart contract source code and infer the trend of token price changes based on the extracted price models. To further strengthen LLMs in this aspect, we leverage Foundry to synthesize on-chain data and use it to fine-tune a DeFi price-specific LLM. Together with the high-level DeFi operations recovered from low-level transaction data, DeFiScope detects various DeFi price manipulations according to systematically mined patterns. Experimental results show that DeFiScope achieves a high recall of 80% on real-world attacks, a precision of 96% on suspicious transactions, and zero false alarms on benign transactions, significantly outperforming SOTA approaches. Moreover, we evaluate DeFiScope's cost-effectiveness and demonstrate its practicality by helping our industry partner confirm 147 real-world price manipulation attacks, including discovering 81 previously unknown historical incidents.
Blockchain technology has emerged as a powerful tool for enhancing trust and security in a variety of contexts. It has the potential to completely transform a variety of industries, from finance and healthcare to supply chain management and beyond, by enabling safe and open record-keeping. A decentralized ledger that securely and publicly records transactions is known as a blockchain. Each block in the chain is added to the chain immutably and permanently once a network of computers confirms a set of transactions contained within. Because the information saved on the Blockchain is safe and cannot be altered or deleted, there is a high level of trust and transparency. The ability of Blockchain technology to do away with the need for middlemen in transactions is one of its main advantages. By creating a decentralized network that allows for direct peer-to-peer transactions, it can significantly reduce the costs and inefficiencies associated with traditional intermediaries such as banks, lawyers, and brokers. This has the potential to disrupt a wide range of industries, from finance and real estate to healthcare and logistics. Blockchain technology also has the potential to enhance trust and security in a variety of contexts. For example, in the realm of finance, Blockchain-based systems can enable secure and transparent record-keeping of financial transactions, reducing the risk of fraud and error. Similarly, in the realm of healthcare, Blockchain-based platforms can make it easier to share medical records securely and effectively while protecting patient privacy. Supply chain management is another industry that Blockchain technology has the potential to disrupt. It can lessen fraud, counterfeiting, and other types of wrongdoing by offering a clear and secure record of each stage in the supply chain, from production to distribution. This is particularly significant in sectors like food and medicines where public health depends on the supply chain’s integrity. Despite these potential advantages, before it can realize its full potential, blockchain technology must overcome a lot of challenges as it is still in its infancy. One of the main problems is scalability because the current Blockchain-based solutions have limitations. Efforts are underway to address this issue through the development of new consensus mechanisms and other technologies. Another challenge is regulatory uncertainty, as the legal and regulatory framework surrounding Blockchain technology is still evolving. In order to achieve widespread adoption, there needs to be greater clarity and consistency in the regulatory environment. This chapter aims to provide an overview of the potential of Blockchain technology, its challenges and limitations, and the opportunities it presents for the future of trust.
Call detail records (CDRs) provide valuable insights into user behavior, which are instrumental for telecom companies in optimizing network coverage and service quality. However, while cloud computing facilitates clustering analysis on a vast scale of CDR data, it introduces privacy risks. The challenge lies in striking a balance between efficiency, security, and cost-effectiveness in privacy-preserving algorithms. To tackle this issue, we propose a privacy-preserving and cost-effective incremental density peak clustering scheme. Our approach leverages homomorphic encryption and order-preserving encryption to enable direct computations and clustering on encrypted data. Moreover, it employs reaching definition analysis to optimize the execution flow of static tasks, pinpointing the optimal junctures for transitioning between the two types of encryption to reduce communication overhead. Furthermore, our scheme utilizes a game theory-based verification strategy to ascertain the accuracy of the results. This methodology can be effectively deployed on the Ethereum blockchain via smart contracts. A comprehensive security analysis confirms that our scheme upholds both privacy and data integrity. Experimental evaluations substantiate the clustering accuracy, communication load, and computational efficiency of our scheme, thereby validating its viability in real-world applications.
