Verifying the correctness of computation without revealing the input is a critical issue intensively studied in real-world applications. The recent surge of zero knowledge arguments has been focusing on its efficiency and practicality. Among them, GKR-based arguments have received wide attention and become the foundation of many zero-knowledge proof protocols. However, GKR-based protocols are restricted to layered arithmetic circuits. We proposeTerrace, a new, efficient zero-knowledge argument system for general circuits, based on GKR. By dynamically patching cross-layer claims to the original circuit for verification instead of verifying those claims separately,Terraceis able to reduce the total circuit size and thus enjoys a logarithmic factor less verification time and proof size.Terraceis further extended to include the verification of non-arithmetic operations by rewriting those claims in the multilinear extension form. Experimental results demonstrate that Terrace enjoys a competitive performance on efficiency, and shows great promise in enabling low-cost verification of neural networks.
Yijing Lin, Hongyang Du, Dusit Niyato, Jiangtian Nie · 7 authors
The construction of virtual transportation networks requires massive data to be transmitted from edge devices to Virtual Service Providers (VSP) to facilitate circulations between the physical and virtual domains in Metaverse. Leveraging semantic communication for reducing information redundancy, VSPs can receive semantic data from edge devices to provide varied services through advanced techniques, e.g., AI-Generated Content (AIGC), for users to explore digital worlds. But the use of semantic communication raises a security issue because attackers could send malicious semantic data with similar semantic information but different desired content to break Metaverse services and cause wrong output of AIGC. Therefore, in this paper, we first propose a blockchain-aided semantic communication framework for AIGC services in virtual transportation networks to facilitate interactions of the physical and virtual domains among VSPs and edge devices. We illustrate a training-based targeted semantic attack scheme to generate adversarial semantic data by various loss functions. We also design a semantic defense scheme that uses the blockchain and zero-knowledge proofs to tell the difference between the semantic similarities of adversarial and authentic semantic data and to check the authenticity of semantic data transformations. Simulation results show that the proposed defense method can reduce the semantic similarity of the adversarial semantic data and the authentic ones by up to 30% compared with the attack scheme.
Open access
3 source records
Adversarial Robustness in Machine Learning
Big Data and Digital Economy
Physical Unclonable Functions (PUFs) and Hardware Security
Matthew Green, Mathias Hall-Andersen, Eric Hennenfent, Gabriel Kaptchuk · 6 authors
We consider the problem of proving in zero-knowledge the existence of vulnerabilities in executables compiled to run on real-world processors. We demonstrate that it is practical to prove knowledge of real exploits for real-world processor architectures without the need for source code and without limiting our consideration to narrow vulnerability classes. To achieve this, we devise a novel circuit compiler and a toolchain that produces highly optimized, non-interactive zero-knowledge proofs for programs executed on the MSP430, an ISA commonly used in embedded hardware. Our toolchain employs a highly optimized circuit compiler and a number of novel optimizations to construct efficient proofs for program binaries. To demonstrate the capability of our system, we test our toolchain by constructing proofs for challenges in the Microcorruption capture the flag exercises.
Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is a practical zero-knowledge proof system for Rank-1 Constraint Satisfaction (R1CS), enabling privacy preservation and addressing the previous scalability concerns on zero-knowledge proofs. Existing constructions of zk-SNARKs require huge memory overhead to generate proofs in that the size of the zk-SNARK circuit can be large even for a very simple use case, which limits the applications for regular resource-constrained users. To reduce the memory utilization of zk-SNARKs, this paper presents a hash-based method “Split”. Concretely, Split intends to partition the zk-SNARK circuits so that components can be processed sequentially while ensuring strong security properties leveraging hash circuits. As a zk-SNARK circuit is partitioned, obsolete variables are no longer preserved in the memory. We further propose an enhanced Split as$n$-Split, which leads to better optimization by properly choosing multiple splits. Our experimental results validate the effectiveness and efficiency of Split in conserving memory usage for resource-constrained provers as long as the circuit can be partitioned to a Good Split, indicating that via Split zk-SNARKs can be brought one step closer to practical applications.
