Jiawen Kang, Zehui Xiong, Chunxiao Jiang, Yi Liu · 9 authors
The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves "data island" problems caused by data privacy concerns. However, large-scale FEL still faces following crucial challenges: (i) there lacks a secure and communication-efficient model training scheme for FEL; (2) there is no scalable and flexible FEL framework for updating local models and global model sharing (trading) management. To bridge the gaps, we first propose a blockchain-empowered secure FEL system with a hierarchical blockchain framework consisting of a main chain and subchains. This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation. A Proof-of-Verifying consensus scheme is then designed to remove low-quality model updates and manage qualified model updates in a decentralized and secure manner, thereby achieving secure FEL. To improve communication efficiency of the blockchain-empowered FEL, a gradient compression scheme is designed to generate sparse but important gradients to reduce communication overhead without compromising accuracy, and also further strengthen privacy preservation of training data. The security analysis and numerical results indicate that the proposed schemes can achieve secure, scalable, and communication-efficient decentralized FEL.
Crowdsourcing is a process wherein an individual or an organisation utilizes the talent pool present over the Internet to accomplish their task. The existing crowdsourcing platforms and their reputation computation are centralised and hence prone to various attacks or malicious manipulation of the data by the central entity. A few distributed crowdsourcing platforms have been proposed but they lack a robust reputation mechanism. So we propose a decentralised crowdsourcing platform having an immutable reputation mechanism to tackle these problems. It is built on top of Ethereum network and does not require the user to trust a third party for a non malicious experience. It also utilizes IOTAs consensus mechanism which reduces the cost for task evaluation significantly.
Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to contribute their efforts, Shapley Value (SV) is often adopted to fairly assess their contribution. However, the calculation of SV is time-consuming and computationally costly. In this paper, we propose FedCoin, a blockchain-based peer-to-peer payment system for FL to enable a feasible SV based profit distribution. In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol. It is in contrast to the popular BitCoin network where consensus entities "mine" new blocks by solving meaningless puzzles. Based on the computed SVs, a scheme for dividing the incentive payoffs among FL clients with nonrepudiation and tamper-resistance properties is proposed. Experimental results based on real-world data show that FedCoin can promote high-quality data from FL clients through accurately computing SVs with an upper bound on the computational resources required for reaching consensus. It opens opportunities for non-data owners to play a role in FL.
Sarah Iqbal, Asad Waqar Malik, Anis Ur Rahman, Rafidah Md Noor
With the widespread adoption of the internet of things (IoT) technologies towards building a smart city, connected devices often offload computation tasks to nearby edge locations (base stations) to reduce overall computation and network delay. However, serving an ever-increasing number of end devices at these traditional edge locations is becoming impossible, subsequently making them fail to deliver the agreed quality of service to all requesting devices. However, the backend cloud data center is available to serve these requests but incurred additional communication delay, thus, unsuitable for delay-sensitive applications. Furthermore, the fact that the underlying network is inherently ad hoc which makes it prone to malicious nodes affecting its overall performance. In this work, we propose a secure fog computing paradigm where roadside units (RSUs) are used to offload tasks to nearby fog vehicles based on repute scores maintained at a distributed blockchain ledger. The experimental results demonstrate a significant performance gain in terms of queuing time, end-to-end delay, and task completion rate when compared to the baseline queuing-based task offloading scheme.
Zongyang Zhang, Weihan Li, Haitao Liu, Jianwei Liu
With the continuous development and popularity of blockchain technology, anonymity of cryptocurrency has attracted wide attention. Zcash is an altcoin of Bitcoin aiming to protect blockchain anonymity. Its anonymity is highly guaranteed by zero-knowledge proofs. However, it is still practicable to decrease Zcash's anonymity. In this paper, we provide a refined empirical analysis of Zcash anonymity. We improve current address clustering methods and increase the clustering rate by 9%. We also analyze the whole process of distributing mining reward and identify 87.5% addresses and 25.7% transactions. Besides, we simplify Zcash transaction network and then pick out nodes (edges) which play important roles in network connectivity. We show that these nodes are mostly mining pools. In particular, users participating in shieldedpool are mostly founders, miners and mining pools, although shieldedpool itself is designed for protecting anonymity of users with high privacy requirements. Our results, to an extent, are opposite to the original intention of Zcash.
