The on-demand mobility market, including ridesharing, is becoming increasingly important with e-hailing fares growing at a rate of approximately 130% per annum since 2013. By increasing utilization of existing vehicles and empty seats, ridesharing can provide many benefits including reduced traffic congestion and environmental impact from vehicle usage and production. However, the safety of riders and drivers has become of paramount concern and a method for privacy-preserving identity verification between untrusted parties is essential for protecting users. To this end, we propose a novel privacy-preserving identity verification system, extending zero-knowledge proof (ZKP) and blockchain for use in ridesharing applications. We design a permissioned blockchain network to perform the ZKP verification of a driver's identity, which also acts as an immutable ledger to store ride logs and ZKP records. For the ZKP module, we design a protocol to facilitate user verification without requiring the exchange of any private information. We prototype the proposed system on the Hyperledger Fabric platform, with the Hyperledger Ursa cryptography library, and conduct extensive experimentation. To measure the prototype's performance, we utilize the Hyperledger Caliper benchmark tool to perform extensive analysis and the results show that our system is suitable for use in real-world ridesharing applications.
Dick Carrillo, Lam Duc Nguyen, Pedro H. J. Nardelli, Evangelos Pournaras · 14 authors
In this paper, we propose a global digital platform to avoid and combat epidemics by providing relevant real-time information to support selective lockdowns. It leverages the pervasiveness of wireless connectivity while being trustworthy and secure. The proposed system is conceptualized to be decentralized yet federated, based on ubiquitous public systems and active citizen participation. Its foundations lie on the principle of informational self-determination. We argue that only in this way it can become a trustworthy and legitimate public good infrastructure for citizens by balancing the asymmetry of the different hierarchical levels within the federated organization while providing highly effective detection and guiding mitigation measures toward graceful lockdown of the society. To exemplify the proposed system, we choose a remote patient monitoring as use case. This use case is evaluated considering different numbers of endorsed peers on a solution that is based on the integration of distributed ledger technologies and NB-IoT (narrowband IoT). An experimental setup is used to evaluate the performance of this integration, in which the end-to-end latency is slightly increased when a new endorsed element is added. However, the system reliability, privacy, and interoperability are guaranteed. In this sense, we expect active participation of empowered citizens to supplement the more usual top-down management of epidemics.
Ankit Khushal Barai, Robin Singh Bhadoria, Jyotshana Bagwari, Ivan Perl
Conventional machine learning (ML) needs centralized training data to be present on a given machine or datacenter. The healthcare, finance, and other institutions where data sharing is prohibited require an approach for training ML models in secured architecture. Recently, techniques such as federated learning (FL), MIT Media Lab's Split Neural networks, blockchain, aim to address privacy and regulation of data. However, there are difference between the design principles of FL and the requirements of Institutions like healthcare, finance, etc., which needs blockchain-orchestrated FL having the following features: clients with their local data can define access policies to their data and define how updated weights are to be encrypted between the workers and the aggregator using blockchain technology and also prepares audit trail logs undertaken within network and it keeps actual list of participants hidden. This is expected to remove barriers in a range of sectors including healthcare, finance, security, logistics, governance, operations, and manufacturing.
Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keeps raising on related practical problems, including1) how to protect data security and location privacy? 2) how to efficiently and dynamically deploy SRC Internet of Thing (IoT) witnesses to monitor large areas?To answer these questions, in this paper, we propose a decentralized and permissionless blockchain protocol, namedBychain. Specifically, 1) a privacy-preserving SRC protocol for activity-tracking and corresponding generalized block structure is developed, by connecting an interactive zero-knowledge proof protocol and the key escrow mechanism. As a result, connections between personal identity and the ownership of on-chain location information are decoupled. Meanwhile, the owner of the on-chain location data can still claim its ownership without revealing the private key to anyone else. 2) An artificial potential field-based incentive allocation mechanism is proposed to incentivize IoT witnesses to pursue the maximum monitoring coverage deployment. We implemented and evaluated the proposed blockchain protocol in the real-world using the Bluetooth 5.0. The storage, CPU utilization, power consumption, time delay, and security of each procedure and performance of activities are analyzed. The experiment and security analysis is shown to provide a real-world performance evaluation.