We provide a generic construction to turn any classical Zero-Knowledge (ZK) protocol into a composable (quantum) oblivious transfer (OT) protocol, mostly lifting the round-complexity properties and security guarantees (plain-model/statistical security/unstructured functions...) of the ZK protocol to the resulting OT protocol. Such a construction is unlikely to exist classically as Cryptomania is believed to be different from Minicrypt. In particular, by instantiating our construction using Non-Interactive ZK (NIZK), we provide the first round-optimal (2-message) quantum OT protocol secure in the random oracle model, and round-optimal extensions to string and k-out-of-n OT. At the heart of our construction lies a new method that allows us to prove properties on a received quantum state without revealing additional information on it, even in a non-interactive way, without public-key primitives, and/or with statistical guarantees when using an appropriate classical ZK protocol. We can notably prove that a state has been partially measured (with arbitrary constraints on the set of measured qubits), without revealing any additional information on this set. This notion can be seen as an analog of ZK to quantum states, and we expect it to be of independent interest as it extends complexity theory to quantum languages, as illustrated by the two new complexity classes we introduce, ZKstatesQIP and ZKstatesQMA.
Blockchain smart contracts have given rise to a variety of interesting and compelling applications and emerged as a revolutionary force for the Internet. Smart contracts from various fields now hold over one trillion dollars worth of virtual coins, attracting numerous attacks. Quite a few practitioners have devoted themselves to developing tools for detecting bugs in smart contracts. One line of efforts revolve around static analysis techniques, which heavily suffer from high false positive rates. Another line of works concentrate on fuzzing techniques. Unfortunately, current fuzzing approaches for smart contracts tend to conduct fuzzing starting from the initial state of the contract, which expends too much energy revolving around the initial state of the contract and thus is usually unable to unearth bugs triggered by other states. Moreover, most existing methods treat each branch equally, failing to take care of the branches that are rare or more likely to possess bugs. This might lead to resources wasted on normal branches. In this paper, we try to tackle these challenges from three aspects: 1) generating function invocation sequences, we explicitly consider data dependencies between functions to facilitate exploring richer states. We further prolong a function invocation sequence$\mathcal {S}_{1}$by appending a new sequence$\mathcal {S}_{2}$, so that the appended sequence$\mathcal {S}_{2}$can start fuzzing from states that are different from the initial state; 2) we incorporate a branch distance-based measure to evolve test cases iteratively towards a target branch; 3) we engage a branch search algorithm to discover rare and vulnerable branches, and design an energy allocation mechanism to take care of exercising these crucial branches. We implement IR-Fuzz and extensively evaluate it over 12K real-world contracts. Empirical results show that: (i) IR-Fuzz achieves 28% higher branch coverage than state-of-the-art fuzzing approaches, (ii) IR-Fuzz detects more vulnerabilities and increases the average accuracy of vulnerability detection by 7% over current methods, and (iii) IR-Fuzz is fast, generating an average of 350 test cases per second. Our implementation and dataset are released athttps://github.com/Messi-Q/IR-Fuzz, hoping to facilitate future research.
Bin Wang, Xiaohan Yuan, Li Duan, Hongliang Ma · 7 authors
With the rapid development of decentralized financial (DeFi), the total value locked (TVL) in DeFi continues to increase. A big number of adversaries exploit logic vulnerabilities to attack DeFi applications for profit, such as flash loan attacks and price manipulation attacks. However, the current vulnerability detection tools for smart contracts cannot be directly used to detect the logic vulnerabilities generated by the combination of different protocols. How to characterize and detect DeFi attacks that exploited logic vulnerabilities is a big challenge. In this work, we propose a deep-learning-based attack detection system on DeFi, called DeFiScanner, in which we design a novel neural network that includes a global model, a local model, and a fusion model to characterize DeFi attacks. First, the unstructured emitted events are automatically and efficiently normalized. Second, the transaction-related features of normalized emitted events are enriched with the global model and the semantic features of emitted events are extracted with the local model. Finally, the transaction-related features and the semantic features of emitted events are fused efficiently with the fusion model to detect DeFi attacks. We collect a dataset that consists of 50 910 real-world DeFi transactions on Ethereum (ETH). The extensive experimental results demonstrate the effectiveness of DeFiScanner. The true positive rate (TPR) and the area under the receiver operating characteristic (ROC) curve of the system reach 0.91 and 0.97, respectively.
