With the growing need for privacy and self-sovereign identity, traditional identity management relying on centralized data registries not only represents single points of failure but also lacks transparency and control over users' identity information. With the built-in tamper-proofness and transparency, blockchain has been widely studied to accommodate the challenges in traditional identity management. Still, it usually comes with privacy concerns due to its public accessibility. Anonymous credentials take advantage of the recent progress in zero-knowledge proof, allowing the unlinkable presentation of only the necessary attributes for a service to guarantee anonymity. However, the existing anonymous credentials require a secondary issuer to verify and manage the anonymized credentials, which compromises the overall transparency and causes indirect management of the user's identity. In this paper, we propose GrAC, a blockchain-based identity management system based on a novel identity graph, which allows users and identity providers to securely store and manage identity information on the blockchain without intermediate entities. GrAC also includes an anonymous authentication protocol suite based on zero-knowledge proof, allowing users to generate one-time anonymous credentials that selectively reveal minimal information to the service provider for authentication. The analysis and evaluations show that Gr Achas a reasonable overhead and provides adequate anonymity protection while removing the need for intermediate issuers.
Prefix summation has found its applications over the years in various important domains from sorting to geographical terrain analysis. In our work, we address the privacy-preserving prefix summation problem on blockchain where multiple parties aim to aggregate their secret values through a secure multi-party computation. For the given problem, we propose a novel crypto-graphic protocol (i.e. PRFX) by integrating zero-knowledge proof and hypercube network topology. In addition, we theoretically show the applicability of the proposed protocol on the privacy-preserving delegation using Euler Tour Technique. We analyze the protocol with respect to the scalability perspectives including the communication, computation and storage overheads. The proposed protocol is also exposed to the experimental study where its performance is measured through the blockchain gas cost, the zero-knowledge proof generation time and lastly the zero-knowledge proof size.
Elmira Ebrahimi, Michael Sober, Anh-Tu Hoang, Can Umut Ileri · 6 authors
Federated learning is a distributed learning technique that enables parties to train a model collaboratively without disclosing their local data. To this end, a centralized aggregator collects local models from participating parties and aggregates them to form a global model. However, if parties are malicious, this approach is susceptible to security threats such as model poisoning and global aggregation attacks. Blockchain-based federated learning has been introduced as an alternative to the centralized aggregator to aggregate local models directly on the blockchain. However, employing blockchain-based solutions introduces challenges, including high computational costs and privacy concerns. To mitigate such challenges, this paper leverages zero-knowledge proofs (i.e., zk-SNARKs) to guarantee the privacy and verifiability of local model training and global model aggregation. The proposed framework verifies the local and global models' computational correctness without storing and revealing them on the blockchain. We evaluate our framework by utilizing a real-life dataset, with experimental results demonstrating its viability regarding both computational costs and learning model performance.
Hangchao Ding, Huayun Tang, Jia Chen, Yanzhao Wang
As a privacy-preserving technique, Secure Multi-Party Computation (SMPC) has been extensively applied in Neural Networks (NN) privacy-preserving schemes, including secret sharing, homomorphic encryption, and zero-knowledge proof. Federated Learning (FL) is applied to train models from scattered data, which can protect the client's data. Therefore, the leakage of gradient in Federated Learning has been a risk of privacy and security. We construct a post-quantum privacy-preserving cryptography algorithm, in which gradient can be protected by SMPC techniques. Non-interactive zero-knowledge proof, Shamir secret sharing, and homomorphic encryption techniques are applied to guarantee the security of gradient transmission. L WE-based Kyber key exchange protocol is also applied to be resistant to quantum attack.
In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and l-diversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%.
Mary Jane C. Samonte, Raphael M. Abaleta, Maverick D. Cayabyab, L.G. Guerrero
Domain Name Systems (DNS) are crucial components of any modern network, as they allow users to type in a domain name as opposed to complete IP addresses to visit web pages on the internet. The DNS resolution process typically involves a recursive server that fetches addresses from the root, top-level domain, and authoritative nameservers and sends the address to the client. Since this process can be slow, a caching system is implemented to save and access previous queries. However, this approach can result in security and privacy vulnerabilities. Blockchain is an emerging distributed ledger technology whose decentralized nature and data validation mechanisms help ensure data integrity, reliability, and authenticity. This study investigates the feasibility of integrating blockchain into DNS. It examines and reviews how DNS security is typically implemented, how blockchain can be implemented in other systems, like Internet of Things (IoT) systems, as a security measure, and how other technologies, like honeypots and artificial intelligence (AI), can be implemented to enhance system security in different systems, and potentially in DNS. The discussion results highlight blockchain's effectiveness in securing and improving the privacy of DNSs and their users. The study recommends that more studies be conducted on integrating blockchain in existing system architectures, like DNS, and on using other technologies, such as honeypots and AI, in conjunction with blockchain to improve security in computer networks and systems further.