The education sector has witnessed a significant shift towards digitising student records, with relevant data now stored in centralized data repositories. While traditional identity management solutions in education are functional, they often face various challenges, including data privacy concerns, limited portability, and reliability challenges. As the volume of student data continues to grow, inadequate data management practices have led to several problems. These include students losing control and empowerment over their educational information, increased vulnerability to potential data breaches and unauthorized access, a lack of transparency and accountability, data silos and inconsistencies, and administrative inefficiencies. To address these limitations, the implementation of a blockchain-assisted self-sovereign identity (Ba-SSI) concept in the education system presents a viable solution. Self-sovereign identity (SSI) represents a paradigm shift from traditional centralized identity systems, allowing individuals to maintain full control of their identity data without relying on centralized authorities. By leveraging the decentralized nature, SSI frameworks can ensure security, interoperability, and scalability, thereby improving user-centric identity management. This survey paper explores the potential of Ba-SSI within the context of education. It thoroughly reviews the current state of digital identity management in education, highlighting the limitations of conventional systems and the emerging role of blockchain technology in addressing these challenges. The paper discusses the fundamental principles of blockchain technology and how it can be utilized to enhance security, interoperability, and scalability in identity management. Additionally, it examines the insights and benefits of this approach for the education system. Finally, the paper concludes by addressing the issues, challenges, benefits, and future research directions in this domain, underscoring the potential of Ba-SSI solutions to revolutionize the management and empowerment of student data within the education sector.
Blockchain stores and writes all the transactions because of the unlimited storage capacity. Leader election is the process of electing a node as an overall incharge of the distributed network. Leader election is a complicated task as we have to choose a leader by giving equal opportunity to all the nodes. We implement all the algorithms of the DONS protocol in order to elect a leader but in our TBNS (Trust Based Neighbor Selection) protocol, we add zk-SNARKs proof to enhance the security of Blockchain. zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) is a type of proof used in cryptography to prove the authenticity of information without revealing any additional information. It allows one party to prove to another that they know a certain piece of information without actually revealing the information itself. In the end, the results of our proposed model are compared with RTT-NS and DONS.
Manivannan Senthil Velmurugan, C. K. Shinzeer, G. Maya, R. Ramya · 6 authors
Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions.
Distributed certification is a set of mechanisms that allows an all-knowing prover to convince the units of a communication network that the network's state has some desired property, such as being 3-colorable or triangle-free. Classical mechanisms, such as proof labeling schemes (PLS), consist of a message from the prover to each unit, followed by one round of communication between each unit and its neighbors. Later works consider extensions, called distributed interactive proofs, where the prover and the units can have multiple rounds of communication before the communication among the units. Recently, Bick, Kol, and Oshman (SODA '22) defined a zero-knowledge version of distributed interactive proofs, where the prover convinces the units of the network's state without revealing any other information about the network's state or structure. In their work, they propose different variants of this model and show that many graph properties of interest can be certified with them. In this work, we define and study distributed non-interactive zero-knowledge proofs (dNIZK); these can be seen as a non-interactive version of the aforementioned model, and also as a zero-knowledge version of PLS. We prove the following: - There exists a dNIZK protocol for 3-coloring with O(log n)-bit messages from the prover and O(log n)-size messages among neighbors. - There exists a family of dNIZK protocols for triangle-freeness, that presents a trade-off between the size of the messages from the prover and the size of the messages among neighbors. - There exists a dNIZK protocol for any graph property in NP in the random oracle models, which is secure against an arbitrary number of malicious parties.
Large language models (LLMs) are increasingly utilized in domains such as finance, healthcare, and interpersonal relationships to provide advice tailored to user traits and contexts. However, this personalization often relies on sensitive data, raising critical privacy concerns and necessitating data minimization. To address these challenges, we propose a framework that integrates zero-knowledge proof (ZKP) technology, specifically zkVM, with LLM-based chatbots. This integration enables privacy-preserving data sharing by verifying user traits without disclosing sensitive information. Our research introduces both an architecture and a prompting strategy for this approach. Through empirical evaluation, we clarify the current constraints and performance limitations of both zkVM and the proposed prompting strategy, thereby demonstrating their practical feasibility in real-world scenarios.