Abstract Advances in Deep Learning (DL), Big Data and image processing have facilitated online disinformation spreading through Deepfakes. This entails severe threats including public opinion manipulation, geopolitical tensions, chaos in financial markets, scams, defamation and identity theft among others. Therefore, it is imperative to develop techniques to prevent, detect, and stop the spreading of deepfake content. Along these lines, the goal of this paper is to present a big picture perspective of the deepfake paradigm, by reviewing current and future trends. First, a compact summary of DL techniques used for deepfakes is presented. Then, a review of the fight between generation and detection techniques is elaborated. Moreover, we delve into the potential that new technologies, such as distributed ledgers and blockchain, can offer with regard to cybersecurity and the fight against digital deception. Two scenarios of application, including online social networks engineering attacks and Internet of Things, are reviewed where main insights and open challenges are tackled. Finally, future trends and research lines are discussed, pointing out potential key agents and technologies.
Open access
2 source records
Generative Adversarial Networks and Image Synthesis
Machine Learning as a Service is a promising service for individuals and companies who would like to delegate model training to third parties. The customers desire proof of the integrity of the model training to prevent potential backdoor attacks launched by the server, while the server desires to prove the integrity without revealing their intellectual assets, hyper-parameters of the training scheme. Zero-knowledge proof, a cryptographic tool can theoretically satisfy the above demand, but is still practically infeasible due to the inefficiency of proving. Thus, we propose zkMLaaS, a privacy-preserving and verifiable scheme for efficient training proof generation in the MLaaS scenario. zkMLaaS features a two-round challenge-response pro-tocol equipped with the random sampling. This greatly reduces the time cost of proof generation and ensures the integrity of training procedure simultaneously. We analyze the security of zkMLaaS and conduct comprehensive evaluation which shows it saves around$273\times$times compared with naive scheme.
Su‐Juan Qin, Zhao Liu, Feixiang Ren, Chong Hui Tan
Ethereum is currently one of the most popular blockchain platforms. Smart contracts are an important part of blockchain. Because developers lack understanding of contract security and the huge value of contracts themselves, contracts are often attacked. Therefore, how to effectively detect smart contract vulnerabilities has become a crucial issue. This paper uses deep learning to detect vulnerabilities, which can get rid of dependence on expert experience. In order to solve the problem of poor detection effect caused by excessive noise, this paper proposes a vulnerability detection technology based on critical combination path and deep learning. The critical combination path only contains code related to vulnerabilities, eliminating many invalid codes, thus greatly reducing the impact of noise. At the same time, by analyzing the characteristics of assembly code, a normalization method is proposed to remove many homogeneous codes. The normalized critical combination paths are then vectorized using SimHash, and then converted to grayscale images for classification using a neural network. The experimental results show that the proposed scheme is effective.
In 2016, the famous attack on the smart contract named The DAO led to a financial loss of 60 million dollars. Since then, attacks on smart contracts have increased. Thus, the security issue of smart contracts has attracted more and more attention from the community, putting pressure on developers to discover security issues in smart contracts before deploying them. To address this problem, many researchers have developed smart contract analyzers to early detect potential vulnerabilities. However, a common problem with these tools is a large number of alarms with a high false positive rate. Consequently, developers need to spend much time and effort investigating the alarms which are falsely detected as vulnerable. In this paper, we propose SCAR, a novel approach to prioritize the alarms of static analysis tools. Based on the intuition that alarms with similar contexts tend to have the same labels (true positive or false positive), SCAR is built with two deep learning models to capture the patterns associated with the contexts of the labeled alarms. After that, for new alarms, SCAR calculates their likelihood to be true positives and ranks them according to the predicted scores. SCAR is evaluated on a large data set of 14,184 alarms from 47,518 realworld smart contracts. The results show that the programmers can productively find up to two-thirds of the actual vulnerabilities by investigating only 20% of the ranked alarms.
Shunhui Ji, Shaoqing Zhu, Pengcheng Zhang, Hai Dong
With the widespread use of smart contracts, security incidents caused by improper programming have drawn increasing attention. Data flow correctness is a fundamental and vital requirement for smart contracts. Although test cases generated by existing approaches for data flow testing of smart contracts can achieve certain coverage, not all def-use (definitionuse) pairs are covered, which may result in some errors not being detected. To further improve the adequacy and efficiency of testing, we present an approach named TCG-Re (T est C ase G eneration Combined with Data Flow R eduction) to generate test cases for data flow testing of smart contracts. Data flow reduction is performed to reduce redundant def-use pairs and obtain the final test requirements in a smart contract. The test case generation process is optimized to improve the coverage of the generated test cases for the def-use pairs. We collect 30 smart contracts of different scales to perform experimental evaluation. The result shows that, compared with other existing approaches, TCG-Re can not only achieve full coverage for most contracts, but also be more efficient. In addition, TCG-Re is also effective for discovering several types of errors in smart contracts.
