Blockchain Papers

Zhelei Zhou, Xinle Cao, Jian Liu, Bingsheng Zhang · 5 authors

No abstract is available for this record.

Ke Zhou, Jieren Cheng, Hui Li, Yuming Yuan · 6 authors

No abstract is available for this record.

Han Yu, Tiantian Ji, Zhongru Wang, Hao Liu · 7 authors

A smart contract honeypot is a special type of smart contract. This type of contract seems to have obvious vulnerabilities in contract design. If a user transfers a certain amount of funds to the contract, then the user can withdraw the funds in the contract. However, once users try to take advantage of this seemingly obvious vulnerability, they will fall into a real trap. Consequently, the user’s investment in the contract cannot be retrieved. The honeypot induces other accounts to launch funds, which seriously threatens the security of property on the blockchain. Detection methods for honeypots are available. However, studying the manner by which to defend existing honeypots is insufficient to fight against honeypots. The new honeypots that may appear in the future from the perspective of an attacker must also be predicted. Therefore, we propose a type of adversarial honeypot. The code and behavioral features of honeypots are obtained through a comparative analysis of the 158,568 non-honeypots and 352 honeypots. To build an adversarial honeypot, we try to separately hide these features and make the honeypot bypass the existing detection technology. We construct 18 instances on the basis of the proposed adversarial honeypot and use an open-source honeypot detection tool to detect these instances. The experimental result shows that the proposed honeypot can bypass the detection tool with a 100% ratio. Therefore, this type of honeypot should be given attention, and defensive measures should be proposed as soon as possible.

João F. Ferreira, Pedro E. D. Cruz, Thomas Durieux, Rui Abreu

Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present SmartBugs, an extensible and easy-to-use execution framework that simplifies the execution of analysis tools on smart contracts written in Solidity, the primary language used in Ethereum. SmartBugs is currently distributed with support for 10 tools and two datasets of Solidity contracts. The first dataset can be used to evaluate the precision of analysis tools, as it contains 143 annotated vulnerable contracts with 208 tagged vulnerabilities. The second dataset contains 47,518 unique contracts collected through Etherscan. We discuss how SmartBugs supported the largest experimental setup to date both in the number of tools and in execution time. Moreover, we show how it enables easy integration and comparison of analysis tools by presenting a new extension to the tool SmartCheck that improves substantially the detection of vulnerabilities related to the DASP10 categories Bad Randomness, Time Manipulation, and Access Control (identified vulnerabilities increased from 11% to 24%).

Yu Feng, Emina Torlak, Rastislav Bodík

This paper presents Solar, a system for automatic synthesis of adversarial contracts that exploit vulnerabilities in a victim smart contract. To make the synthesis tractable, we introduce a query language as well as summary-based symbolic evaluation, which significantly reduces the number of instructions that our synthesizer needs to evaluate symbolically, without compromising the precision of the vulnerability query. We encoded common vulnerabilities of smart contracts and evaluated Solar on the entire data set from Etherscan. Our experiments demonstrate the benefits of summary-based symbolic evaluation and show that Solar outperforms state-of-the-art smart contracts analyzers, teether, Mythril, and ContractFuzzer, in terms of running time and precision.

Yuwei Sun, Hiroshi Esaki, Hideya Ochiai

With the approach of 5G Society, more and more devices have been connected to the Internet, where information is stored, analyzed, and shared. Federated learning allows participants to train a machine learning model through sharing the parameters of it based on local training, instead of raw private data at local. In this research, we propose the implementation of the blockchain in federated learning for local parameters evaluation and global parameter aggregation, thus alleviating the influence of end-point adversarial training data. Besides, all updates of local parameters are encrypted and stored in a block of the blockchain after the consensus by the committee. We evaluate the performance of the scheme when adopting various types of corruption to the adversary's dataset, including noise with various degrees and circle occlusion with various diameters. At last, it shows robust and resilient performance compared with the traditional federated learning, achieving a validation accuracy rate of 0.957 when adding noise with a degree of 1.0, and one of 0.944 when adopting circle occlusion with a diameter of 28 pixels for the classification.