Kentaroh Toyoda, Jun Zhao, Allan N. Zhang, P. Takis Mathiopoulos
Federated learning (FL) is a promising decentralized deep learning technique that allows users to collaboratively update models without sharing their own data. However, due to its decentralized nature, no one can monitor workers' behavior, and they may thus deviate protocols (e.g., participating without updating any models). To solve this problem, many researchers have proposed blockchain-enabled FL to reward workers (or users) with cryptocurrencies to encourage workers to follow the protocols. However, there is a lack of theoretical discussions concerning how such rewards impact workers' behavior and how much should be given to workers. In this article, we propose a mechanism-design-oriented FL protocol on a public blockchain network. Mechanism design (MD) is often used to make a rule intended to achieve a specific goal. With MD in mind, we introduce the concept of competition into blockchain-based FL so that only workers who have contributed well can obtain rewards, which naturally prevents workers from deviating from the protocol. We then mathematically answer the following questions with contest theory, a novel field of study in economics: i) What behavior will workers take?; ii) how much effort should workers exert to maximize their profits?; iii) how many workers should be rewarded?; and iv) what is the best proportion for reward distribution?
Electronic medical data have significant advantages over paper-based patient records when it comes to storage and retrieval. However, most existing medical data sharing schemes have security risks, such as being prone to data tampering and forgery, and do not support the ability to verify the authenticity of the data source. To solve these problems, we propose a medical data sharing scheme based on attribute cryptosystem and blockchain technology in this paper. First, the encrypted medical data are stored in the cloud, and the storage address and medical-related information are written into the blockchain, which can ensure efficient storage and eliminate the possibility of irreversible modification of the data. Second, the proposed scheme combines attribute-based encryption (ABE) and attribute-based signature (ABS), which achieves the sharing of medical data in many-to-many communications. The ABE achieves data privacy and fine-grained access control, and the ABS verifies the authenticity of the source of the medical data while protecting the signer's identity. Moreover, the data user outsources most of the operations of medical data ciphertext decryption to the cloud service provider (CSP), which can greatly reduce the computational burden. Finally, results of the analysis show that our scheme satisfies the requirements for confidentiality and unforgeability in the random oracle model, and that the proposed scheme offers higher computational performance than other similar schemes.
Hui Li, Lishuang Pei, Dan Liao, Song Chen · 6 authors
Vehicular Ad Hoc Network (VANET) is an important foundation of intelligent transportation system and is widely used in traffic management, automatic driving, and road optimization. With the gradual popularization and further development of VANET, a large amount of VANET data has been produced. However, it poses huge challenges to the security and privacy when using VANET data provides services for users. In this paper, combining the technologies of blockchain and ciphertext-based attribute encryption (CP-ABE), we propose a fine-grained access control scheme for VANET data based on blockchain (FADB). In FADB, we employ the blockchain to replace the third-party service providers for user identity management and data storage. And different VANET data access rights can be established according to user attribute. By improving the CP-ABE, the lightweight VANET devices can outsource complex encryption and decryption operations to powerful RSUs and further improve the efficiency of data access. Final, we carry out a series of simulation tests and security analysis, proving that the FADB can provide effective data security and low performance overhead.
Multi-party authorization (MPA) typically involves multiple parties to control and grant access to shared data. MPA is used to solve the insider’s attack problem by ensuring that a single authority or party is not acting alone. Currently, almost all existing implementations of MPA are centralized and fall short in providing logs and events related to provenance of granting permissions in a trusted, secure, immutable, auditable, and decentralized manner. Moreover, for sharing data, proxy re-encryption algorithms are often used to give secure access to encrypted shared data. These schemes and algorithms are also centralized and cannot be trusted. In this paper, we propose a fully decentralized blockchain-based solution in which MPA is implemented using Ethereum smart contracts, and proxy re-encryption algorithms (which are computationally expensive) are implemented using multiple oracles to give access to encrypted shared data stored on a public and decentralized storage platform, such as the Interplanetary File Systems (IPFS). The smart contracts help to validate results based on the majority of encrypted results determined by the oracles. For this, we incorporate reputation mechanisms in the proposed smart contracts to rate the oracles based on their malicious and non-malicious behaviors. We present algorithms along with their full implementation, testing, and validation details. We evaluate the proposed system in terms of security, cost, and generalization to show its reliability and practicality. We make the smart contract source code publicly available on Github.
