Stateless Ethereum is proposed to address the challenges of unbounded state growth, which poses centralization risks to Ethereum by imposing considerable operational costs on network participants. This approach enables Ethereum clients to verify and maintain blockchain using only block witnesses, eliminating the need for the underlying state data. Verkle Tries, as the next-generation state trie structure for Ethereum, are set to replace Merkle Patricia Tries to facilitate the stateless Ethereum by significantly reducing the size of block witnesses. Besides, since blockchain maintenance and verification entail intensive read and update operations on state data, ensuring the correctness of state access in Verkle Tries is crucial.In this study, we conduct the first systematic study on the correctness issues in Ethereum Verkle Tries, and define two kinds of such issues. Besides, we design VERDIFF, the first tool for automatically identifying correctness issues in Ethereum Verkle Tries through preimage-aware differential testing. To facilitate the identification of interpretable and reproducible issues, we proposePAL, the first domain-specific language designed to construct semantically valid trie access statements as test inputs for Verkle Tries. During the preimage-aware differential testing, these statements are iteratively generated and mutated based on our novel feedback mechanisms for inducing diverse behaviors when accessing states across varied regions in Verkle Tries at the preimage level. Moreover, we define new correctness issue oracles based on our definitions of these issues to ensure their accurate identification. We implement VERDIFF on Ethereum clients, and conduct extensive experiments to evaluate its effectiveness in identifying correctness issues. Through our evaluation, VERDIFF can uncover eight critical correctness issues, spanning four distinct classes, in Ethereum Verkle Tries across three major Ethereum clients (Go-ethereum, Nethermind, and EthereumJS). Additionally, it achieves at least 23.3% higher code path coverage compared to two baselines. Furthermore, we empirically analyze the security risks posed by the identified correctness issues, and explore their potential security implications, highlighting the critical role of VERDIFF in securing Ethereum Verkle Tries.
R Mahaveerakannan, Balamanigandan Ramachandran, S. Saraswathi, Dinesh Goyal · 5 authors
This program integrates the use of UAVs and terrestrial nodes to develop a safe and effective communication system which can be utilized for post-disaster management. Furthermore, blockchain technology will also assist in collaboration and security. Communication systems are frequently demolished by natural disasters making it more difficult to provide aid. While UAVs and wireless mesh networks (WMNs) offer considerable support towards resolving them, the greater challenge of ensuring speedy, secure, and discreet communication among multiple organizations still exists. The proposed approach provides supporting ad hoc networks to accommodate large-scale disaster response projects under the name ROSNet. This network will have a place for blockchain technology, terrestrial nodes and UAVs. Alongside a consortium blockchain architecture, smart contracts and cryptographic techniques enable the system to be tailored toward safe collaboration between multiple jurisdictions making it possible Interchangeable communication and wireless mesh networks in control of a Robot Operating System (ROS). At the base station, user interfaces for situational awareness and node optimization are provided alongside a graphical user interface (GUI) while delegated proof-of-stake increases pragmatic Byzantine fault tolerance ratios (DPoS-PBFT) consensus protocol for scalability and network fault tolerance. The use of semi-autonomous ground nodes and UAVs helps increase the connectivity of the system and automates the response operations.
Yanming Zhu, Xuefei Yin, Alan Wee‐Chung Liew, Hui Tian
With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis.
Federated learning (FL) enables distributed clients to privately train machine learning models using their own local data, thus avoiding the security risk of directly exchanging private data between clients and servers. However, there is a risk in federated learning: a malicious party may launch a reverse attack based on the parameters uploaded by the client to analyze the attributes of the client’s local private data. Some studies use the Shamir secret sharing method and zero-knowledge proofs (ZKP) to ensure the privacy and integrity of the inputs in FL. However, the Shamir secret sharing scheme often requires an optimistic guarantee on the number of malicious clients, which cannot obtain a sufficient number of secret slices through collusion, and the general ZKP scheme requires the clients to compute the proof belongs to others during the proof and verification stages, and its efficiency needs to be improved. In this paper, we propose VSSPFL method to achieve efficient and secure data collaboration, which improves the Shamir secret sharing scheme to prevent reverse attacks by an unknown number of malicious clients and reduces the cost of ZKP by using a probabilistic integrity check method.
