Blockchain Papers

Huakun Huang, Longtao Guo, Lingjun Zhao, Haoda Wang · 6 authors

No abstract is available for this record.

Qiang Han, Lu Wang, Haoyu Zhang, Leyi Shi · 5 authors

No abstract is available for this record.

Janaka Senanayake, Harsha Kalutarage, Andrei Petrovski, Luca Piras · 5 authors

Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to reduce potential vulnerabilities during development, their effectiveness in detecting vulnerabilities may fall short. To address this, “Defendroid”, a blockchain-based federated neural network enhanced with Explainable Artificial Intelligence (XAI) is introduced in this work. Trained on the LVDAndro dataset, the vanilla neural network model achieves a 96% accuracy and 0.96 F1-Score in binary classification for vulnerability detection. Additionally, in multi-class classification, the model accurately identifies Common Weakness Enumeration (CWE) categories with a 93% accuracy and 0.91 F1-Score. In a move to foster collaboration and model improvement, the model has been deployed within a blockchain-based federated environment. This environment enables community-driven collaborative training and enhancements in partnership with other clients. The extended model demonstrates improved accuracy of 96% and F1-Score of 0.96 in both binary and multi-class classifications. The use of XAI plays a pivotal role in presenting vulnerability detection results to developers, offering prediction probabilities for each word within the code. This model has been integrated into an Application Programming Interface (API) as the backend and further incorporated into Android Studio as a plugin, facilitating real-time vulnerability detection. Notably, Defendroid exhibits high efficiency, delivering prediction probabilities for a single code line in an average processing time of a mere 300 ms. The weight-sharing transparency in the blockchain-driven federated model enhances trust and traceability, fostering community engagement while preserving source code privacy and contributing to accuracy improvement.

Yongkai Fan, Kaile Ma, Linlin Zhang, Xia Lei · 6 authors

The integrity of cloud-based convolutional neural network (CNN) prediction services can be jeopardized by a malicious cloud server. Although zero-knowledge proof approaches can be used to verify integrity, they are difficult to use for larger CNN models like LeNet-5 and VGG16, due to the large cost (in terms of time and storage) of generating a proof. This paper proposes ValidCNN, which can efficiently generate integrity proofs based zk-SNARK. At the heart of ValidCNN, it is a novel usage of Freivald's concepts for circuit construction, and a more efficient way for verifying matrix multiplication. Our experimental results demonstrate that VaildCNN significantly outperforms the state-of-the-art approaches that are based on zk-SNARK. For example, compared with ZEN, VaildCNN achieves a 12-fold improvement in time and a 31-fold improvement in storage. Compared with vCNN, VaildCNN achieves a 195-fold and 279-fold improvement in time and storage respectively.

Rohini Pise, Sonali Patil

No abstract is available for this record.

Chavhan Sujeet Yashavant

Smart contracts are a driving force for the Ethereum blockchain. A smart contract is a code that resides on blockchain and executes when certain predetermined conditions are satisfied. Ethereum smart contracts handle ether (a cryptocurrency) equivalent to millions of dollars and other essential assets. A bug in the smart contract can cause financial loss and damage to essential assets. The Ethereum community has developed ample tools to detect bugs in smart contracts. However, the tools generate false reports. We plan to integrate existing tools and detect smart contract bugs by combining the best of each tool. We use the Logistic Regression model to combine the tools. We are working on a training dataset of around 40K real-world Ethereum smart contracts labelled with vulnerabilities for the Logistic Regression model. We also plan to improve individual tools by finding the drawbacks of each tool. Finally, we aim to develop a new tool that will be better than existing tools regarding precision and recall.

