Blockchain Papers

Israelin Insulata J, J. Roselin

No abstract is available for this record.

Jonas Ryan Nouri

No abstract is available for this record.

Dakai Kang, Junchao Chen, Tien Tuan Anh Dinh, Mohammad Sadoghi

The rise of cryptocurrencies like Bitcoin and Ethereum has driven interest in blockchain database technology, with smart contracts enabling the growth of decentralized finance (DeFi). However, research has shown that adversaries exploit transaction ordering to extract profits through attacks like front-running, sandwich attacks, and liquidation manipulation. This issue affects blockchains where block proposers have full control over transaction ordering. To address this, a more fair transaction ordering mechanism is essential. Existing fairness protocols, such as Pompe and Themis, operate on leader-based consensus protocols, which not only suffer from low throughput caused by the single-leader bottleneck, but also allow adversarial block proposers to manipulate transaction ordering. To address these limitations, we propose a new framework, FairDAG, that runs fairness protocols on top of DAG-based consensus protocols. FairDAG improves protocol performance in both throughput and fairness quality by leveraging the multi-proposer design and validity property of DAG-based consensus protocols. We conducted a comprehensive analytical and experimental evaluation of two FairDAG variants - FairDAG-AB and FairDAG-RL. Our results demonstrate that FairDAG outperforms prior fairness protocols in both throughput and fairness quality.

Yejin Kwon, Youngbom Park

No abstract is available for this record.

Abdelrazak A. Yousef Elbunan, Nuradeen K. Emhemed Fethalla, Badriya Abdullah Altarhuni

The rapid proliferation of autonomous robotic systems, ranging from nano-drones to industrial collaborative robots (cobots), is generating massive, distributed datasets. While deep learning (DL) serves as the cornerstone of modern robotic intelligence, the conventional approach of centralizing this data for training poses insurmountable challenges related to privacy, security, bandwidth, and latency. Federated Learning (FL) has emerged as a disruptive paradigm that enables collaborative model training across distributed devices without the need for raw data exchange. However, the integration of FL into real-world robotic swarms—characterized by extreme heterogeneity, dynamic connectivity, and stringent resource constraints—introduces a unique set of complexities that extend far beyond those of conventional edge devices. This survey provides a comprehensive and critical examination of the burgeoning field of FL within robotic and autonomous systems. We move beyond a mere overview to present a novel taxonomy that classifies FL architectures for robotics based on communication topology, learning paradigm, and application criticality. A significant portion of our analysis is dedicated to the potent synergy between FL and Distributed Ledger Technologies (DLTs), particularly blockchain, for achieving decentralized trust, auditability, and robust aggregation in the presence of potentially malicious agents. We extensively review applications across perception, control, and collaborative tasks, highlighting pioneering works in multi-robot SLAM, federated reinforcement learning, and human-robot interaction. Furthermore, we identify and discuss pressing open challenges, including communication efficiency in mobile swarms, energy-aware client selection, personalized learning for non-IID data, and defense mechanisms against sophisticated adversarial attacks. This paper serves as a foundational reference for researchers and practitioners aiming to develop the next generation of private, secure, and collectively intelligent robotic systems.

Changjin Zhao, Xiang Feng, Huiqun Yu

No abstract is available for this record.

Dongwei Xiao, Zhibo Liu, Yiteng Peng, Shuai Wang

Zero-knowledge (ZK) proofs have been increasingly popular in privacy-preserving applications and blockchain systems.To facilitate handy and efficient ZK proof generation for normal users, the industry has designed domain-specific languages (DSLs) and ZK compilers.Given a program in ZK DSL, a ZK compiler compiles it into a circuit, which is then passed to the prover and verifier for ZK checking.However, the correctness of ZK compilers is not well studied, and recent works have shown that de facto ZK compilers are buggy, which can allow malicious users to generate invalid proofs that are accepted by the verifier, causing security breaches and financial losses in cryptocurrency.In this paper, we propose MTZK, a metamorphic testing framework to test ZK compilers and uncover incorrect compilations.Our approach leverages deliberately designed metamorphic relations (MRs) to mutate ZK compiler inputs.This way, ZK compilers can be automatically tested for compilation correctness using inputs and mutated variants without requiring manual intervention.We propose a set of design considerations and optimizations to deliver an efficient and effective testing framework.In the evaluation of four industrial ZK compilers, we successfully uncovered 21 bugs, out of which the developers have promptly patched 15.We also show possible exploitations of the uncovered bugs to demonstrate their severe security implications.