Andrea Tesei, Domenico Lattuca, Marco Luise, Paolo Pagano · 6 authors
The widespread adoption of Cooperative, Connected, and Automated Mobility (CCAM) applications requires the implementation of stringent security mechanisms to minimize the surface of cyber attacks. Authentication is an effective process for validating user identity in vehicular networks. However, authentication alone is not enough to prevent dangerous attack situations. Existing security mechanisms are not able to promptly revoke the credentials of misbehaving vehicles, thus tolerate malicious actors to remain trusted in the system for a long time. The resulting vulnerability window allows the implementation of complex attacks, thus posing a substantial impairment to the security of the vehicular ecosystem. In this paper we propose a Distributed Ledger-based Vehicular Revocation Scheme that improves the state of the art by providing a \textit{vulnerability window} lower than 1 second, reducing well-behaved vehicles exposure to sophisticated and potentially dangerous attacks. The proposed scheme harnesses the advantages of the underlying Distributed Ledger Technology (DLT) to implement a privacy-aware revocation process while being fully transparent to all participating entities. Furthermore, it meets the critical message processing times defined by EU and US standards, thus closing a critical gap in the current international standards. Theoretical analysis and experimental validation demonstrate the effectiveness and efficiency of the proposed scheme, where DLT streamlines the revocation operation overhead and delivers an economically viable yet scalable solution against cyber attacks on vehicular systems.
This paper presents a recruiting application that is based on blockchain technology and uses PrivateSky platform, all ecosystem being developed in accordance with General Data Protection Regulation (GDPR). The presented application can be easily adapted to any industry recruitment methodology. It uses new privacy principles applied with help of blockchain technologies and the authors will present in the final paper some functionalities obtained with help of Machine Learning (ML) and Natural Language Processing (NLP) techniques.
In current healthcare systems, electronic medical records (EMRs) are always located in different hospitals and controlled by a centralized cloud provider. However, it leads to single point of failure as patients being the real owner lose track of their private and sensitive EMRs. Hence, this article aims to build an access control framework based on smart contract, which is built on the top of distributed ledger (blockchain), to secure the sharing of EMRs among different entities involved in the smart healthcare system. For this, we propose four forms of smart contracts for user verification, access authorization, misbehavior detection, and access revocation, respectively. In this framework, considering the block size of ledger and huge amount of patient data, the EMRs are stored in cloud after being encrypted through the cryptographic functions of elliptic curve cryptography (ECC) and Edwards-curve digital signature algorithm (EdDSA), while their corresponding hashes are packed into blockchain. The performance evaluation based on a private Ethereum system is used to verify the efficiency of proposed access control framework in the real-time smart healthcare system.
Blockchain has been a new technology that involves an emergent evolution of many areas, particularly in finance, healthcare and supply chain. Due to its remarkable features including immutability, decentralization, peer-to-peer networking and capability to corporate with data protection and privacy, it is attractive to system integration. In traditional system integration architecture, a central mediator with a silo to store transactions stands in the center of integration to provide trust and transparency for participated systems. Hence existing central mediator could become a possible target of Distributed Denial of Service (DDoS) cyberattack and single point of failure. This paper presents a blockchain-base system architecture and its implementation for system integration which doesn't need a central mediator. The blockchain-based system has been implemented using Ethereum Blockchain in compliance with the General Data Protection Regulation (GDPR) standard to support the integration of DNA profiles information systems.
Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed. However, it is of great challenge for current FL frameworks to improve communication performance and maintain the security and robustness under malicious node attacks. In this paper, we propose Galaxy Federated Learning Framework(GFL), a decentralized FL framework based on blockchain. GFL introduces the consistent hashing algorithm to improve communication performance and proposes a novel ring decentralized FL algorithm(RDFL) to improve decentralized FL performance and bandwidth utilization. In addition, GFL introduces InterPlanetary File System(IPFS) and blockchain to further improve communication efficiency and FL security. Our experiments show that GFL improves communication performance and decentralized FL performance under the data poisoning of malicious nodes and non-independent and identically distributed(Non-IID) datasets.