Yuying Du, Xueyan Tang

Smart contracts, as a key component of blockchain technology, play a crucial role in ensuring the automation of transactions and adherence to protocol rules. However, smart contracts are susceptible to security vulnerabilities, which, if exploited, can lead to significant asset losses. This study explores the potential of enhancing smart contract security audits using the GPT-4 model. We utilized a dataset of 35 smart contracts from the SolidiFI-benchmark vulnerability library, containing 732 vulnerabilities, and compared it with five other vulnerability detection tools to evaluate GPT-4's ability to identify seven common types of vulnerabilities. Moreover, we assessed GPT-4's performance in code parsing and vulnerability capture by simulating a professional auditor's auditing process using CoT(Chain of Thought) prompts based on the audit reports of eight groups of smart contracts. We also evaluated GPT-4's ability to write Solidity Proof of Concepts (PoCs). Through experimentation, we found that GPT-4 performed poorly in detecting smart contract vulnerabilities, with a high Precision of 96.6%, but a low Recall of 37.8%, and an F1-score of 41.1%, indicating a tendency to miss vulnerabilities during detection. Meanwhile, it demonstrated good contract code parsing capabilities, with an average comprehensive score of 6.5, capable of identifying the background information and functional relationships of smart contracts; in 60% of the cases, it could write usable PoCs, suggesting GPT-4 has significant potential application in PoC writing. These experimental results indicate that GPT-4 lacks the ability to detect smart contract vulnerabilities effectively, but its performance in contract code parsing and PoC writing demonstrates its significant potential as an auxiliary tool in enhancing the efficiency and effectiveness of smart contract security audits.

Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri

Machine learning models are increasingly used in societal applications, yet legal and privacy concerns demand that they very often be kept confidential. Consequently, there is a growing distrust about the fairness properties of these models in the minds of consumers, who are often at the receiving end of model predictions. To this end, we propose \name -- a system that uses Zero-Knowledge Proofs (a cryptographic primitive) to publicly verify the fairness of a model, while maintaining confidentiality. We also propose a fairness certification algorithm for fully-connected neural networks which is befitting to ZKPs and is used in this system. We implement \name in Gnark and demonstrate empirically that our system is practically feasible. Code is available at https://github.com/infinite-pursuits/FairProof.

Haya R. Hasan, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob · 5 authors

The metaverse has gained immense popularity in recent years. However, the widespread adoption of this immersive virtual environment faces a substantial challenge from deepfake technology. The proliferation of deepfakes poses a significant risk as it enables the dissemination of misinformation and manipulated digital media. In this paper, we propose a blockchain and Non-Fungible Tokens (NFTs) based solution to combat the threats associated with deepfakes. Our proposed solution is applicable to various types of digital media found on the internet, social media platforms, and within the metaverse. We incorporate decentralized off-chain storage to ensure the security and integrity of digital media. This leads to preserving both the digital media and associated NFT metadata in a secure manner. We leverage the intrinsic features of blockchain, such as tamper-proof logs and data provenance, to enable users to verify the authenticity of digital media. We introduce a decentralized reputation system that employs equations, trust factors, and trust badges. Through this system, digital creators are incentivized with trust badges based on their trust factor values, which enhances the overall credibility of the content. We present the cost and security analyses as well as provide a comparative evaluation against existing solutions. We make our smart contract code publicly available on GitHub.

Shiuh‐Pyng Shieh, J. Voas, Phil Laplante, Jason Rupe · 9 authors

The convergence of technologies is happening across various aspects, such as communication, computing, medicine, and transportation. The smartphone is a perfect example of convergence, packing features, such as a camera, GPS, artificial intelligence, and Internet connectivity into one sleek device. Autonomous driving is another good example. In a time of rapidly converging technologies, reliability engineering must take into account the potential for cyber threats, the need for cyber trust, the importance of cyber security, and the criticality of cyber resilience. In this way, reliability engineers can ensure the confidentiality, integrity, and availability of computer systems and networks in the face of evolving threats and changing technologies. In this article, we introduce the challenges and current progress of reliability engineering in emerging technologies, including practices and applications of cyber trust and security, AI-empowered autonomous driving systems, modern mobile networks, blockchains and distributed ledger technologies, prognostic and health management, integrated circuit and hardware, and enterprise cybersecurity and threat hunting.