The rapid increase in Internet of Things (IoT) applications has exposed critical security vulnerabilities, particularly concerning user privacy and identity forgery. To address these concerns, Blockchain technology offers a promising solution by providing strong security and ensuring data integrity through its transparent ledger system. By leveraging blockchain, IoT systems can enhance their security protocols, making it more difficult for attackers to exploit vulnerabilities and access sensitive data. We proposed Attribute-Based Access Control (ABAC) integrated with trust-based delegated consensus blockchain (TDCB) technology. The ABAC scheme employs Fully Homomorphic Encryption (FHE) processes to encrypt attributes and access regulations, enabling analytical operations directly on encrypted data. Dueling Double Deep Q-Networks with Prioritized Experience Replay (D3P) with Deep Reinforcement Learning (DRL) collaborate with Multiple blockchain nodes to decode the ABAC system’s data and optimize the performances of the blockchain. Our proposed scheme ABAC-TDBC-D3P enhances throughput and security and reduces total computing costs. The simulation results demonstrate that the suggested ABAC-TDCB-D3P scheme has a percentage of 86% for Collusive Rumour Attack (CRA) and 91% for Naive Malicious Attack (NMA). Significant improvements in blockchain security, particularly in mitigating the impact of malicious nodes, were compared to previous schemes.
The medical industry has made significant advancements in recent years. However, the lack of accountability in medical management has resulted in systemic deficiencies, which have adversely affected patient trust and contributed to an increase in medical disputes. As a result, there is a growing emphasis on managing the quality of medical services, particularly in enhancing patient experience. To address these challenges, we propose a new system for evaluating health services. This system will allow patients to anonymously rate the services they receive while also providing doctors the opportunity to appeal specific reviews. The hospital handles the evaluations and appeals through the management of the cloud platform. We propose a new scheme to assist the work of the platform, which is a lattice-based group signature with verifier-local revocation (VLR-GS). Most of the work on VLR-GS has focused on the random oracle model (ROM) or using non-interactive zero-knowledge proofs (NIZKs). Our construction is anonymous and traceable in the standard model under the hardness of the learning with errors problem and short integer solution problem. Furthermore, theoretically analyzing it has practical significance in both security and efficiency. In conclusion, the proposed scheme establishes a secure and privacy-oriented platform for an anonymous medical service evaluation system, with the goal of fostering patient trust and improving hospital service quality within the healthcare sector.
Nojan Sheybani, Anees Ahmed, Michel A. Kinsy, Farinaz Koushanfar
Zero-Knowledge Proofs (ZKPs) are a cryptographic primitive that allows a prover to demonstrate knowledge of a secret value to a verifier without revealing anything about the secret itself. ZKPs have shown to be an extremely powerful tool, as evidenced in both industry and academic settings. In recent years, the utilization of user data in practical applications has necessitated the rapid development of privacy-preserving techniques, including ZKPs. This has led to the creation of several robust open-source ZKP frameworks. However, there remains a significant gap in understanding the capabilities and real-world applications of these frameworks. Furthermore, identifying the most suitable frameworks for the developers' specific applications and settings is a challenge, given the variety of options available. The primary goal of our work is to lower the barrier to entry for understanding and building applications with open-source ZKP frameworks. In this work, we survey and evaluate 25 general-purpose, prominent ZKP frameworks. Recognizing that ZKPs have various constructions and underlying arithmetic schemes, our survey aims to provide a comprehensive overview of the ZKP landscape. These systems are assessed based on their usability and performance in SHA-256 and matrix multiplication experiments. Acknowledging that setting up a functional development environment can be challenging for these frameworks, we offer a fully open-source collection of Docker containers. These containers include a working development environment and are accompanied by documented code from our experiments. We conclude our work with a thorough analysis of the practical applications of ZKPs, recommendations for ZKP settings in different application scenarios, and a discussion on the future development of ZKP frameworks.