Elli Androulaki, Jan Camenisch, Angelo De, Maria Dubovitskaya · 6 authors
Token payment systems were the first application of blockchain technology and are still the most widely used one. Early implementations of such systems, like Bitcoin or Ethereum, provide virtually no privacy beyond basic pseudonymity: all transactions are written in plain to the blockchain, which makes them linkable and traceable.
Artificial intelligence (AI) has been witnessed to provide valuable solutions to all walks of life. However, data island and computing resources limitations in the centralized AI architectures have increased their technical barriers, and thus distributed AI collaboration in data, models, and resources has attracted intensive research interests. Since the existing trust-based collaboration models are no longer applicable for the large-scale distributed collaboration among trustless machines in open and dynamic environments, this article proposes a novel decentralized AI collaboration framework, i.e., learning markets (LM), in which blockchain provides a trustless environment for collaboration and transaction, while smart contracts serve as software-defined agents to encapsulate and process scalable collaboration relationships and market mechanisms. LM can not only help those participants without mutual trust realize collaborative mining with dynamic and quantitative rewards but also build an AI market with natural auditability and traceability for trading trusted and verified models. We implement and comprehensively analyze LM based on the Ethereum interplenary file system platform (IPFS), and the results prove that it has advantages in collaboration fairness, transparency, security, decentralization and universality. Based on our collaboration framework, distributed AI contributors are expected to cooperate and complete those learning tasks that cannot be done previously due to lack of complete data, sufficient computing resources and state-of-the-art models.
Manas Pratim Bhattacharya, Pavol Zavarsky, Sergey Butakov
Self-sovereign identities provide user autonomy and immutability to individual identities and full control to their identity owners. The immutability and control are possible by implementing identities in a decentralized manner on blockchains that are specially designed for identity operations such as Hyperledger Indy. As with any type of identity, self-sovereign identities too deal with Personally Identifiable Information (PII) of the identity holders and comes with the usual risks of privacy and security. This study examined certain scenarios of personal data disclosure via credential exchanges between such identities and risks of man-in-the-middle attacks in the blockchain based identity system Hyperledger Indy. On the basis of the findings, the paper proposes the following enhancements: 1) A novel attribute sensitivity score model for self-sovereign identity agents to ascertain the sensitivity of attributes shared in credential exchanges 2) A method of mitigating man-in-the-middle attacks between peer self-sovereign identities and 3) A novel quantitative model for determining a credential issuer's reputation based on the number of issued credentials in a window period, which is then utilized to calculate an overall confidence level score for the issuer.
The pervasive adoption of Internet-connected digital services has led to a growing concern in the personal data privacy of their customers. On the other hand, machine learning (ML) techniques have been widely adopted by digital service providers to improve operational productivity and customer satisfaction. ML inevitably accesses and processes users' personal data, which could potentially breach the relevant privacy protection regulations if not performed carefully. The situation is exacerbated by the cloud-based implementation of digital services when user data are captured and stored in distributed locations, hence aggregation of the user data for ML could be a serious breach of privacy regulations. In this backdrop, Federated Learning (FL) is an emerging area that allows ML on distributed data without the data leaving their stored location. However, depending on the nature of the digital services, data captured at different locations may carry different significance to the business operation, hence a weighted aggregation will be highly desirable for enhancing the quality of the FL-learned model. Furthermore, to prevent leakage of user data from the aggregated gradients, cryptographic mechanisms are needed to allow secure aggregation of FL. In this paper, we propose a privacy-enhanced FL scheme for supporting secure weighted aggregation. Besides, by devising a verification protocol based on Zero-Knowledge Proof (ZKP), the proposed scheme is capable of guarding against fraudulent messages from FL participants. Experimental results show that our scheme is practical and secure. Compared to existing FL approaches, our scheme achieves secure weighted aggregation with an additional security guarantee against fraudulent messages with an affordable 1.2 times runtime overheads and 1.3 times communication costs.