Bianca-Mihaela Ganescu, Jonathan Passerat‐Palmbach

Generative AI, exemplified by models like transformers, has opened up new possibilities in various domains but also raised concerns about fairness, transparency and reliability, especially in fields like medicine and law. This paper emphasizes the urgency of ensuring fairness and quality in these domains through generative AI. It explores using cryptographic techniques, particularly Zero-Knowledge Proofs (ZKPs), to address concerns regarding performance fairness and accuracy while protecting model privacy. Applying ZKPs to Machine Learning models, known as ZKML (Zero-Knowledge Machine Learning), enables independent validation of AI-generated content without revealing sensitive model information, promoting transparency and trust. ZKML enhances AI fairness by providing cryptographic audit trails for model predictions and ensuring uniform performance across users. We introduce snarkGPT, a practical ZKML implementation for transformers, to empower users to verify output accuracy and quality while preserving model privacy. We present a series of empirical results studying snarkGPT's scalability and performance to assess the feasibility and challenges of adopting a ZKML-powered approach to capture quality and performance fairness problems in generative AI models.

Liquan Chen, Yaqing Zhu, Suhui Liu, Hongtao Yu · 5 authors

No abstract is available for this record.

Shuohan Wu, Zihao Li, Luyi Yan, Weimin Chen · 8 authors

Given the growing importance of smart contracts in various applications, ensuring their security and reliability is critical. Fuzzing, an effective vulnerability detection technique, has recently been widely applied to smart contracts. Despite numerous studies, a systematic investigation of smart contract fuzzing techniques remains lacking. In this paper, we fill this gap by: 1) providing a comprehensive review of current research in contract fuzzing, and 2) conducting an in-depth empirical study to evaluate state-of-the-art contract fuzzers' usability. To guarantee a fair evaluation, we employ a carefully-labeled benchmark and introduce a set of pragmatic performance metrics, evaluating fuzzers from five complementary perspectives. Based on our findings, we provide direction for the future research and development of contract fuzzers.

Chaofan Shou, Jing Liu, Doudou Lu, Koushik Sen

As blockchain platforms grow exponentially, millions of lines of smart contract code are being deployed to manage extensive digital assets. However, vulnerabilities in this mission-critical code have led to significant exploitations and asset losses. Thorough automated security analysis of smart contracts is thus imperative. This paper introduces LLM4Fuzz to optimize automated smart contract security analysis by leveraging large language models (LLMs) to intelligently guide and prioritize fuzzing campaigns. While traditional fuzzing suffers from low efficiency in exploring the vast state space, LLM4Fuzz employs LLMs to direct fuzzers towards high-value code regions and input sequences more likely to trigger vulnerabilities. Additionally, LLM4Fuzz can leverage LLMs to guide fuzzers based on user-defined invariants, reducing blind exploration overhead. Evaluations of LLM4Fuzz on real-world DeFi projects show substantial gains in efficiency, coverage, and vulnerability detection compared to baseline fuzzing. LLM4Fuzz also uncovered five critical vulnerabilities that can lead to a loss of more than $247k.

Shoupeng Ren, Lipeng He, Tian-Yu Tu, Di Wu · 7 authors

The exploitation of smart contract vulnerabilities in Decentralized Finance (DeFi) has resulted in financial losses exceeding 3 billion US dollars. Existing defense mechanisms primarily focus on detecting and reacting to adversarial transactions executed by attackers that target victim contracts. However, with the emergence of private transaction pools where transactions are sent directly to miners without first appearing in public mempools, current detection tools face significant challenges in identifying attack activities effectively. Based on the fact that most attack logic rely on deploying intermediate smart contracts as supporting components to the exploitation of victim contracts, novel detection methods have been proposed that focus on identifying these adversarial contracts instead of adversarial transactions. However, previous state-of-the-art approaches in this direction have failed to produce results satisfactory enough for real-world deployment. In this paper, we propose LookAhead, a new framework for detecting DeFi attacks via unveiling adversarial contracts. LookAhead leverages common attack patterns, code semantics and intrinsic characteristics found in adversarial smart contracts to train Machine Learning (ML)-based classifiers that can effectively distinguish adversarial contracts from benign ones and make timely predictions of different types of potential attacks. Experiments on our labeled datasets show that LookAhead achieves an F1-score as high as 0.8966, which represents an improvement of over 44.4% compared to the previous state-of-the-art solution, with a False Positive Rate (FPR) at only 0.16%.