Performance predictability, ensuring low latency variability, is crucial for the reliability and efficiency of blockchain consensus. Byzantine Fault Tolerant Proof-of-Stake (BFT-PoS) consensus aims to achieve stable transaction processing latency by scheduling block generation at consistent intervals. However, BFT-PoS's incentive mechanisms grant all transaction tips to the block proposer, which can be exploited by delaying proposals to gain extra Maximal Extractable Value (MEV) rewards, thus undermining performance predictability. Existing solutions impose penalties for delays but lack a standard for measuring the extra rewards from delays or fail in malicious environments. This paper introduces InTime, a novel approach to safeguard performance predictability in BFT-PoS by economically motivating timely block proposals. We first introduce the untimely MEV ratio , a reliable metric to measure the extra rewards gained from proposal delays, facilitating our countermeasures against deliberate delays. Furthermore, we propose the arrival rate incentive (ARI), aligning rewards with transaction arrival timing among nodes to reduce potential MEV manipulation. To make ARI robust against malicious behaviors, we establish a committee time witness (CTW) workflow to accurately gather and verify transaction arrival times. Extensive experiments demonstrate that InTime can effectively reduce latency variability by up to 95.9%.
The rise of cryptocurrencies and decentralized fi- nance (DeFi) has highlighted the importance of secure and collaborative management systems for digital assets. Multi-party crypto vaults provide a way to ensure distributed control, privacy, and fault tolerance by involving multiple participants in the management and approval of transactions. This paper explores the two primary approaches—Multi-Signature (Multi-Sig) and Multi-Party Computation (MPC)—that underpin these vaults. Multi-Sig schemes offer fast, scalable solutions for real-time applications, while MPC ensures strong privacy and security by allowing encrypted computations without exposing private keys. In addition key management models, such as split-key and blockchain-based methods, and fault tolerance mechanisms, including social recovery and time-locked protocols, which ensure that vault systems remain secure and operational even in cases of participant failure. This paper recommends MPC as the optimal approach for high-security, privacy-sensitive applications, such as institutional custody and financial systems, while outlining av- enues for future research, including post-quantum cryptography and latency reduction in MPC protocols. Index Terms—Multi-Party Computation (MPC), Multi- Signature (Multi-Sig), Crypto Vaults, Threshold Cryptography, Blockchain Key Management, Decentralized Finance (DeFi),, Threshold ECDSA, Social Recovery Mechanism, Time-Locked Withdrawal Protocol, Fault Tolerance in Cryptography, Split- Key Management, Smart Contracts, Privacy-Preserving Cryp- tography, Institutional Crypto Custody
Tao Lu, Yuxun Chen, Zonghui Wang, Xiaohang Wang · 6 authors
Zero-knowledge proof (ZKP) is a cryptographic primitive that enables one party to prove the validity of a statement to other parties without disclosing any secret information. With its widespread adoption in applications such as blockchain and verifiable machine learning, the demand for generating zero-knowledge proofs has increased dramatically. In recent years, considerable efforts have been directed toward developing GPU-accelerated systems for proof generation. However, these previous systems only explored efficiently generating a single proof by reducing latency rather than batch generation to provide high throughput.
Threshold cryptography is a powerful and well-known technique with many applications to systems relying on distributed trust. It has recently emerged also as a solution to challenges in blockchain: frontrunning prevention, managing wallet keys, and generating randomness. This work presents Thetacrypt, a versatile library for integrating many threshold schemes into one codebase. It offers a way to easily build distributed systems using threshold cryptography and is agnostic to their implementation language. The architecture of Thetacrypt supports diverse protocols uniformly. The library currently includes six cryptographic schemes that span ciphers, signatures, and randomness generation. The library additionally contains a flexible adapter to an underlying networking layer that provides peer-to-peer communication and a total-order broadcast channel; the latter can be implemented by distributed ledgers, for instance. Thetacrypt serves as a controlled testbed for evaluating the performance of multiple threshold-cryptographic schemes under consistent conditions, showing how the traditional micro benchmarking approach neglects the distributed nature of the protocols and its relevance when considering system performance.