Data security and privacy preserving have gradually become key issues in the process of data cooperation. Most existing data multiparty computing solutions rely on trusted third parties, and the data owner loses control of the data, which may easily lead to privacy data leakage. This paper designed a multiparty computation system combined with blockchain and secure multiparty computation technology. This system uses the decentralization of the blockchain to remove dependence on third parties in existing data cooperation system, transfers secure multiparty computation protocol to off-chain execution, and on-chain smart contract is responsible for selecting agent computing nodes fairly and generating computation certificate. This system realizes the safe and fair joint computation of multiparty data without the data owner losing control of their original data.
Data sharing and exchange is an effective way to release the vitality and value of data resources, but it also brings security problems, especially in the distributed environment. In this paper, a data sharing scheme based on blockchain, smart contract and attribute-based encryption is proposed, which supports attribute level user authority revocation, and is suitable for the privilege management of data sharing in distributed environment. Firstly, the model of data sharing and exchange is given; secondly, the operation of data sharing and exchange is defined; finally, implementation by smart contract is given and an experiment is carried out. The results show that the scheme can effectively solve the problem of data access authorization and revocation in distributed environment. In this scheme, we introduce the trusted third party as the key management center to generate, encrypt and decrypt the key, thus reducing the pressure on the client.
Owing to its benefits such as flexibility, scalability, and interoperability, Software-Defined Networking (SDN) has been incorporated into Internet of Vehicles (IoV) to cope with the increasing demands of vehicular applications. The integration of SDN and IoV, namely SDN-IoV, can enrich many new applications for intelligent transportation such as traffic monitoring, smart navigation, and self-driving. The spatial crowdsourcing technology has been adopted as an effective data collection and processing method that is the premise of various SDN-IoV applications. However, as huge amounts of data are generated in spatial crowdsourcing services, the data privacy and security has become a key challenge for SDN-IoV. To overcome abovementioned challenge, a Deep Reinforcement Learning (DRL) and Blockchain empowered Spatial Crowdsourcing System (DB-SCS) is proposed. In DB-SCS, we design an improved multi-blockchain structure and a blockchain-based hierarchical task management method, which divide the spatial tasks into different categories according to the privacy requirements and the areas of the task and then decompose different categories of tasks and task receivers into sub-blockchains. While guaranteeing the data privacy, DB-SCS can also enhance the spatial crowdsourcing performance by using the proposed DRL-based management strategy to dynamically select the consensus algorithm, block size, and block generation rule. Extensive simulation experiments demonstrate that the DB-SCS can obtain high throughput, low overhead, and data privacy under various SDN-IoV scenarios.
Based on the characteristics of blockchain technology, an aviation data exchange platform is designed to realize the rapid exchange of aviation data between business departments. The platform design adopts alliance chain and private chain. Each data production unit, such as relevant departments of air traffic control, airlines, airports, etc., has its own private chain. Multiple private chains form an alliance chain. In each private chain, there are servers, which store encrypted aviation data. The alliance chain builds alliance block based on the hash value of private chain block, and uses re-encryption to exchange data by changing encryption, the platform achieves data integrity, access control, exchange security and other security goals.
