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762 results · page 18 of 32
Jiaze Sun, Zhiqiang Yin, Hengshan Zhang, Xiang Chen · 5 authors
No abstract is available for this record.
Zhongkai Lu, Lingling Wang, Zhengyin Zhang, Mei Huang · 6 authors
Federated learning is a widely used method for collaborative machine learning without sharing local data. In this approach, participants train models using their local data, and the model updates are aggregated into a global model. However, ensuring trustworthy model training is crucial because malicious participants may not use their actual local data or may not train the model as intended, which makes it challenging to guarantee the authenticity of the data and the integrity of the model training. To address these issues, we propose a trustworthy model training scheme (TMT-FL) with verifiable authenticity and integrity. Specifically, we leverage zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) based proofs to verify the integrity of the training execution. To deal with the performance bottleneck in generating zk-SNARK proofs, we use the Chinese Remainder Theorem to optimize the convolution operation, and present an improved zk-SNARK based proof generating scheme which significantly reduces the online proving time. Besides, we adopt matrix commitment along with bloom filter to ensure the authenticity and integrity of the training datasets. Extensive experimental results demonstrate that our improved zk-SNARK scheme performs nearly$3.1\times$faster than the state-of-the-art in online proving time. Moreover, we experimentally confirm the efficiency of TMT-FL under diverse datasets in terms of computational costs, storage costs, and communication overheads.
B. Subashini, Haaniya Iram, Anna Anbumozhi
One revolutionary way to tackle privacy and security issues in federated learning (FL) is to include blockchain technology and zero-knowledge proofs (ZK) into machine learning frameworks. To strengthen FL's defences against threats such as model poisoning attacks, this work investigates the use of ZK proofs. This study presents a new technique that uses secure multi-party computation (MPC) to efficiently detect poisoned models, addressing the shortcomings of previous ZK systems. Data anonymization, encryption of sensitive information, and encoding of categorical data all contribute to the proposed model's privacy-preserving features. Adding a privacy-protecting layer is an integral part of ML model integration. ZK circuits employ ZK-SNARKs or Bulletproofs to generate proofs that the ML model may use to predict without disclosing the data. ZK-SNARKs are trusted, and request validation and data access rules control proof access.
Maitri Kushwaha, Arnab Mukherjee, Aishwarya Pandey, Raju Halder
No abstract is available for this record.
A. Zhalilov, A. Toktorbaev
This study investigates the critical challenges associated with ensuring the security and robustness of artificial intelligence (AI) systems, especially within high-stakes applications such as autonomous vehicles, healthcare, and financial technologies. The primary objective is to identify vulnerabilities in AI algorithms and propose effective mitigation strategies. The research emphasizes contemporary threats, including adversarial attacks, algorithmic opacity, data breaches, and the ethical ramifications of AI deployment. A review of current literature reveals that adversarial attacks, where subtle input perturbations cause significant misclassifications, present a considerable risk to AI reliability. Techniques such as robust training, involving training models on adversarial examples, have shown effectiveness in improving resilience, albeit with higher computational demands. The study also explores the importance of explainable AI (XAI) tools like LIME and SHAP, which enhance transparency by clarifying the decision-making processes of complex models. This transparency is vital for fostering user trust, especially in fields like medicine and finance, where understanding AI decisions is essential. XAI approaches enable better oversight and adherence to ethical standards. Data privacy concerns are addressed through methods such as differential privacy, which protects sensitive information by adding noise, and federated learning, which enables decentralized model training without exposing raw data. The findings indicate that these strategies secure data while maintaining model efficacy. By integrating robustness and explainability, this study contributes practical solutions to strengthen AI systems against evolving threats, advancing AI security and fostering trust in these technologies.
S Sato, Hidema Tanaka
No abstract is available for this record.
Zijing Di, Lucas Xia, Wilson Nguyen, Nirvan Tyagi
No abstract is available for this record.
Yibin Yang, David Heath, Carmit Hazay, Vladimir Kolesnikov · 5 authors
We explore Zero-Knowledge Proofs (ZKPs) of statements expressed as programs written in high-level languages, e.g., C or assembly. At the core of executing such programs in ZK is the repeated evaluation of a CPU step, achieved by branching over the CPU's instruction set. This approach is general and covers traversal-execution of a program's control flow graph (CFG): here CPU instructions are straight-line program fragments (of various sizes) associated with the CFG nodes. This highlights the usefulness of ZK CPUs with a large number of instructions of varying sizes.