Nathan Martindale, Scott Stewart, Mark Adams, Greg Westphal

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

Abbas Yazdinejad, Reza M. Parizi, Gautam Srivastava, Ali Dehghantanha

Deepfakes generally refers to a new breed of adversarial deep learning technology to create non-consensual contents (mostly videos) for nefarious purposes. Most researches focus on the `detection' of deepfakes using AI-assisted approaches to take on this problem. This has been the common method operandi used by researchers thus far. However, there is one missing aspect of the deepfake problem, which is `authentication'. Instead of attempting to detect what content is fake, in this paper, we focus on techniques to provide tamper-proof evidence of what content is real. Blockchain has been advocated to be helpful with the authentication aspect of many real-world scenarios. Despite the scattered efforts around such solutions, there are no studies that can shed light on where it makes sense to adopt blockchain technology to better take on the deepfake problem. This paper aims to provide a one-stop guide to using blockchain to navigate deepfake artificial intelligence. We discuss potential use cases and solutions to tackle deepfakes technology via blockchain functionalities and features.

Meng Zhang, Pengcheng Zhang, Xiapu Luo, Feng Xiao

State-of-the-art work of evaluating smart contract static analysis tools faces a major problem: most test cases (i.e., labeled buggy contracts) are too simple and lack complexity, which makes the evaluation unable to show the real performance of the analysis tools when complex contracts are analyzed. To fill the gap, we propose a novel source code obfuscation approach for Ethereum smart contracts. We use the buggy contracts in the public dataset11https://github.com/smartbugs/smartbugs/tree/master/dataset to evaluate our approach. The evaluation result shows that our approach can effectively increase the complexity of a contract. Besides, we use obfuscated contracts to evaluate the static analysis tools. The evaluation result shows that the performance of most smart contract static analysis tools decreases in different degree when the original contracts are obfuscated.

Shailendra Rathore, Jong Hyuk Park

With the recent development of Internet of Things (IoT) in the next generation cyber-physical system (CPS) such as autonomous driving, there is a significant requirement of big data analysis with high accuracy and low latency. For efficient big data analysis, deep learning (DL) supports strong analytic capability; it has been applied at the cloud and edge layers by extensive research to provide accurate data analysis at low latency. However, existing researches failed to address certain challenges, such as centralized control, adversarial attacks, security, and privacy. To this end, we propose DeepBlockIoTNet, a secure DL approach with blockchain for the IoT network wherein the DL operation is carried out among the edge nodes at the edge layer in a decentralized, secure manner. The blockchain provides a secure DL operation and removes the control from a centralized authority. The experimental evaluation demonstrates that the proposed approach supports higher accuracy.

Ye Liu, Yi Li, Shang‐Wei Lin, Qiang Yan

Unlike those on public permissionless blockchains, smart contracts on enterprise permissioned blockchains are not limited by resource constraints, and therefore often larger and more complex. Current testing and analysis tools lack support for such contracts, which demonstrate stateful behaviors and require special treatment in quality assurance. In this paper, we present a model-based testing platform, called ModCon, relying on user-specified models to define test oracles, guide test generation, and measure test adequacy. ModCon is Web-based and supports both permissionless and permissioned blockchain platforms. We demonstrate the usage and key features of ModCon on real enterprise smart contract applications.

Thippa Reddy Gadekallu, M Manoj, Sivarama Krishnan S, Neeraj Kumar · 6 authors

The application of machine learning (ML) algorithms are massively scaling up due to rapid digitization and emergence of new tecnologies like the Internet of Things (IoT). In today's digital era, we can find ML algorithms being applied in the areas of healthcare, IoT, engineering, finance, and more. However, all these algorithms need to be trained in order to predict/solve a particular problem. There is high possibility of tampering with the training datasets and producing biased results. Hence, in this article, we propose a blockchain-based solution to secure the datasets generated from IoT devices for e-health applications. The proposed blockchain-based solution uses private cloud to tackle the aforementioned issue. For evaluation, we have developed a system that can be used by dataset owners to secure their data.