I. R. Solomka, B. B. Liubinskyi, B. O. Peniak

Proof-of-Stake (PoS) blockchains often select committees in direct proportion to stake, which makes security sensitive to large validators and stake concentration. In such settings, a purely stake-based lottery can sometimes produce committees whose adversarial share crosses the safety threshold, even if the global adversarial stake remains below one third. This paper introduces a simple mixed-weight rule that combines stake with a bounded baseline distribution through a single mixing parameter λ. The rule leaves committee size, rewards, and VRF-based sortition unchanged, but pulls weight away from highly concentrated positions. Proved that, whenever the adversary is more concentrated than the baseline, the expected adversarial seats fall linearly in λ, while standard concentration bounds show an exponential drop in committee-capture probability. While the mechanism relies on entity-level attribution (or high-cost identities) to prevent Sybil attacks, experiments on ten production PoS networks indicate that modest mixing (around λ=0.3) reduces expected adversarial seats by about one quarter and tightens worst-case guarantees by orders of magnitude.

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.

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.

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.

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.

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.

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.

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.

Dalila Ressi, Alvise Spanò, Lorenzo Benetollo, Michele Bugliesi · 6 authors

Smart contracts are central to a myriad of critical blockchain applications, from financial transactions to supply chain management. However, their adoption is hindered by security vulnerabilities that can result in significant financial losses. Most vulnerability detection tools and methods available nowadays leverage either static analysis methods or machine learning. Unfortunately, as valuable as they are, both approaches suffer from limitations that make them only partially effective. In this survey, we analyze the state of the art in machine-learning vulnerability detection for Ethereum smart contracts, by categorizing existing tools and methodologies, evaluating them, and highlighting their limitations. Our critical assessment unveils issues such as restricted vulnerability coverage and dataset construction flaws, providing us with new metrics to overcome the difficulties that restrain a sound comparison of existing solutions. Driven by our findings, we discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts. Our guidelines address the known flaws while at the same time opening new avenues for research and development. By shedding light on current challenges and offering novel directions for improvement, we contribute to the advancement of secure smart contract development and blockchain technology as a whole.

Sizai Hou, Songze Li, Tayyebeh Jahani-Nezhad, Giuseppe Caire

Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data while preserving user privacy. However, the typical paradigm of FL faces challenges of both privacy and robustness: the transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates. Current solutions attempting to address both problems under the one-server FL setting fall short in the following aspects: 1) designed for simple validity checks that are insufficient against advanced attacks (e.g., checking norm of individual update); and 2) partial privacy leakage for more complicated robust aggregation algorithms (e.g., distances between model updates are leaked for multi-Krum). In this work, we formalize a novel security notion of aggregated privacy that characterizes the minimum amount of user information, in the form of some aggregated statistics of users' updates, that is necessary to be revealed to accomplish more advanced robust aggregation. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy. As concrete instantiations of PriRoAgg, we construct two secure and robust protocols based on state-of-the-art robust algorithms, for which we provide full theoretical analyses on security and complexity. Extensive experiments are conducted for these protocols, demonstrating their robustness against various model integrity attacks, and their efficiency advantages over baselines.