Currently, the outbreak of COVID-19 pandemic has caused catastrophic effect on every aspect of our lives, globally. The entire human race of all countries and regions has suffered devastating losses. With its high infectiousness and mortality rate, it is of great significance to carry out effective precautions and prevention of COVID-19. Specifically, the transportation system has been confirmed as one of the crucial spreading routes. Hence, enhancing healthcare monitoring and infection tracking for high-mobility transportation system is infeasible for pandemic control. Meanwhile, due to the promising advantages in the emerging intelligent transportation system (ITS), vehicular ad hoc networks (VANETs) is able to collect and process relevant vehicular data for improving the driving experience and road safety, which provide a way for non-contact automatic healthcare monitoring. Furthermore, the proliferating cloud computing and blockchain techniques enable sufficient processing and storing capabilities, along with decentralized remote auditing towards heterogenous vehicular data. In this case, the automated infection tracking for pandemic control could be achieved accordingly. For the above consideration, in this paper we develop a practical homomorphic authentication scheme for cloud-assisted VANETs, where the healthcare monitoring for all involving passengers is provided. Notably, the integrated cloud-assisted VANET infrastructure is utilized, where the hybrid medical data acquisition module is attached. In this way, timely, non-contact measurement on all passengers’ physical status can be remotely done by vehicular cloud (VC), which could also drastically improve the efficiency and guarantee safety. Vulnerabilities of the employed dedicated-short-range-communication (DSRC) technique could be properly addressed with the applied homomorphic encryption design. Additionally, the decentralized blockchain-based vehicle recording mechanism is cooperatively performed by VC and edge units. Infection tracking on specific vehicle and individual can be offered in this way. Each signature sequence is collaboratively maintained and verified by the current roadside unit (RSU) and its neighbor RSUs. The security analysis demonstrates that the proposed scheme is secure against major attacks, while the performance comparison with the state-of-the-arts relevant methods are presented for efficiency discussion.
Current smart contract-enabled blockchain technology exhibits limited support for data quality assessment of transaction payloads. This is critical because blockchain aims at removing intermediaries, which often play an important role in guaranteeing a certain level of the quality of data used by a system. Moreover, owing to the immutability typical of blockchain, poor quality data are bound to remain stored in a blockchain, possibly forever. This article contextualizes the issue of data quality in blockchains, discussing how to extend or adapt blockchain technology to support data quality assessment and identifying a set of challenges for future research.
Harsh Desai, Mustafa Safa Özdayi, Murat Kantarcıoğlu
Federated Learning (FL) is a distributed, and decentralized machine learning protocol. By executing FL, a set of agents can jointly train a model without sharing their datasets with each other, or a third-party. This makes FL particularly suitable for settings where data privacy is desired. At the same time, concealing training data gives attackers an opportunity to inject backdoors into the trained model. It has been shown that an attacker can inject backdoors to the trained model during FL, and then can leverage the backdoor to make the model misclassify later. Several works tried to alleviate this threat by designing robust aggregation functions. However, given more sophisticated attacks are developed over time, which by-pass the existing defenses, we approach this problem from a complementary angle in this work. Particularly, we aim to discourage backdoor attacks by detecting, and punishing the attackers, possibly after the end of training phase. To this end, we develop a hybrid blockchain-based FL framework that uses smart contracts to automatically detect, and punish the attackers via monetary penalties. Our framework is general in the sense that, any aggregation function, and any attacker detection algorithm can be plugged into it. We conduct experiments to demonstrate that our framework preserves the communication-efficient nature of FL, and provide empirical results to illustrate that it can successfully penalize attackers by leveraging our novel attacker detection algorithm.
Rabeya Bosri, Mohammad Shahriar Rahman, Md Zakirul Alam Bhuiyan, Abdullah Al Omar
Data privacy is one of the intriguing problems in e-commerce site. For personal or business purposes, users have to disclose their private data to these e-commerce sites. Often such businesses use these highly sensitive data for computing artificial intelligence-driven analyses like recommendation generation without user consent. In the case of recommendation generation, data need to be analyzed at the business platforms. An automated personalization, based on artificial intelligence, on a list of products with respect to user interest is generated by a recommender system. However, the secure utilization of user data is absent in such systems. This paper proposes Private-Rec, a privacy-preserving platform for a recommendation system through the integration of artificial intelligence and blockchain. In Private-Rec, blockchain gives the user a secure environment through the distributed attribute in which data can be used with the required permission. Under this platform, users receive incentives (i.e., point, discount) from the recommended company for sharing their data to be used for computing recommendations. The Private-Rec platform has been studied empirically.