Jiaheng Zhang, Zhiyong Fang, Yupeng Zhang, Dawn Song

Machine learning has become increasingly prominent and is widely used in various applications in practice. Despite its great success, the integrity of machine learning predictions and accuracy is a rising concern. The reproducibility of machine learning models that are claimed to achieve high accuracy remains challenging, and the correctness and consistency of machine learning predictions in real products lack any security guarantees. In this paper, we initiate the study of zero knowledge machine learning and propose protocols for zero knowledge decision tree predictions and accuracy tests. The protocols allow the owner of a decision tree model to convince others that the model computes a prediction on a data sample, or achieves a certain accuracy on a public dataset, without leaking any information about the model itself. We develop approaches to efficiently turn decision tree predictions and accuracy into statements of zero knowledge proofs. We implement our protocols and demonstrate their efficiency in practice. For a decision tree model with 23 levels and 1,029 nodes, it only takes 250 seconds to generate a zero knowledge proof proving that the model achieves high accuracy on a dataset of 5,000 samples and 54 attributes, and the proof size is around 287 kilobytes.

Meng Shen, Huan Wang, Bin Zhang, Liehuang Zhu · 7 authors

Federated learning (FL) serves as an enabling technology for intelligent edge computing, where high-quality machine learning (ML) models are collaboratively trained over large amounts of data generated by various Internet of Things devices while preserving data privacy. To further provide data confidentiality, computation auditability, and participant incentives, the blockchain framework has been incorporated into FL. However, it is an open question whether the model updates from participants in blockchain-assisted FL can disclose properties of the private data the participants are unintended to share. In this article, we propose a novel property inference attack that exploits the unintended property leakage in blockchain-assisted FL for intelligent edge computing. More specifically, we present an active attack to learn the property leakage from model updates of participants and to identify a set of participants with a certain property. We also design a dynamic participant selection strategy tailored to the setting of large-scale FL, which accelerates the selection process of target participants and improves attack accuracy. We evaluate the proposed attack through extensive experiments with publicly available data sets. The experimental results demonstrate that the proposed attack is effective and efficient in inferring various properties of training data, while maintaining the high quality of the main tasks in FL.

Alexander Leid, Brink van der Merwe, Willem Visser

Ethereum smart contract exploits have inflicted enormous monetary damage due to vulnerabilities introduced accidentally by the contract authors. Many of these errors can now be detected automatically by a growing number of security analysis tools that specifically target the most common vulnerabilities present in the Ethereum smart contract ecosystem. The aim of this work is to identify state-of-the-art security analysis tools that assist auditors in automatically testing and verifying real-world contracts. We compare two such symbolic executioners, Manticore (which we also extend) and Mythril, and one fuzz tester, Echidna, to evaluate their effectiveness when analysing a set of challenge contracts hosted online, as well as twenty of the most popular ERC-20 tokens found on the main Ethereum network. Our results showed that the tools were able to solve 24 of the 39 challenge contracts and both symbolic tools achieved on average more than 80% code coverage on successful evaluations of the popular ERC-20 token data. Code coverage plateaued after the second symbolic transaction, suggesting a good performance target for continuous integration environments.

Daniel Soto, Alexandre Bergel, Alejandro Hevia

This paper studies how a simple approach based on fuzzing testing can help authors of Solidity contracts to accurately estimate the gas cost of services specified in a contract. Our fuzzer creates a private blockchain and randomly generates transactions. Such an environment is meant to simulate large scale behavior that may be seen in a public blockchain. Our fuzzer handles Ethereum starting and target endpoints in a transaction to accommodate requirements expressed in financial contracts. By comparing the gas computation made by the Ethereum Solidity compiler and the actual consumption during our fuzzing, we are able to find discrepancies between predicted and real gas consumption. Our findings are beneficial to transaction authors to correctly predict the computing resources of Ethereum miners.