Christodoulos Pappas, Dimitrios Papadopoulos
Space-efficient SNARKs aim to reduce the prover's space overhead which is one the main obstacles for deploying SNARKs in practice, as it can be prohibitively large (e.g., orders of magnitude larger than natively performing the computation). In this work, we propose Sparrow, a novel space-efficient zero-knowledge SNARK for data-parallel arithmetic circuits with two attractive features: (i) it is the first space-efficient scheme where, for a given field, the prover overhead increases with a multiplicative sublogarithmic factor as the circuit size increases, and (ii) compared to prior space-efficient SNARKs that work for arbitrary arithmetic circuits, it achieves prover space asymptotically smaller than the circuit size itself. Our key building block is a novel space-efficient sumcheck argument with improved prover time which may be of independent interest. Our experimental results for three use cases (arbitrary data parallel circuits, multiplication trees, batch SHA256 hashing) indicate Sparrow outperforms the prior state-of-the-art space-efficient SNARK for arithmetic circuits Gemini (Bootle et al., EUROCRYPT'22) by 3.2-28.7x in total prover space and 3.1-11.3x in prover time. We then use Sparrow to build zero-knowledge proofs of tree training and prediction, relying on its space efficiency to scale to large datasets and forests of multiple trees. Compared to a (non-space-efficient) optimal-time SNARK based on the GKR protocol, we observe prover space reduction of 16-240x for tree training while maintaining essentially the same prover and verifier times and proof size. Even more interestingly, our prover requires comparable space to natively perform the underlying computation. E.g., for a 400MB dataset, our prover only needs 1.4x more space than the native computation.
Kasra Abbaszadeh, Christodoulos Pappas, Jonathan Katz, Dimitrios Papadopoulos
A zero-knowledge proof of training (zkPoT) enables a party to prove that they have correctly trained a committed model based on a committed dataset without revealing any additional information about the model or the dataset. An ideal zkPoT should offer provable security and privacy guarantees, succinct proof size and verifier runtime, and practical prover efficiency. In this work, we present Kaizen, a zkPoT targeted for deep neural networks (DNNs) that achieves all these goals at once. Our construction enables a prover to iteratively train their model via (mini-batch) gradient descent, where the number of iterations need not be fixed in advance; at the end of each iteration, the prover generates a commitment to the trained model parameters attached with a succinct zkPoT, attesting to the correctness of the executed iterations. The proof size and verifier time are independent of the number of iterations.
Preethi Preethi, Mohammed Mujeer Ulla, Ashwitha Anni, Pavithra Narasimha Murthy · 5 authors
Smart contracts are automated agreements executed on a blockchain, offering reliability through their immutable and distributed nature. Yet, their unalterable deployment necessitates precise preemptive security checks, as vulnerabilities could lead to substantial financial damages henceforth testing for vulnerabilities is necessary prior to deployment. This paper presents the graphical attention model distillation and inference network (GAMDI-Net), a pioneering methodology that significantly enhances smart contract vulnerability detection. GAMDI-Net introduces a unique graphical learning module that employs attention mechanism networks to transform complex contract code into a smart graphical representation. In addition to this a dual-modality model distillation and mutual modality learning mechanism, GAMDI-Net excels in synthesizing semantic and control flow data to predict absent bytecode embeddings with high accuracy. This methodology not only improves the precision of vulnerability detection but also addresses scalability and efficiency challenges, reinforcing trust in the deployment of secure smart contracts within the blockchain ecosystem.
Eli Bradley, Brent Waters, David J. Wu
No abstract is available for this record.
Qianyi Zhan, Yuanyuan Liu, Zhenping Xie, Yuan Liu
No abstract is available for this record.
Haijun Wang, Yingmei Hu, Hao Wu, Dijun Liu · 8 authors
Smart contracts are susceptible to various vulnerabilities that can be exploited by hackers via developing adversarial contracts. Existing vulnerability detection techniques often concentrate solely on vulnerable contracts, neglecting adversarial contracts, which may weaken the effectiveness of vulnerability detection and fail to meet practical needs.