Various deep learning techniques, including blockchain-based approaches, have been explored to unlock the potential of edge data processing and resultant intelligence. However, existing studies often overlook the resource requirements of blockchain consensus processing in typical Internet of Things (IoT) edge network settings. This paper presents our FLCoin approach. Specifically, we propose a novel committee-based method for consensus processing in which committee members are elected via the FL process. Additionally, we employed a two-layer blockchain architecture for federated learning (FL) processing to facilitate the seamless integration of blockchain and FL techniques. Our analysis reveals that the communication overhead remains stable as the network size increases, ensuring the scalability of our blockchain-based FL system. To assess the performance of the proposed method, experiments were conducted using the MNIST dataset to train a standard five-layer CNN model. Our evaluation demonstrated the efficiency of FLCoin. With an increasing number of nodes participating in the model training, the consensus latency remained below 3 s, resulting in a low total training time. Notably, compared with a blockchain-based FL system utilizing PBFT as the consensus protocol, our approach achieved a 90% improvement in communication overhead and a 35% reduction in training time cost. Our approach ensures an efficient and scalable solution, enabling the integration of blockchain and FL into IoT edge networks. The proposed architecture provides a solid foundation for building intelligent IoT services.
Blockchain is an emerging technology for data security, integrity and secure communication in the networks of autonomous vehicles (AVs), this technology is extremely utilized for the AV s applications to record the access control data. The aim of this work is to share the knowledge on the growing demand of blockchain technology for the autonomous vehicles, discuss the consequences of integration of blockchain technology in AVs networks and access control systems for autonomous vehicles. This work offers a comprehensive investigation of existing applications for AVs based on blockchain technology with respect to various aspects including secure communication channels, forensics applications, secure data storage, reputation and trust management, data integrity and privacy. This work also offers an extensive study on various types of consensus algorithms used in Ethereum/Hyperledger, and provides a comprehensive comparison of trust and risk based on blockchain consensus algorithms on Ethereum over Hyperledger for autonomous vehicle applications.
Rongping Lin, Fan Wang, Shan Luo, Xiong Wang · 5 authors
Federated Learning (FL) is a distributed machine learning method that ensures the privacy and security of participants’ data by avoiding direct data upload to a central node for training. However, the traditional FL typically applies a star structure with cloud servers as the central aggregator for the model parameters from different terminals, leading to problems such as central failure, malicious tampering and malicious participants, resulting in training errors or system crashes. To address these issues, a permissioned blockchain is used to build a secure and reliable data-sharing platform among participating terminals, replacing the central aggregator in the traditional FL called blockchain-based federated learning. However, the block generation method of the blockchain system may introduce significant latency in the federated learning where distributed model parameters upload randomly, resulting in low efficiency of the federated learning. To overcome this, we propose a block generation strategy that groups terminals and generates a block for each group, which minimizes the latency of a single round of federated learning, and an optimal block generation algorithm that considers data distribution, terminal resources, and network resources is provided. The analysis shows that the proposed algorithm can effectively obtain the optimal solution of block generation to minimize the authentication time, and we conduct extensive experiments that demonstrate the time efficiency of the proposed algorithm.
In sectors such as finance and healthcare, where data governance is subject to rigorous regulatory requirements, the exchange and utilization of data are particularly challenging. Federated Learning (FL) has risen as a pioneering distributed machine learning paradigm that enables collaborative model training across multiple institutions while maintaining data decentralization. Despite its advantages, FL is vulnerable to adversarial threats, particularly poisoning attacks during model aggregation, a process typically managed by a central server. However, in these systems, neural network models still possess the capacity to inadvertently memorize and potentially expose individual training instances. This presents a significant privacy risk, as attackers could reconstruct private data by leveraging the information contained in the model itself. Existing solutions fall short of providing a viable, privacy-preserving BRFL system that is both completely secure against information leakage and computationally efficient. To address these concerns, we propose Lancelot, an innovative and computationally efficient BRFL framework that employs fully homomorphic encryption (FHE) to safeguard against malicious client activities while preserving data privacy. Our extensive testing, which includes medical imaging diagnostics and widely-used public image datasets, demonstrates that Lancelot significantly outperforms existing methods, offering more than a twenty-fold increase in processing speed, all while maintaining data privacy.
In recent years, blockchain technology has evolved significantly, enabling a decentralized network application model that offers both user anonymity and transparency. This unique characteristic of blockchain has led to its adoption in various sectors, including healthcare, finance, and transportation. The advancement of modern zero-knowledge proof technology has further enhanced blockchain's applications across these fields, bolstering privacy protection. Zero-knowledge proofs have become a key mechanism in blockchain smart contracts, offering a balance between transparency and privacy. Moreover, the integration of zero-knowledge proof technology with blockchain is facilitating technical advancements in areas facing challenges, such as autonomous driving technology. It is also addressing security concerns in more established technologies like the Internet of Things. This synergy between zero-knowledge proof and blockchain technologies is paving the way for innovative solutions across a wide range of applications.