Tongzhou Shen, Liehuang Zhu, Feng Gao, Zhuo Chen · 6 authors

As a decentralized network infrastructure, the data sent to the blockchain are public and temper-evident. The cover of massive normal transactions in a blockchain network is ideal for constructing a stable and reliable covert channel to achieve one-to-many group covert communication. Existing blockchain-based covert communication schemes face challenges in balancing concealment, embedding rate and filtering efficiency, making them unsuitable for direct extension to group scenarios. Adopting a key-leakage scheme can increase the channel capacity while maintaining high concealment from external adversaries. However, it will also expose more knowledge to the receiver. A malicious receiver has the ability to steal a sender’s identity or replay historical transactions to control the entire channel. In this paper, we define the capabilities of malicious receivers in blockchain-based group covert communication scenarios and propose a group covert communication scheme resistant to transaction forgery attacks. Theoretical analysis and experiments prove that our covert transactions do not have any transaction correlativity, ensuring the unique authenticity of the sender’s identity while maintaining supreme concealment compared with the existing schemes. The precision and recall of machine learning detection results can reach 0.57–0.62 (0.5 is the ideal value).

Yongkai Fan, Binyuan Xu, Linlin Zhang, Gang Tan · 7 authors

Model prediction based on machine learning is provided as a service in cloud environments, but how to verify that the model prediction service is entirely conducted becomes a critical challenge. Although zero-knowledge proof techniques potentially solve the integrity verification problem when applied to the prediction integrity of massive privacy-preserving Convolutional Neural Networks (CNNs), the significant proof burden results in low practicality. In this research, we present psvCNN (parallel splitting zero-knowledge technique for integrity verification). The psvCNN scheme effectively improves the utilization of computational resources in CNN prediction integrity, proving by an independent splitting design. Through a convolutional kernel-based model splitting design and an underlying zero-knowledge succinct non-interactive knowledge argument, our psvCNN develops parallelizable zero-knowledge proof circuits for CNN prediction. Furthermore, psvCNN presents an updated Freivalds algorithm for a faster integrity verification process. Experiments show that psvCNN is practical and efficient in terms of proof time and storage, generating a prediction integrity proof with a proof size of 1.2MB in 7.65s for the structurally complicated CNN model VGG16. psvCNN is 3765 times faster than the latest zk-SNARK-based non-interactive method vCNN, and 12 times faster than the latest sumcheck-based interactive technique zkCNN in terms of proving time.

Huiwen Yang, Xiguo Gu, Xiang Chen, Liwei Zheng · 5 authors

Smart contracts are computer programs that run on a blockchain. As the functions implemented by smart contracts become increasingly complex, the number of cross-contract interactions within them also rises. Consequently, the combinatorial explosion of transaction sequences poses a significant challenge for smart contract security vulnerability detection. Existing static analysis-based methods for detecting cross-contract vulnerabilities suffer from high false-positive rates and cannot generate test cases, while fuzz testing-based methods exhibit low code coverage and may not accurately detect security vulnerabilities. The goal of this paper is to address the above limitations and efficiently detect cross-contract vulnerabilities. To achieve this goal, we present CrossFuzz, a fuzz testing-based method for detecting cross-contract vulnerabilities. First, CrossFuzz generates parameters of constructors by tracing data propagation paths. Then, it collects inter-contract data flow information. Finally, CrossFuzz optimizes mutation strategies for transaction sequences based on inter-contract data flow information to improve the performance of fuzz testing. We implemented CrossFuzz, which is an extension of ConFuzzius, and conducted experiments on a real-world dataset containing 396 smart contracts. The results show that CrossFuzz outperforms xFuzz, a fuzz testing-based tool optimized for cross-contract vulnerability detection, with a 10.58% increase in bytecode coverage. Furthermore, CrossFuzz detects 1.82 times more security vulnerabilities than ConFuzzius. Our method utilizes data flow information to optimize mutation strategies. It significantly improves the efficiency of fuzz testing for detecting cross-contract vulnerabilities.