LI Wen-yin, Meng Wang, Bin Yu, Yuhang Shi · 6 authors
As one of the representative Delegated Proof-of-Stake (DPoS) blockchain platforms, EOSIO blockchain platform is developing rapidly in recent years due to its excellent features, such as the scalability of transaction speed and support for smart contracts and decentralized applications. However, vulnerabilities in EOSIO smart contracts have caused serious economic losses and moreover vulnerability detection tools for EOSIO contracts are limited. To overcome the above shortcomings, we implement a grey-box fuzzer called GFuzzer based on WebAssembly for smart contracts on the EOSIO platform considering that EOSIO contracts are not open-sourced. In order to generate more test cases for branches that are difficult to cover, GFuzzer selects test cases with the minimum distance to explore uncovered branches for mutation. We evaluate GFuzzer on 3963 real-world smart contracts and the experimental results show that GFuzzer can detect more vulnerabilities in EOSIO contracts than the existing tools EOSFuzzer and EVulHunter, and is efficient in achieving high branch coverage during vulnerability detection.
The world of web3 is currently being plagued by security risks. In the first half of 2022 alone, web3 projects have lost more than $2 billion due to various security issues, out of which more than 76% of the projects have been audited. At present, the main attack surfaces on the web3 chain, including oracle manipulation, logical loopholes across smart contracts and MEV front-running transactions, cannot be defended through the current mainstream security methods, such as contract auditing, and can only be passively defended after receiving the alerts of malicious attacks. Hence, there is a huge demand for more proactive defense products in the market. However, most of the existing security products and security defense solutions are limited in defense capabilities, as they rely on external mechanisms and thus cannot effectively identify and counter hacker attacks during the contract execution. This paper proposes a new security system for smart contracts on the blockchain and demonstrates how it can actively defend against malicious attacks and provide effective protection for assets on the blockchain through experimental verification and comparison. It compares the existing on-chain security verification solutions as well as the engineering development model and performs logic-based argument verification during on-chain transaction execution and high-precision computation-intensive verification in the off-chain sandbox environment, thereby can prevent risky transactions, protect assets, and promote the security and development of the entire web3.
Currently, Machine Learning as a Service (MLaaS) greatly benefits artificial intelligence (AI) model trading. However, threats such as model piracy and patent grabbing devastatingly violate the copyright of AI models. Current invasive copyright protection solutions mainly rely on watermarking to embed specific information into AI models, which inevitably decreases the accuracy. While non-invasive schemes, such as adversarial samples, cannot guarantee uniqueness as the adversarial sample generation algorithm would be known to all traders, and thus need to be changed after trading. To enable the ownership information transferable across AI model trading, we propose a blockchain-driven Non-Fungible Token (NFT) approach for trading-oriented AI model copyright protection. We design a mapping mechanism from AI models parameters to NFTs which can identify uniqueness and ownership of AI models across trading. Besides, a reputation-based rewards and penalties scheme is proposed to prevent NFT piracy. Lastly, the evaluation verifies the applicability of our approach.
Youwei Huang, Sen Fang, Li, Jianwen, Hu, Bin · 5 authors
Smart contracts on the blockchain offer decentralized financial services but often lack robust security measures, leading to significant economic losses. While substantial research has focused on identifying vulnerabilities in smart contracts, a notable gap remains in evaluating the malicious intent behind their development. To address this, we introduce \textsc{SmartIntentNN} (Smart Contract Intent Neural Network), a deep learning-based tool designed to automate the detection of developers' intent in smart contracts. Our approach integrates a Universal Sentence Encoder for contextual representation of smart contract code, employs a K-means clustering algorithm to highlight intent-related code features, and utilizes a bidirectional LSTM-based multi-label classification network to predict ten distinct categories of unsafe intent. Evaluations on 10,000 real-world smart contracts demonstrate that \textsc{SmartIntentNN} surpasses all baselines, achieving an F1-score of 0.8633. A demo video is available at \url{https://youtu.be/otT0fDYjwK8}.