Marc Eisenstadt, Manoharan Ramachandran, Niaz Chowdhury, Allan Third · 5 authors
Goal: As the Coronavirus Pandemic of 2019/2020 unfolds, a COVID-19 ‘Immunity Passport’ has been mooted as a way to enable individuals to return back to work. While the quality of antibody testing, the avail- the ability of vaccines, and the likelihood of even attaining COVID-19 immunity continue to be researched, we address the issues involved in providing tamper-proof and privacy-preserving certification for test results and vaccinations. Methods: We developed a prototype mobile phone app and requisite decentralized server architecture that facilitates instant verification of tamper-proof test results. Personally identifiable information is only stored at the user’s discretion, and the app allows the end-user selectively to present only the specific test result with no other personal information revealed. The architecture, designed for scalability, relies upon (a) the 2019 World Wide Web Consortium standard called ‘Verifiable Credentials’, (b) Tim Berners-Lee’s decentralized personal data platform ‘Solid’, and (c) a Consortium Ethereum-based blockchain. Results: Our mobile phone app and decentralized server architecture enable the mixture of verifiability and privacy in a manner derived from public/private key pairs and digital signatures, generalized to avoid restrictive ownership of sensitive digital keys and/or data. Benchmark performance tests show it to scale linearly in the worst case, as significant processing is done locally on each app. For the test certificate Holder, Issuer (e.g. healthcare staff, pharmacy) and Verifier (e.g. employer), it is ‘just another app’ which takes only minutes to use. Conclusions: The app and decentralized server architecture offer a prototype proof of concept that is readily scalable, applicable generically, and in effect ‘waiting in the wings’ for the biological issues, plus key ethical issues raised in the discussion section, to be resolved.
Accountability is a fundamental after-the-fact approach to detect and punish illegal actions during the execution of a warrant for accessing users’ sensitive data. To achieve accountability in a security protocol, a trusted authority is required, denoted as judge, to faithfully cooperate with the rest of the entities in the system. However, malicious judges or uncooperative protocol participants may void the accountability mechanism in practice, for example by fabricating fake evidence or by refusing to provide any evidence at all. To provide remediation to these issues, in this paper we propose Fialka, a novel accountable decryption system based on privacy-preserving smart contracts (PPSC). The neutrality that is inherent to a secure blockchain platform is inherited by PPSC which are then used in our approach as an accountable key manager as well as a transparent judge. To the best of our knowledge, we present the first PPSC-based accountable decryption system to increase the transparency of warrant execution with formal definitions and proofs. Furthermore, we provide and evaluate a prototype implementation using the PPSC-enabled platform Oasis Devnet, which additionally demonstrates the feasibility of Fialka.
Blockchain-Based Healthcare System (HS-BC) is an electronic medical record (EMR) data-sharing system, which significantly facilitates the storage, access, and sharing of EMR data, and provides patients with timely and effective medical diagnosis and treatment. However, due to the high openness of the blockchain, security and privacy still are major concerns in HS-BC. In this paper, we propose an attribute-based signature scheme with attribute revocation to protect the privacy of the user's identity in HS-BC. Under the premise of using attributes to identify users and protect their identity, the user combines the attribute master-key and the attribute update-key to calculate the attribute signing key, where the attribute master-key is related to the user identity and attribute set, and the attribute update-key is related to the attribute revocation. Through making use of the KUNodes algorithm, attribute revocation can effectively achieve. The designed attribute-based signature scheme requires relatively few pairing operations and does not rely on a central authority. Besides, we formally conducted a security analysis and proved that the proposed scheme is unforgeable, collusion resistant and privacy-preserving. Finally, we evaluate the proposed attribute-based signature scheme in terms of storage and efficiency. Comparison and experimental results show that it is feasible.
Automated digital contact tracing is effective and efficient, and one of the non-pharmaceutical complementary approaches to mitigate and manage epidemics like Coronavirus disease 2019 (COVID-19). Despite the advantages of digital contact tracing, it is not widely used in the western world, including the US and Europe, due to strict privacy regulations and patient rights. We categorized the current approaches for contact tracing, namely: mobile service-provider-application, mobile network operators' call detail, citizen-application, and IoT-based. Current measures for infection control and tracing do not include animals and moving objects like cars despite evidence that these moving objects can be infection carriers. In this article, we designed and presented a novel privacy anonymous IoT model. We presented an RFID proof-of-concept for this model. Our model leverages blockchain's trust-oriented decentralization for on-chain data logging and retrieval. Our model solution will allow moving objects to receive or send notifications when they are close to a flagged, probable, or confirmed diseased case, or flagged place or object. We implemented and presented three prototype blockchain smart contracts for our model. We then simulated contract deployments and execution of functions. We presented the cost differentials. Our simulation results show less than one-second deployment and call time for smart contracts, though, in real life, it can be up to 25 seconds on Ethereum public blockchain. Our simulation results also show that it costs an average of $1.95 to deploy our prototype smart contracts, and an average of $0.34 to call our functions. Our model will make it easy to identify clusters of infection contacts and help deliver a notification for mass isolation while preserving individual privacy. Furthermore, it can be used to understand better human connectivity, model similar other infection spread network, and develop public policies to control the spread of COVID-19 while preparing for future epidemics.