Ken Naganuma, Masayuki Yoshino, A. Inoue, Yukinori Matsuoka · 6 authors

In recent years, the zero-knowledge proof and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) have drawn significant attention as privacy-enhancing technologies in various domains, especially the cryptocurrency industry and verifiable computations. A post-quantum designated verifier type zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) for Boolean circuits was proposed by Gennaro et al. in ACM CCS `18. However, this scheme does not include arithmetic circuits. Furthermore, it is difficult to use it in various applications. Their paper described the construction of a post-quantum designated verifier zk-SNARK for arithmetic circuits from quadratic arithmetic programs (QAPs) as an open problem. Recently, Nitulescu proposed a post-quantum designated verifier zk-SNARK for arithmetic circuits using square arithmetic programs (SAPs), which are the special cases of QAPs.In this paper, we give another answer to this problem and propose a post-quantum designated verifier zk-SNARK scheme for arithmetic circuits using QAPs. Our proposal, which employs QAPs, the zero-knowledge proof comprises three learning with errors (LWE) ciphertexts. We implemented our proposed scheme and the other known schemes using the libsnark library. Our experimental results show that our scheme can generate a zero-knowledge proof, which is known as the bottleneck of zk-SNARK, for an arithmetic circuit that comprises 216gates in a processing time of only 50 s, which is approximately three times faster than that of the post-quantum zk-SNARKs by Gennaro et al. or two times faster than the one by Nitulescu.

Yuhe Huang, Bo Jiang, W. K. Chan

EOSIO is one typical public blockchain platform. It is scalable in terms of transaction speeds and has a growing ecosystem supporting smart contracts and decentralized applications. However, the vulnerabilities within the EOSIO smart contracts have led to serious attacks, which caused serious financial loss to its end users. In this work, we systematically analyzed three typical EOSIO smart contract vulnerabilities and their related attacks. Then we presented EOSFuzzer, a general black-box fuzzing framework to detect vulnerabilities within EOSIO smart contracts. In particular, EOSFuzzer proposed effective attacking scenarios and test oracles for EOSIO smart contract fuzzing. Our fuzzing experiment on 3963 EOSIO smart contracts shows that EOSFuzzer is both effective and efficient to detect EOSIO smart contract vulnerabilities with high accuracy.

João F. Ferreira, Pedro E. D. Cruz, Thomas Durieux, Abreu, Rui

Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present SmartBugs, an extensible and easy-to-use execution framework that simplifies the execution of analysis tools on smart contracts written in Solidity, the primary language used in Ethereum. SmartBugs is currently distributed with support for 10 tools and two datasets of Solidity contracts. The first dataset can be used to evaluate the precision of analysis tools, as it contains 143 annotated vulnerable contracts with 208 tagged vulnerabilities. The second dataset contains 47,518 unique contracts collected through Etherscan. We discuss how SmartBugs supported the largest experimental setup to date both in the number of tools and in execution time. Moreover, we show how it enables easy integration and comparison of analysis tools by presenting a new extension to the tool SmartCheck that improves substantially the detection of vulnerabilities related to the DASP10 categories Bad Randomness, Time Manipulation, and Access Control (identified vulnerabilities increased from 11% to 24%).