Hongbo Wen, Hanzhi Liu, Jiaxin Song, Yanju Chen · 6 authors

Blockchain adoption has surged with the rise of Decentralized Finance (DeFi) applications. However, the significant value of digital assets managed by DeFi protocols makes them prime targets for attacks. Current smart contract vulnerability detection tools struggle with DeFi protocols due to deep logical bugs arising from complex financial interactions between multiple smart contracts. These tools primarily analyze individual contracts and resort to brute-force methods for DeFi protocols crossing numerous smart contracts, leading to inefficiency. We introduce Foray, a highly effective attack synthesis framework against deep logical bugs in DeFi protocols. Foray proposes a novel attack sketch generation and completion framework. Specifically, instead of treating DeFis as regular programs, we design a domain-specific language (DSL) to lift the low-level smart contracts into their high-level financial operations. Based on our DSL, we first compile a given DeFi protocol into a token flow graph, our graphical representation of DeFi protocols. Then, we design an efficient sketch generation method to synthesize attack sketches for a certain attack goal (e.g., price manipulation, arbitrage, etc.). This algorithm strategically identifies candidate sketches by finding reachable paths in TFG, which is much more efficient than random enumeration. For each candidate sketch written in our DSL, Foray designs a domain-specific symbolic compilation to compile it into SMT constraints. Our compilation simplifies the constraints by removing redundant smart contract semantics. It maintains the usability of symbolic compilation, yet scales to problems orders of magnitude larger. Finally, the candidates are completed via existing solvers and are transformed into concrete attacks via direct syntax transformation.

Victor Takashi Hayashi, Wilson Vicente Ruggiero

Although hardware trojans impose a relevant threat to the hardware security of RISC-V and Web3 applications, existing datasets have a limited set of examples, as the most famous hardware trojan dataset TrustHub has 106 different trojans. RISC-V specifically has study cases of three and four different hardware trojans, and no research was found regarding Web3 hardware trojans in modules such as a hardware wallet. This research presents a dataset of 290 Verilog examples generated with ChatGPT-4 Large Language Model (LLM) based on 29 golden models and the TrustHub taxonomy. It is expected that this dataset supports future research endeavors regarding defense mechanisms against hardware trojans in RISC-V, hardware wallet, and hardware Proof of Work (PoW) miner.

Dimitris Karydas, Helen C. Leligou

Federated Learning (FL) was first introduced as an idea by Google in 2016, in which multiple devices jointly train a machine learning model without sharing their data under the supervision of a central server. This offers big opportunities in critical areas like healthcare, industry, and finance, where sharing information with other organizations’ devices is completely prohibited. The combination of Federated Learning with Blockchain technology has led to the so-called Blockchain Federated learning (B.F.L.) which operates in a distributed manner and offers enhanced trust, improved security and privacy, improved traceability and immutability and at the same time enables dataset monetization through tokenization. Unfortunately, vulnerabilities of the blockchain-based solutions have been identified while the implementation of blockchain introduces significant energy consumption issues. There are many solutions that also offer personalized ideas and uses. In the field of security, solutions such as security against model-poisoning backdoor assaults with poles and modified algorithms are proposed. Defense systems that identify hostile devices, Against Phishing and other social engineering attack mechanisms that could threaten current security systems after careful comparison of mutual systems. In a federated learning system built on blockchain, the design of reward mechanisms plays a crucial role in incentivizing active participation. We can use tokens for rewards or other cryptocurrency methods for rewards to a federated learning system. Smart Contracts combined with proof of stake with performance-based rewards or (and) value of data contribution. Some of them use games or game theory-inspired mechanisms with unlimited uses even in other applications like games. All of the above is useless if the energy consumption exceeds the cost of implementing a system. Thus, all of the above is combined with algorithms that make simple or more complex hardware and software adjustments. Heterogeneous data fusion methods, energy consumption models, bandwidth, and controls transmission power try to solve the optimization problems to reduce energy consumption, including communication and compute energy. New technologies such as quantum computing with its advantages such as speed and the ability to solve problems that classical computers cannot solve, their multidimensional nature, analyze large data sets more efficiently than classical artificial intelligence counterparts and the later maturity of a technology that is now expensive will provide solutions in areas such as cryptography, security and why not in energy autonomy. The human brain and an emerging technology can provide solutions to all of the above solutions due to the brain's decentralized nature, built-in reward mechanism, negligible energy use, and really high processing power In this paper we attempt to survey the currently identified threats, attacks and defenses, the rewards and the energy efficiency issues of BFL in order to guide the researchers and the designers of FL based solution to adopt the most appropriate of each application approach.