Youwei Huang, Sen Fang, Jianwen Li, Bin Hu · 6 authors
In recent years, research in software security has concentrated on identifying vulnerabilities in smart contracts to prevent significant losses of crypto assets on blockchains. Despite early successes in this area, detecting developers' intents in smart contracts has become a more pressing issue, as malicious intents have caused substantial financial losses. Unfortunately, existing research lacks effective methods for detecting development intents in smart contracts. To address this gap, we propose \textsc{SmartIntentNN} (Smart Contract Intent Neural Network), a deep learning model designed to automatically detect development intents in smart contracts. \textsc{SmartIntentNN} leverages a pre-trained sentence encoder to generate contextual representations of smart contracts, employs a K-means clustering model to identify and highlight prominent intent features, and utilizes a bidirectional LSTM-based deep neural network for multi-label classification. We trained and evaluated \textsc{SmartIntentNN} on a dataset containing over 40,000 real-world smart contracts, employing self-comparison baselines in our experimental setup. The results show that \textsc{SmartIntentNN} achieves an F1-score of 0.8633 in identifying intents across 10 distinct categories, outperforming all baselines and addressing the gap in smart contract detection by incorporating intent analysis.
Blockchains with smart contracts are distributed ledger systems that achieve block-state consistency among distributed nodes by only allowing deterministic operations of smart contracts. However, the power of smart contracts is enabled by interacting with stochastic off-chain data, which in turn opens the possibility to undermine the block-state consistency. To address this issue, an oracle smart contract is used to provide a single consistent source of external data; but, simultaneously, this introduces a single point of failure, which is called the oracle problem. To address the oracle problem, we propose an adaptive conformal consensus (ACon$^2$) algorithm that derives a consensus set of data from multiple oracle contracts via the recent advance in online uncertainty quantification learning. Interesting, the consensus set provides a desired correctness guarantee under distribution shift and Byzantine adversaries. We demonstrate the efficacy of the proposed algorithm on two price datasets and an Ethereum case study. In particular, the Solidity implementation of the proposed algorithm shows the potential practicality of the proposed algorithm, implying that online machine learning algorithms are applicable to address security issues in blockchains.
Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun
Over the past few years, AI methods of generating images have been increasing in capabilities, with recent breakthroughs enabling high-resolution, photorealistic "deepfakes" (artificially generated images with the purpose of misinformation or harm). The rise of deepfakes has potential for social disruption. Recent work has proposed using ZK-SNARKs (zero-knowledge succinct non-interactive argument of knowledge) and attested cameras to verify that images were taken by a camera. ZK-SNARKs allow verification of image transformations non-interactively (i.e., post-hoc) with only standard cryptographic hardness assumptions. Unfortunately, this work does not preserve input privacy, is impractically slow (working only on 128$\times$128 images), and/or requires custom cryptographic arguments. To address these issues, we present zk-img, a library for attesting to image transformations while hiding the pre-transformed image. zk-img allows application developers to specify high level image transformations. Then, zk-img will transparently compile these specifications to ZK-SNARKs. To hide the input or output images, zk-img will compute the hash of the images inside the ZK-SNARK. We further propose methods of chaining image transformations securely and privately, which allows for arbitrarily many transformations. By combining these optimizations, zk-img is the first system to be able to transform HD images on commodity hardware, securely and privately.
Open access
2 source records
Digital Media Forensic Detection
Adversarial Robustness in Machine Learning
Generative Adversarial Networks and Image Synthesis
Mehmet Parlak, Nurkan Fatih Altunel, Utku Ayaz Akkaş, Emir Tarik Arici
In case of an accident between two autonomous vehicles equipped with emerging technologies, how do we apportion liability among the various players? A special liability regime has not even yet been established for damages that may arise due to the accidents of autonomous vehicles. Would the immutable, time-stamped sensor records of vehicles on distributed ledger help define the intertwined relations of liability subjects right through the accident? What if the synthetic media created through deepfake gets involved in the insurance claims? While integrating AI-powered anomaly or deepfake detection into automated insurance claims processing helps to prevent insurance fraud, it is only a matter of time before deepfake becomes nearly undetectable even to elaborate forensic tools. This paper proposes a blockchain-based insurtech decentralized application to check the authenticity and provenance of the accident footage and also to decentralize the loss-adjusting process through a hybrid of decentralized and centralized databases using smart contracts.