Advanced mathematical and deep learning (DL) algorithms have recently played a crucial role in diagnosing medical parameters and diseases. One of these areas that need to be more focused on is dentistry. This is why creating digital twins of dental issues in the metaverse is a practical and effective technique to benefit from the immersive characteristics of this technology and adapt the real world of dentistry to the virtual world. These technologies can create virtual facilities and environments for patients, physicians, and researchers to access a variety of medical services. Experiencing an immersive interaction between doctors and patients can be another considerable advantage of these technologies, which can dramatically improve the efficiency of the healthcare system. In addition, offering these amenities through a blockchain system enhances reliability, safety, openness, and the ability to trace data exchange. It also brings about cost savings through improved efficiencies. In this paper, a digital twin of cervical vertebral maturation (CVM), which is a critical factor in a wide range of dental surgery, within a blockchain-based metaverse platform is designed and implemented. A DL method has been used to create an automated diagnosis process for the upcoming CVM images in the proposed platform. This method includes MobileNetV2, a mobile architecture that improves the performance of mobile models in multiple tasks and benchmarks. The proposed technique of digital twinning is simple, fast, and suitable for physicians and medical specialists, as well as for adapting to the Internet of Medical Things (IoMT) due to its low latency and computing costs. One of the important contributions of the current study is to use of DL-based computer vision as a real-time measurement method so that the proposed digital twin does not require additional sensors. Furthermore, a comprehensive conceptual framework for creating digital twins of CVM based on MobileNetV2 within a blockchain ecosystem has been designed and implemented, showing the applicability and suitability of the introduced approach. The high performance of the proposed model on a collected small dataset demonstrates that low-cost deep learning can be used for diagnosis, anomaly detection, better design, and many more applications of the upcoming digital representations. In addition, this study shows how digital twins can be performed and developed for dental issues with the lowest hardware infrastructures, reducing the costs of diagnosis and treatment for patients.
Open access
Medical Imaging and Analysis
Dental Radiography and Imaging
Artificial Intelligence in Healthcare and Education
Sai Batchu, Michael J. Diaz, Lauren Ladehoff, Kevin T. Root · 5 authors
Aim: Conventional techniques to share and archive spinal imaging data raise issues with trust and security, with novel approaches being more greatly considered. Ethereum smart contracts present one such novel approach. Ethereum is an open-source platform that allows for the use of smart contracts. Smart contracts are packages of code that are self-executing and reside in the Ethereum state, defining conditions for programmed transactions. Though powerful, limited attempts have been made to showcase the clinical utility of such technologies, especially in the pre- and post-operative imaging arenas. Herein, we therefore aim to propose a proof-of-concept smart contract that stores intraoperative three-dimensional (3D) augmented reality surgical navigation (ARSN) data and was tested on a private, proof-of-authority network. To the author's best knowledge, the present study represents a first-use case of the Interplanetary File Storage protocol for storing and retrieving spine imaging smart contracts. Methods: The content identifier hashes were stored inside the smart contracts while the interplanetary file system (IPFS) was used to efficiently store the image files. Insertion was achieved with four storage mappings, one for each of the following: fictitious patient data, specific diagnosis, patient identity document (ID), and Gertzbein grade. Inserted patient observations were then queried with wildcards. Insertion and retrieval times for different record volumes were collected. Results: It took 276 milliseconds to insert 50 records and 713 milliseconds to insert 350 records. Inserting 50 records required 934 Megabyte (MB) of memory per insertion with patient data and imaging, while inserting 350 records required almost the same amount of memory per insertion. In a database of 350 records, the retrieval function needs about 1,026 MB to query a record with all three fields left blank, but only 970 MB to obtain the same observation from a database of 50 records. Conclusions: The concept presented in this study exemplifies the clinical utility of smart contracts and off-chain data storage for efficient retrieval/insertion of ARSN data.
In order to solve the problem of adding artificial intelligence and blockchain technology to education, the purpose of meeting the needs of combining artificial intelligence and blockchain technology with modern education is to make up for the lack of artificial intelligence in modern education, improve students’ interest in learning, and cultivate high-quality students. Through practice and analysis in a chemistry class in an experimental school, samples were taken from 821 students in the third grade parallel class, and 39 teachers taught students in accordance with their aptitude; finally, from the average score statistics of the first and second inspections, it can be seen that the intelligent classroom teaching of chemistry has a certain effect on improving the average score of students in parallel classes in grade three.