Wu Yin, Xiaofei Xie, Chengyu Peng, Dijun Liu · 8 authors
Smart contracts are prone to vulnerabilities, with reentrancy attacks posing significant risks due to their destructive potential. While various methods exist for detecting reentrancy vulnerabilities in smart contracts, such as static analysis, these approaches often suffer from high false positive rates and lack the ability to directly illustrate how vulnerabilities can be exploited in attacks.
Zhen Ling Teo, Xiaoman Zhang, Yechao Yang, Liyuan Jin · 15 authors
No abstract is available for this record.
Patrizio Germani, Michelangelo Amoruso Manzari, Riccardo Magni, Paolo Dibitonto · 6 authors
In recent years, the use of deep learning models in sensitive applications increased exponentially. There is a strong need of having a mechanisms for a transparent and secure inference verification. To this end, we propose a system leveraging Zero-Knowledge Proofs (ZKPs) and Blockchain technologies to ensure the validity of model inferences without revealing neither input data nor model details.In this paper, we propose a system that is capable of making non-interactive proofs that are verified on a Blockchain thus creating a trustless environment between the prover and the verifier. The solution is based on the Easy Zero-Knowledge Inference (EZKL) [1] library and leverages ZK-SNARK [2] proofs. We provide a detailed descriptions of the system’s architecture, the implementation as well as the benefits of this approach in enhancing transparency and security in Artificial Intelligence (AI) applications.
Chitranjan Prasad Sah, Manjot Kaur, Gurwinder Singh
In this paper, security mechanism called zero knowledge proofs has been defined using subset sum problem. We have used subset sum problem for robustness analysis of zero-knowledge proofs technique in cryptography. The upper bound of subset sum problem given by meet-in-the-middle algorithm is more efficient than Nïve algorithm, so we have used meet-in-the-middle algorithm for analyzing robustness in comparison with discrete logarithm problem used by Henry. Also, we have computed covariance between asymptotic notations of meet-in-the-middle and the discrete logarithm problem is employed in the technique of zero knowledge proofs which is positive. Positive covariance between them shows that relationship between input variants used by both functions is similar in behavior. Both functions are growing in same direction and our used function (meet-in-the-middle) is growing faster than Henry (Discrete Logarithm).
Olive Franzese, Ali Shahin Shamsabadi, Luck, Carter, Haddadi, Hamed
The black-box service model enables ML service providers to serve clients while keeping their intellectual property and client data confidential. Confidentiality is critical for delivering ML services legally and responsibly, but makes it difficult for outside parties to verify important model properties such as fairness. Existing methods that assess model fairness confidentially lack either (i) reliability because they certify fairness with respect to a static set of data, and therefore fail to guarantee fairness in the presence of distribution shift or service provider malfeasance; and/or (ii) scalability due to the computational overhead of confidentiality-preserving cryptographic primitives. We address these problems by introducing online fairness certificates, which verify that a model is fair with respect to data received by the service provider online during deployment. We then present OATH, a deployably efficient and scalable zero-knowledge proof protocol for confidential online group fairness certification. OATH exploits statistical properties of group fairness via a cut-and-choose style protocol, enabling scalability improvements over baselines.
Renwen Ma, Kai Hwang, Mo Li and, Yiming Miao
This paper proposes a new global model aggregation method based on using zero-knowledge federated learning (ZKFL). The purpose is to secure horizontal or P2P federated machine learning systems with shorter aggregation times, higher model accuracy, and lower system costs. We use a model parameter-sharing Chord overlay network among all client hosts. The overlay guarantees a trusted sharing of zero-knowledge proofs for aggregation integrity, even under malicious Byzantine attacks. We tested over popular datasets, Fashion-MNIST and CIFAR10, to prove the new system protection concept. Our benchmark experiments validate the claimed advantages of the ZKFL scheme in all objective functions. Our aggregation method can be applied to secure both rank-based and similarity-based aggregation schemes. For a large system with over 200 clients, our system takes only 3 seconds to yield high-precision global machine models under the ALIE attacks with the Fashion-MNIST dataset. We have achieved up to 85% model accuracy, compared to only 3%$\sim$45% accuracy observed with federated schemes without protection. Moreover, our method demands a low memory overhead for handling zero-knowledge proofs as the system scales greatly to a larger number of client nodes.