Zhenbin Yan, Yi Deng

Abstract Round complexity is one of the fundamental problems in zero-knowledge (ZK) proof systems. Non-malleable zero-knowledge (NMZK) protocols are ZK protocols that provide security even when man-in-the-middle adversaries interact with a prover and a verifier simultaneously. It is known that the first constant-round public-coin NMZK arguments for NP can be constructed by assuming the existence of collision-resistant hash functions (Pass, R. and Rosen, A. (2005) New and Improved Constructions of Non-Malleable Cryptographic Protocols. In Gabow, H.N. and Fagin, R. (eds) Proc. 37th Annual ACM Symposium on Theory of Computing, Baltimore, MD, USA, May 2224, 2005, pp. 533542. ACM) and has relatively high round complexity; the first four-round private-coin NMZK arguments for NP can be constructed in the plain model by assuming the existence of one-way functions (Goyal, V., Richelson, S., Rosen, A. and Vald, M. (2014) An Algebraic Approach to Non-Malleability. In 55th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2014, Philadelphia, PA, USA, October 1821, 2014, pp. 4150. IEEE Computer Society and Ciampi, M., Ostrovsky, R., Siniscalchi, L. and Visconti, I. (2017) Delayed-Input Non-Malleable Zero Knowledge and Multi-Party Coin Tossing in Four Rounds. In Kalai, Y. and Reyzin, L. (eds) Theory of Cryptography15th Int. Conf., TCC 2017. Lecture Notes in Computer Science, Baltimore, MD, USA, November 1215, 2017, Part I, Vol. 10677, pp. 711742. Springer). In this paper, we present a six-round public-coin NMZK argument of knowledge system assuming the existence of collision-resistant hash functions and a three-round private-coin NMZK argument system from multi-collision resistance of hash functions assumption in the keyless setting.

Andrew R. Short, Helen C. Leligou, Michael Papoutsidakis, Efstathios Theocharis

The potential of Federated Learning (FL) deployment increases rapidly as the number of connected devices increases, the value of artificial intelligence is recognized and networking technologies and edge computing evolves. However, as in any distributed system, a set of security issues arise in FL systems. In this paper, we discuss the use of blockchain technology to address diverse security aspects of FL systems and focus on the model poisoning attack for which we propose a novel Blockchain-based defense scheme. An assessment using data from the MNIST database has shown that the proposed approach, which has been designed to be implemented on blockchain technology, offers significant protection against adversaries attempting model poisoning attacks. The approach adopts a novel algorithm for evaluating the model updates, by verifying each model update separately against a verification dataset, without requiring information about the training dataset size, which is often unavailable or easily falsified.

Jiaming Ye, Mingliang Ma, Yun Lin, Yulei Sui · 5 authors

Reentrancy bugs in smart contracts caused a devastating financial loss in 2016, considered as one of the most severe vulnerabilities in smart contracts. Most of the existing general-purpose security tools for smart contracts have claimed to be able to detect reentrancy bugs. In this paper, we present Clairvoyance, a cross-function and cross-contract static analysis by identifying infeasible paths to detect reentrancy vulnerabilities in smart contracts. To reduce FPs, we have summarized five major path protective techniques (PPTs) to support fast yet precise path feasibility checking. We have implemented our approach and compared Clairvoyance with three state-of-the-art tools on 17770 real-worlds contracts. The results show that Clairvoyance yields the best detection accuracy among all the tools.

Pengcheng Zhang, Jianan Yu, Shunhui Ji

Testing is an important technique to improve the quality of Ethereum smart contract programs. However, current work on testing smart contract only focus on static problems of smart contract programs. A data flow oriented test case generation approach for dynamic testing of smart contract programs is still missing. To address this problem, this paper proposes a novel test case generation approach, called ADF-GA (All-uses Data Flow criterion based test case generation using Genetic Algorithm), for Solidity based Ethereum smart contract programs. ADF-GA aims to efficiently generate a valid set of test cases via three stages. First, the corresponding program control flow graph is constructed from the source codes. Second, the generated control flow graph is analyzed to obtain the variable information in the Solidity programs, locate the require statements, and also get the definition-use pairs to be tested. Finally, a genetic algorithm is used to generate test cases, in which an improved fitness function is proposed to calculate the definition-use pairs coverage of each test case with program instrumentation. Experimental studies are performed on several representative Solidity programs. The results show that ADF-GA can effectively generate test cases, achieve better coverage, and reduce the number of iterations in genetic algorithm.

Sungjae Hwang, Sukyoung Ryu

Ethereum, one of the most popular blockchain platforms, provides financial transactions like payments and auctions through smart contracts. Due to the immense interest in smart contracts in academia, the research community of smart contract security has made a significant improvement recently. Researchers have reported various security vulnerabilities in smart contracts, and developed static analysis tools and verification frameworks to detect them. However, it is unclear whether such great efforts from academia has indeed enhanced the security of smart contracts in reality.