Rafael Marin Machado de Souza, A. I. S. Holm, Márcio Biczyk, Leandro Nunes de Castro
Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC.
Millions of smart contracts are deployed on various blockchain platforms, involving extensive digital assets. However, vulnerabilities within these smart contracts have resulted in substantial exploitation and asset losses. Traditional methods for detecting smart contract vulnerabilities are limited by their narrow detection range and enormous computational cost. This paper investigates how large language models (LLMs), particularly ChatGPT 4, can be leveraged to detect vulnerabilities in smart contracts. We conduct a comprehensive survey of several existing detection methods for smart contract vulnerabilities. Meanwhile, we design a variety of prompt information, and added contract opcodes and expert rules as auxiliary information. Utilizing ChatGPT, we evaluate the effectiveness of the large language model in identifying vulnerabilities across two datasets. The experimental results demonstrate that ChatGPT, informed by specific prompts, can effectively pinpoint vulnerabilities, highlighting the utility of LLMs in enhancing the security of smart contracts.
Anh Pham, Maxim Edelson, Armin Nouri, Tsung-Ting Kuo
BACKGROUND: The consent protocol is now a critical part in the overall orchestration of clinical research. We aimed to demonstrate the feasibility of an Ethereum-based informed consent system, which includes an immutable and automated channel of consent matching, to simultaneously assure patient privacy and increase the efficiency of researchers' data access. METHOD: We simulated a multi-site scenario, each assigned 10000 consent records. A consent record contained one patient's data-sharing preference with regards to seven data categories. We developed a blockchain-based infrastructure with a smart contract to record consents on-chain, and to query consenting patients corresponding to specific criteria. We measured our system's recording efficiency against a baseline design and verified accuracy by testing an exhaustive list of possible queries. RESULTS: Our method achieved ∼3-4% lead with an average insertion speed of ∼2 s per record per node on either a 3-, 4- or 5-node network, and 100 % accuracy. It also outperformed other solutions in external validation. DISCUSSION: The speed we achieved is reasonable in a real-world system under the realistic assumption that patients may not change their minds too frequently, with the added benefit of immutability. Furthermore, the per-insertion time did improve slightly as the number of network nodes increased, attesting to the benefit of node parallelism as it suggests no attrition of insertion efficiency due to scale of nodes. CONCLUSIONS: Our work confirms the technical feasibility of a blockchain-based consent mechanism, assuring patients with an immutable audit trail, and providing researchers with an efficient way to reach their cohorts.
ABSTRACT The healthcare industry has undergone a digital transformation in recent years, with the adoption of electronic health records (EHRs) becoming increasingly prevalent. While this digitization offers various advantages, concerns regarding the security and privacy of sensitive medical data have also intensified. Data breaches and cyber‐attacks targeting healthcare organizations have underscored the need for robust solutions to protect patient data. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature, which ensures secure and transparent data recording. This paper proposes a novel approach that combines blockchain with advanced encryption scheme and privacy protection technique to establish a secure and privacy protected medical data sharing environment. The proposed system consists of three phases such as initialization phase, data processing phase, and authentication phase. The hybrid Feistal‐Shannon homomorphic encryption algorithm (HFSHE) is proposed to encrypt the medical data to ensure data confidentiality, integrity, and availability. Ring signature is integrated to the system to provide additional anonymity and protect the identities of the participants involved in data transactions. In addition, the smart contract developed performs authentication checks on users, generates a time seal, and verifies the ring signature. Through this enhancement, the system becomes more resilient to both external and internal threats, enhancing overall security as well as privacy. A comprehensive security analysis is conducted to compare the proposed method's performance against existing techniques. The results demonstrate the effectiveness of the proposed approach in safeguarding sensitive medical information within the blockchain ecosystem.
Data sharing serves to maximize the efficiency of data resources by facilitating their full utilization and reducing associated costs. However, existing data-sharing schemes are confronted with issues such as data loss, data tampering, difficulties in privacy protection, and high sharing costs. To address these issues, this paper proposes a blockchain-based security-oriented data-sharing scheme. Firstly, an architecture that separates data from data ownership is employed to enhance the security of the scheme and reduce storage overhead. Secondly, a lightweight on-chain and off-chain collaborative data security algorithm based on ECC and ECDHE is designed to ensure confidentiality during data sharing. Finally, a mechanism for tracking the circulation of shared data is proposed, which records the data flow in non-fungible tokens (NFTs), thereby improving the traceability of the proposed scheme. We designed relevant experiments to evaluate the proposed solution, and the results demonstrate that the data-sharing scheme devised in this paper performs well in terms of both security and usability, effectively achieving secure data sharing.