Raed Bani-Hani, Ahmed S. Shatnawi, Lana Al-Yahya
Smart contracts are programs that reside and execute on a blockchain, like any transaction. They are automatically executed when preprogrammed terms and conditions are met. Although the smart contract (SC) must be presented in the blockchain for the integrity of data and transactions stored within it, it is highly exposed to several vulnerabilities attackers exploit to access the data. In this paper, classification and detection of vulnerabilities targeting smart contracts are performed using deep learning algorithms over two datasets containing 12,253 smart contracts. These contracts are converted into RGB and Grayscale images and then inserted into Residual Network (ResNet50), Visual Geometry Group-19 (VGG19), Dense Convolutional Network (DenseNet201), k-nearest Neighbors (KNN), and Random Forest (RF) algorithms for binary and multi-label classification. A comprehensive analysis is conducted to detect and classify vulnerabilities using different performance metrics. The performance of these algorithms was outstanding, accurately classifying vulnerabilities with high F1 scores and accuracy rates. For binary classification, RF emerged in RGB images as the best algorithm based on the highest F1 score of 86.66% and accuracy of 86.66%. Moving on to multi-label classification, VGG19 stood out in RGB images as the standout algorithm, achieving an impressive accuracy of 89.14% and an F1 score of 85.87%. To the best of our knowledge, and according to the available literature, this study is the first to investigate binary classification of vulnerabilities targeting Ethereum smart contracts, and the experimental results of the proposed methodology for multi-label vulnerability classification outperform existing literature.
Jaehyun Kim, Thi-Thu-Huong Le, Sangmyeong Lee, Howon Kim
In recent years, the proliferation of blockchain technology has led to the widespread adoption of smart contracts, particularly on the Ethereum platform. However, these smart contracts are prone to vulnerabilities that can be exploited, leading to significant financial losses and security breaches. This paper explores the application of Distil-BERT, a distilled version of the BERT (Bidirectional Encoder Representations from Transformers) model, to detect vulnerabilities in Ethereum smart contracts. By fine-tuning Distil-BERT on the SB-curate dataset, a curated dataset of vulnerable Solidity smart contracts, we achieve a performance accuracy exceeding 97%. We provide natural language processing (NLP) techniques that can be leveraged to enhance the accuracy and efficiency of these methods. Our experiments show that Distil-BERT can effectively identify common vulnerabilities, such as reentrancy and integer overflows, outperforming traditional static analysis tools. This research contributes to developing more secure smart contract systems and offers insights into integrating advanced machine learning models in blockchain security.
Abeer Awadallah, Khouloud Eledlebi, Mohamed Jamal Zemerly, Deepak Puthal · 11 authors
The metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions.
Keke Huang, Huidan Hu, Changlu Lin
Unmanned aerial vehicles (UAVs, also known as Drones) have been widely employed in military defense and civilian service. However, as UAVs communicate over insecure open wireless channels, the security challenges and privacy concerns are becoming increasingly prominent. Moreover, some existing schemes to achieve authentication and key agreement (AKA) among UAVs are spliced with the assistance of two UAV-2-GCS mechanisms, which are not flexible enough to be applied in the Internet of Drones (IoD) scenarios. This article proposes a blockchain-assisted AKA scheme for UAVs networks (BAKAS-UAV) referred to as BAKAS-UAV, which addresses security and privacy concerns and overcomes high computational and communication costs in the IoD. A blockchain-based network model is presented in which the ground station acts as an edge node and manages the blockchain, which assists AKA. Based on the network model, both types of AKA mechanisms, UAV-2-GCS and UAV-2-UAV, are proposed, respectively. In particular, the ground control station (GCS) does not participate in the AKA of UAV-2-UAV process; only upon the process is completed the two UAVs synchronize the updated information with GCS. We also implement a smart contract as the authentication service, and the experimental implementation demonstrates the availability of our scheme in IoD. Physical unclonable functions (PUFs) is introduced on the UAVs side to defend against physical capture attacks and also to implement AKA mechanisms. The semantic security is proved formally based on the real-or-random (ROR) model, and the informal analysis shows that the scheme satisfies the demanded security requirements. The scheme’s performance is evaluated by simulating the UAVs and GCS settings with Raspberry Pi 4B and MacOS platforms, respectively, with implementation of several cryptographic primitives. The experimental results show that BAKAS-UAV achieves high efficiency.