Rizwan Ur Rahman, Kuldeep Singh, Deepak Singh Tomar, Rabia Musheer

No abstract is available for this record.

Shweta Mayor Sabharwal, Sonia Chhabra, Manpreet Kaur Aiden

No abstract is available for this record.

Rashi Nimesh Kumar Dhenia, Raghavendra Sridhar, Ishva Jitendrakumar Kanani

As machine learning permeates sensitive domains such as healthcare, finance, and government, protecting individual privacy while leveraging large-scale data remains a paramount challenge. Privacy-Preserving Machine Learning (PPML) combines cryptographic techniques, decentralized training paradigms, and data governance policies to enable secure and compliant model development. This paper provides a comprehensive survey of fundamental PPML methods differential privacy, federated learning, homomorphic encryption and examines key data governance frameworks underpinning ethical AI adoption. We analyze technical trade-offs, including privacy-utility balance, scalability, and adversarial resilience. Finally, ongoing research directions and policy implications are discussed, emphasizing interdisciplinary collaboration for trustworthy AI deployment.

Zhuo Chen, Liehuang Zhu, Peng Jiang, Can Zhang · 6 authors

Blockchain-based censorship-resistant systems enable the user to access the blocked content through a covert channel while avoiding a suspicious network connection between the user and the proxy. However, state-of-the-art blockchain-based censorship-resistant schemes cannot satisfy both low communication fees and unobservability, and their method of identifying transactions with covert data may inadvertently expose the covert channel. In this paper, we present Hades, a blockchain-based covert channel framework that aims to circumvent censorship. Hades allows users to encode covert data as a transaction field, and identify transactions with covert data by using another transaction field as a label. We also present the security model for Hades, which defines the unobservability of Hades as the indistinguishability of transactions with covert data from normal transactions. We further propose two cost-friendly and unobservable instantiations of Hades: the basic RDSAC and the improved DDSAC. RDSAC uses private keys to encode covert data and utilizes random factors in the signing process as labels, while incurring a communication delay. DDSAC avoids the delay by encoding covert data into random factors and sampling a transaction amount from normal transactions as the label. We implement a prototype system of Hades and evaluate its performance. Experiment results show that our Hades prototype is unobservable, robust, and efficient. RDSAC and DDSAC can identify 1,654 transactions in 6.054 seconds and 0.071 seconds, respectively. Hades supports 1KB data transfer at $0.44 on the Bitcoin mainnet and cost-free data transfer on the Bitcoin testnet.

Paul Bilokon

No abstract is available for this record.

Subhasis Thakur, John G. Breslin

In MLaaS, DNN models are kept in a server operated by the service provider and inputs to the DNN models are provided by the clients. Such inputs are used to execute the DNN models and classification results are sent back to the client. In MLaaS, the DNN model owner does not reveal the DNN model parameters to the client. MLaaS there are a few trust problems: (a) The server may not be secure and an attacker may send manipulated classification results to the client. In the case of safety-critical systems using such classification in the decision-making process, an attacker may specifically manipulate the classification result to disrupt the operations of the safety-critical system, (b) The server may intentionally send wrong or random classification results without executing the DNN model to respond to a massive number of classification requests from the clients. In this paper, we investigate the problem of verifying DNN model execution by the service provider in an MLaaS paradigm. A proof of DNN model execution will prove that given an input, the DNN model is executed to generate the classification result by providing sequences of outputs of all functions used in the DNN model. As the service provider in MLaaS does not share the DNN model with the client, we need to verify DNN function outcomes without the knowledge of DNN function parameters. Hence zero-knowledge proof can be used for verifying DNN model execution. In this paper, we use Zero-Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) which reduces the size of proof and complexity of proof verification considerably. In particular, we use a quadratic arithmetic program-based zkSNARK for DNN model verification. Our main results in this paper are as follows: (a) We have developed a DNN model execution verification method using a QAP-based zkSNARK. (b) We prove that the verification protocol is correct and privacy-preserving. (c) We analyzed the cost of using such a verification protocol.