Mischelle A. Esguerra, Keno Piad, Isagani M. Tano, Jayson M. Victoriano · 6 authors
The Technical Education and Skills Development Authority (TESDA) in the Philippines issues National Certificates (NCs) which is an important credential for graduates and skilled workers, affirming their capabilities in line with defined competency standards. However, with the advancement in information technology and the availability of affordable editing tools in the market raised concerns about the creation of counterfeit documents including NCs. The study focused on creating a smart credentialing and verification system for issuing National Certificates using blockchain technology. Researchers used Polygon blockchain that implements Proof-of-Stake consensus algorithm for system's efficiency and security. Certificates generated by the system are stored on the blockchain, with each certificate assigned a unique address for verification purposes. The system was assessed using ISO/IEC 25010 standards, and respondents provided good feedback on a variety of parameters. Future development recommendations include integrating a mobile application for easier certificate access and verification, providing real-time updates, establishing a feedback mechanism, and implementing analytics to gain insights into certificate issuance and user engagement.
Mochan Fan, Zhipeng Zhang, Zonghang Li, Gang Sun · 7 authors
Vertical Federated Learning (VFL), which draws attention because of its ability to evaluate individuals based on features spread across multiple institutions, encounters numerous privacy and security threats. Existing solutions often suffer from centralized architectures, and exorbitant costs. To mitigate these issues, in this paper, we propose SecureVFL, a decentralized multi-party VFL scheme designed to enhance efficiency and trustworthiness while guaranteeing privacy. SecureVFL uses a permissioned blockchain and introduces a novel consensus algorithm, Proof of Feature Sharing (PoFS), to facilitate decentralized, trustworthy, and high-throughput federated training. SecureVFL introduces a verifiable and lightweight three-party Replicated Secret Sharing (RSS) protocol for feature intersection summation among overlapping users. Furthermore, we propose a (42)-sharing protocol to achieve federated training in a four-party VFL setting. This protocol involves only addition operations and exhibits robustness. SecureVFL not only enables anonymous interactions among participants but also safeguards their real identities, and provides mechanisms to unmask these identities when malicious activities are performed. We illustrate the proposed mechanism through a case study on VFL across four banks. Finally, our theoretical analysis proves the security of SecureVFL. Experiments demonstrated that SecureVFL outperformed existing multi-party VFL privacy-preserving schemes, such as MP-FedXGB, in terms of both overhead and model performance.
This study proposes a blockchain-based system that utilizes fully homomorphic encryption to provide data security and statistical privacy when data are shared with third parties for analysis or research purposes. The proposed system not only provides security of data in transit, at rest, and in use but also assures privacy and computational integrity for simple statistical computations. This is achieved by leveraging the attributes of the blockchain technology, which provides availability and data integrity, combined with homomorphic encryption, which provides confidentiality of data in use. The computations are performed on smart contracts residing on the blockchain, providing computational integrity. The proposed system is implemented on the Zama blockchain and performs statistical operations including mean, median, and variance on encrypted data. The results indicate that it is possible to perform fully homomorphic computations on the blockchain. Even though current computing limitations on the blockchain do not allow running the system for large data sets, the technology is available, and with advancements toward more efficient homomorphic operations on blockchains, the proposed system will provide an ultimate solution for providing the much-desired security properties in applications, including data and statistical privacy, confidentiality, and integrity at rest, in transit, and in use.
Federated Learning (FL) addresses the challenges posed by data silos, which arise from privacy, security regulations, and ownership concerns. Despite these barriers, FL enables these isolated data repositories to participate in collaborative learning without compromising privacy or security. Concurrently, the advancement of blockchain technology and decentralized applications (DApps) within Web 3.0 heralds a new era of transformative possibilities in web development. As such, incorporating FL into Web 3.0 paves the path for overcoming the limitations of data silos through collaborative learning. However, given the transaction speed constraints of core blockchains such as Ethereum (ETH) and the latency in smart contracts, employing one-shot FL, which minimizes client-server interactions in traditional FL to a single exchange, is considered more apt for Web 3.0 environments. This paper presents a practical one-shot FL system for Web 3.0, termed OFL-W3. OFL-W3 capitalizes on blockchain technology by utilizing smart contracts for managing transactions. Meanwhile, OFL-W3 utilizes the Inter-Planetary File System (IPFS) coupled with Flask communication, to facilitate backend server operations to use existing one-shot FL algorithms. With the integration of the incentive mechanism, OFL-W3 showcases an effective implementation of one-shot FL on Web 3.0, offering valuable insights and future directions for AI combined with Web 3.0 studies.