This thesis explores the implementation of a Self-Sovereign Identity (SSI) system using Ethereum and Decentralized Identifiers (DIDs). The project focuses on leveraging blockchain technology to create a secure and decentralized framework for digital identity management, incorporating Verifiable Credentials (VCs) and Verifiable Presentations (VPs). Key components include DID document management, secure user authentication, and user-friendly interface. What makes this system different is the integration with existing wallets, privacy and user control through selective disclosure, allowing users to share only necessary information, and key rotation. It also uses EIP-712 signatures for secure and structured data signing, which allows users to clearly see and understand what they are signing while the cryptography is securely handled by the wallet. Future work will focus on adding more wallet support, improving data storage, and enhancing system scalability and security.
Yong Yu, Qian Zhang, Yannan Li, Y. Yao · 5 authors
Auctions conducted via wireless devices have promoted trade worth trillions of dollars globally. Their potential market share and crucial role in resource allocation make them an appealing research topic. However, sealed-bid auctions are usually considered high-risk behaviors for bidders due to their unequal status, dependence on auctioneers, and lack of transparency during an auction. In order to meet these requirements for confidentiality, integrity, and availability of data and reduce communication complexity between wireless devices, in this article, we propose a trustworthy sealed-bid auction protocol in wireless networks with low communication costs. Specifically, the protocol adopts cryptographic techniques such as commitment and zero-knowledge proofs to ensure the validity of bids and the credibility of auction results. Inner-product arguments of vectors and proof of aggregation are employed to reduce communication costs of the proposal. The protocol guarantees the continuous verification of submitted data throughout the process (before, during, and at the end) of an auction so as to grant admission to network resources. We show that the proposed protocol satisfies privacy protection, public verifiability, fairness, and non-repudiation of bidders and auctioneers. In addition, our construction is generic, and the protocol can be extended to ordinal-price sealed-bid auctions. The time consumption evaluated in the implementation indicates the feasibility of the protocol in real-world applications.
Zero-knowledge proofs (ZKPs) are widely applied in digital economies, such as cryptocurrencies and smart contracts, for establishing trust and privacy between untrusted parties. Classical ZKPs rely on computational assumptions and are vulnerable to quantum attacks. While a recent advance suggests quantum-sound symmetric relativistic ZKPs for the graph three-coloring problem without computational assumptions, the high round complexity, which leads to unachievable runtime and overall randomness cost, renders them impractical for real-life deployment. To overcome this, we develop an efficient asymmetric relativistic ZKP protocol using relativistic bit commitments, and prove its quantum soundness by relating it to the nonlocal Clauser-Horne-Shimony-Holt (CHSH) game. Our protocol achieves a linear relationship between the round complexity and the number of edges, and thus significantly improves practical feasibility. In addition, we implement a proof-of-principle experiment which completes all interactive rounds in about 0.22 seconds and requires an overall randomness cost of 430.81 MB. Our work illustrates the powerful potential of integrating special relativity with quantum theory in trustless cryptography, paving the way for robust applications against quantum attacks in distrustful Internet environments. Zero-knowledge proofs can protect privacy online, but almost all current methods are vulnerable to quantum attacks. Here, the authors report an efficient relativistic protocol and experiment that resists quantum attacks and greatly reduces runtime, randomness cost and communication rounds.