Fog computing (FC) is the extension of Cloud Computing (CC), from the core of the internet architecture to the edge of the network, with the aim to perform processes closer to end-users. This extension is proven to enhance security, and to reduce latency and energy consumption. Blockchain (BC), on the other hand, is the base technology behind crypto-currencies, yet is implemented in wide range of different applications. The security and reliability, along with the distributed trust management criteria proposed in BC, excited the research community to integrate it with FC, in a step towards reaching a distributed and trusted, Data, Payment, Reputation, and Identity management systems. In this survey we present the up-to-date state-of-the-art of FC-BC integration with a detailed literature review and classification. We discuss and categorize the related papers according to the year of publication, domain, used algorithms, BC roles, and the placement of the BC in the FC architecture. Our research presents detailed observations, analysis, and open challenges for the BC-FC integration. We believe such conclusions may clarify the vision of the BC-FC integration, and calibrate the compass towards open issues and future research directions.
Due to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the longitudinal impact force to trains and causes the train decoupling, severely affecting the safe operations of trains. It is quite desirable to replace the manual control with intelligent control in heavy haul rail systems. Traditional machine learning-based intelligent control methods suffer from insufficient data. Due to lacking effective incentives and trust, data from different rail lines or operators cannot be shared directly. In this paper, we propose an approach on blockchain-based federated learning to implement asynchronous collaborative machine learning between distributed agents that own data. This method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federated learning. Using the historical driving data collected from real heavy haul rail systems, the learning agent in the federated learning method adopts a support vector machine (SVM) based intelligent control model. To deal with the imbalanced traction and braking data, we optimize the classic SVM model via assigning different penalty factors to the majority and minority classes. The data set are mapped to a high dimension using kernel functions to make it linearly separable. We construct a mixing kernel function composed of polynomial and radial basis function (RBF) kernel functions, which uses a dynamic weight factor changing with train speeds to improve the model accuracy. The simulation results demonstrate the efficiency and accuracy of our proposed intelligent control method.
In a recent seminal work, Bitansky and Shmueli (STOC '20) gave the first construction of a constant round zero-knowledge argument for NP secure against quantum attacks. However, their construction has several drawbacks compared to the classical counterparts. Specifically, their construction only achieves computational soundness, requires strong assumptions of quantum hardness of learning with errors (QLWE assumption) and the existence of quantum fully homomorphic encryption (QFHE), and relies on non-black-box simulation. In this paper, we resolve these issues at the cost of weakening the notion of zero-knowledge to what is called $\epsilon$-zero-knowledge. Concretely, we construct the following protocols:
- We construct a constant round interactive proof for NP that satisfies statistical soundness and black-box $\epsilon$-zero-knowledge against quantum attacks assuming the existence of collapsing hash functions, which is a quantum counterpart of collision-resistant hash functions. Interestingly, this construction is just an adapted version of the classical protocol by Goldreich and Kahan (JoC '96) though the proof of $\epsilon$-zero-knowledge property against quantum adversaries requires novel ideas.
- We construct a constant round interactive argument for NP that satisfies computational soundness and black-box $\epsilon$-zero-knowledge against quantum attacks only assuming the existence of post-quantum one-way functions.
At the heart of our results is a new quantum rewinding technique that enables a simulator to extract a committed message of a malicious verifier while simulating verifier's internal state in an appropriate sense.
We put forth a new framework for building pairing-based non-interactive zero-knowledge (\(\mathsf {NIZK}\)) arguments for a wide class of algebraic languages, which are an extension of linear languages, containing disjunctions of linear languages and more. Our approach differs from the Groth-Sahai methodology, in that we rely on pairings to compile a \(\varSigma \)-protocol into a \(\mathsf {NIZK}\). Our framework enjoys a number of interesting features:
conceptual simplicity, parameters derive from the \(\varSigma \)-protocol;
proofs as short as resulting from the Fiat-Shamir heuristic applied to the underlying \(\varSigma \)-protocol;
fully adaptive soundness and perfect zero-knowledge in the common random string model with a single random group element as CRS;
yields simple and efficient two-round, public coin, publicly-verifiable perfect witness-indistinguishable (WI) arguments(ZAPs) in the plain model. To our knowledge, this is the first construction of two-rounds statistical witness-indistinguishable arguments from pairing assumptions.
Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health. However, big data faces many ongoing serious challenges in the process of collection, storage, and use. Among them, data security and privacy problems have attracted extensive interest. In an effort to overcome this challenge, this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions (SGX). First of all, we define SGX as a trusted edge computing node, design data access module, data protection module, and data integrity check module, to achieve hardware-enhanced data privacy protection. Then, we design a smart contract framework to realize distributed data access control management in a big data environment. The crucial role of the smart contract was revealed by designing multiple access control contracts, register contracts, and history contracts. Access control contracts provide access control methods for different users and enable static access verification and dynamic access verification by checking the user’s properties and history behavior. Register contract contains user property information, edge computing node information, the access control and history smart contract information, and provides functions such as registration, update, and deletion. History contract records the historical behavior information of malicious users, receives the report information of malicious requestors from the access control contract, implements a misbehavior check method to determines whether the requestor has misbehavior, and returns the corresponding result. Finally, we design decentralized system architecture, prove the security properties, and analysis to verify the feasibility of the system. Results demonstrate that our method can effectively improve the timeliness of data, reduce network latency, and ensure the security, reliability, and traceability of data.
Die Möglichkeit, Computerberechnungen mit einem Beweis für deren korrekte Ausführung zu hinterlegen, scheint in Zeiten der oft genannten ‘Digitalisierung’ ein vielversprechendes Mittel zu sein, um es Parteien, die einander nicht trauen, zu ermöglichen sich auszutauschen. Diese Korrektheitsbeweise sind gerade im Themengebiet der Blockchain interessante und vielversprechende Kandidaten, um dortige Probleme zu adressieren. In dieser Arbeit untersuchen wir formal, was eine Blockchain im Sinne Nakamotos auszeichnet, und inwiefern sogenannte ‘Privacy Enhancing Technolgies’ in diesen dezentralen Systemen Anwendung finden. Das Hauptaugenmerk legen wir dabei auf ‘pairing-based preprocessed zero knowledge succinct non interactive arguments of knowledge’ (zkSNARKs). Wir untersuchen deren mathematische Grundlage und entwickeln unsere eigene zkSNARK Konstruktion. Diese basiert auf einer Erweiterung der gängigen F-arithmetischen Schaltung, die neben arithmetischen Operationen auch die der skalare Gruppenexponentiation auf elliptischen Kurven unterstützt. Die Implementierung ist in der Sprache Golang verfasst und unter einer GPL3.0 Lizenz auf Github zugänglich gemacht. Das Programm übersetzt eine eigens dafür entwickelte Programmiersprache in die Form eines ‘QuadraticArithmetic Programs’ und kann daher über unsere Arbeit hinaus in Beweissystemen basierend auf F-arithmetischen Schaltungen eingesetzt werden.
Bao Nguyen, E. Laxmi Lydia, Mohamed Elhoseny, Irina V. Pustokhina · 8 authors
In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data, collected from various data providers. Then, ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process. In this study, the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers, where IoT data is encrypted and recorded in a distributed ledger. The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts. To examine the performance of the proposed method, it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD) from UCI AI repository. The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.
Open access
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
With the rise in popularity of cryptocurrencies, distributed ledger technology is a term that has gained traction. The aim of this study is to review and comparethe distributed ledger technologies blockchain and directed acyclic graph, examining their internal structures as well as some platforms and existing areas of application. An implementation, the goal of which is to illustrate the components of a possible distributed ledger solution and how they might interact, has been made in the form of a smart contract deployed on a simulated distributed ledger network. To give some explanation to the foundations of distributed ledger technology, a brief overview is given on the topics of cryptography, underlying data structures, and the frameworks used in this study. The literature study has been conducted by collecting and reviewing primarily scientific articles on the topic of distributed ledger technologies and consensus algorithms, as well as white papers on selected distributed ledger platforms. The construction has been done using the framework Hyperledger Fabric. The result chapter reviews how the implemented smart contract fulfills the concrete goals. The study is concluded with a discussion regarding how distributed ledgers might possibly be used in thef uture, what might be done to further develop the implemented smart contract and some of the ethical concerns surrounding distributed ledger technology.
As a kind of point-to-point distributed public ledger technology, blockchain has been widely concerned in recent years. The privacy protection of blockchain technology has always been the core issue of people's attention. In this paper, some existing solutions to the current problems of user identity and transaction privacy protection are surveyed, including coin mixing mechanism, zero knowledge proof, ring signature and other technologies. Secondly, five typical applications of privacy protection technology based on blockchain are proposed and analyzed, which are mainly divided into technology applications based on coin mixing protocol, encryption protocol, secure channel protocol and so on. Finally, in view of the shortages of the existing blockchain privacy protection technology, we explore future research challenges that need to be studied in order to preserve privacy in blockchain system, and looks forward to the future development direction.