Collision-resistant cryptographic hash functions (CRHs) are crucial for security, particularly for message authentication in Zero-knowledge Proof (ZKP) applications. However, traditional CRHs like SHA-2 or SHA-3, while optimized for CPUs, generate large circuits, rendering them inefficient in the ZK domain. Conversely, ZK-friendly hashes are designed for circuit efficiency but struggle on conventional hardware, often orders of magnitude slower than standard hashes due to their reliance on expensive finite field arithmetic. To bridge this performance gap, we present HashEmAll, a novel collection of FPGA-based realizations for three prominent ZK-friendly hashes: Griffin, Rescue-Prime, and Reinforced Concrete. Each offers distinct optimization profiles, with both area-optimized and latency-optimized variants available, allowing users to tailor hardware selection to specific application constraints regarding resource utilization and performance. Our extensive evaluation shows that latency-optimized HashEmAll designs outperform CPU implementations by at least $10 \times$, with the leading design achieving a $23 \times$ speedup. These gains are coupled with lower power consumption and compatibility with accessible FPGAs. Importantly, the highly parallel and pipelined architecture of HashEmAll enables significantly better practical scaling than CPU-based approaches towards building real-world ZKP applications, such as data commitments with Merkle Trees, by mitigating the hashing bottleneck for large trees. This highlights the suitability of HashEmAll for real-world ZKP applications involving large-scale data authentication. We also highlight the ability to translate the HashEmAll methodology to various ZK-friendly hash functions and different field sizes.
Federico Badaloni, Sebastian Holler, Chrysoula Oikonomou, Pedro Moreno-Sánchez · 5 authors
A smart contract is an interactive program that governs funds in the realm of a single cryptocurrency. Yet, the many existing cryptocurrencies have spurred the design of cross-chain applications that require interactions with multiple cryp-tocurrencies simultaneously. Currently, cross-chain applications are implemented as use-case-specific cryptographic protocols that serve as overlay to synchronize smart contract executions in the different cryptocurrencies. Hence, their design requires substantial expertise, as well as a security analysis in complex cryptographic frameworks. In this work, we present$BitML^{x}$, the first domain-specific language for cross-chain smart contracts, enabling interactions with several users that hold funds across multiple Bitcoin-like cryptocurrencies. We contribute a compiler to automatically translate a$BitML^{x}$contract into one contract per involved cryp-tocurrency and a user strategy that synchronizes the execution of these contracts. We prove that an honest user, who follows the prescribed strategy when interacting with the several contracts, ends up with at least as many funds as in the corresponding execution of the$BitML^{x}$contract. Last, but not least, we implement the$BitML^{x}$compiler and demonstrate its utility in the design of illustrative examples of cross-chain applications such as multi-chain donations or loans across different cryptocurrencies.
Open access
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cs.CR
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Satellite communication (SC) is an indispensable component of future communication systems due to its extensive coverage and flexibility. In this work, we investigate the fine-grained anonymous access technique, which not only ensures user equipments (UEs) privacy in the open SC environment but also enables satellites to enforce fine-grained access policies with low overhead. Firstly, we design a Merkle forest based framework that facilitates efficient UE attribute data synchronization, leveraging the broadcasting capability of satellites. Subsequently, we construct a zero-knowledge based protocol for UE authentication, key agreement, and handover in SC. The analysis demonstrates the protocol's resilience against prevalent attacks, and simulation results validate its practicality.
Abstract Blockchain-based data transaction protocols augmented with zero-knowledge proofs offer fairness to the participants, yet they encounter challenges pertaining to both security and efficiency. We propose the zk-DSTARK, a zk-STARK-based protocol that enables distributed generation of zero-knowledge proofs, significantly reducing the computational burden. And zk-DSTARK inherits zk-STARK’s feature of single proof generation for multiple uses, improving the efficiency of successive transactions. Furthermore, we propose a fair exchange system named zk-DCIAExchange for off-chain verification, which is based on zk-DSTARK and intel software guard extensions (SGX). This system not only minimizes on-chain overhead but also ensures the security and fairness of the transaction. Experimental results show that, in continuous transactions scenarios, the time overhead for subsequent transactions is diminished by 99.9% compared to the first transaction; compared to zero knowledge contingent payment (ZKCSP), our scheme achieves a remarkable 92% reduction in time overhead, and a 26.3% reduction when compared to FairSwap; with 32 distributed nodes and a trace length of 216, the proof generation time is reduced by ~85.45%; the additional verification time introduced by the SGX is ~0.45 s, which is deemed acceptable, and the on-chain verification overhead is reduced by ~7.2% compared to the ZKCSP and ~54.4% compared to FairSwap.