The importance and usage of the distributed cloud is increasing rapidly over a traditionally centralized cloud for the storing and exchanging of digital assets between untrusted parties in many business sectors. Storing the digital assets in the distributed cloud is considered superior to traditional cloud computing in terms of environmentally friendly, cost, security and other technical dimensions. In this article, a contemporary architecture DistProv is proposed where an open source distributed cloud IPFS is used to store and transfer the digital assets between the consignor and consignee. These two are untrusted parties exchanging sensitive documents secured by cryptographic algorithms with permission-based access verified by ethereum smart contracts using zero-knowledge proof (ZKP) and simultaneously publishing the provenance data about the digital asset as a transaction on the blockchain. This article also discusses on verifying the integrity of the digital assets and authentication of the consignor and thus preserving a strong CIA triad.
Vangelis Malamas, Thomas K. Dasaklis, Panayiotis Kotzanikolaou, Mike Burmester · 5 authors
The Internet of Medical Things (IoMT) provides ubiquitous healthcare services for patient monitoring and treatment. However, the interaction between doctors, patients, healthcare personnel and device manufacturers, with different and often conflicting security and privacy objectives, make such services vulnerable and subject to exploitation. In addition, since parties may require different access levels and the IoMT devices involve different functionalities, access control can be challenging. In this paper, we propose a blockchain-enabled authorization framework for managing both IoMT devices and medical files by creating a distributed chain of custody and health data privacy scheme. The core idea is to build trust domains for the various stakeholders and IoMT devices, in such a way that fine-grain access is enabled by taking into account critical attributes of the IoMT ecosystem such as a) the different roles and capabilities of the IoMT devices and b) their interaction with the users/stakeholders. A private blockchain is used in combination with on-chain smart contracts to allow for a forensics-by-design management architecture with audit trails for integrity and provenance guarantees as well as health data privacy. The private blockchain ecosystem is authenticated by a proof-of-medical-stake consensus mechanism that is tailored for medical applications.
We present a blockchain based system that allows data owners, cloud vendors, and AI developers to collaboratively train machine learning models in a trustless AI marketplace. Data is a highly valued digital asset and central to deriving business insights. Our system enables data owners to retain ownership and privacy of their data, while still allowing AI developers to leverage the data for training. Similarly, AI developers can utilize compute resources from cloud vendors without loosing ownership or privacy of their trained models. Our system protocols are set up to incentivize all three entities - data owners, cloud vendors, and AI developers to truthfully record their actions on the distributed ledger, so that the blockchain system provides verifiable evidence of wrongdoing and dispute resolution. Our system is implemented on the Hyperledger Fabric and can provide a viable alternative to centralized AI systems that do not guarantee data or model privacy. We present experimental performance results that demonstrate the latency and throughput of its transactions under different network configurations where peers on the blockchain may be spread across different datacenters and geographies. Our results indicate that the proposed solution scales well to large number of data and model owners and can train up to 70 models per second on a 12-peer non optimized blockchain network and roughly 30 models per second in a 24 peer network.
The emerging blockchain technology, powered by the smart contract functionality, is expected to revolutionize traditional systems by decentralization and autonomy. When the blockchain technology is applied in real-life systems, a smart contract may need to take in sensitive data to execute its code. For example, a decentralized medical insurance smart contract needs access to personal health data. Therefore, it is crucial to guarantee both privacy and authenticity of data sent to the smart contract, so that everyone can verify data without leaking sensitive information. Currently, only few works have tried to achieve privacy and authenticity at the same time. In this work, we first propose zk-DASNARK, a zero-knowledge SNARK scheme for authenticated data. It is designed by combining the zk-SNARK technique with digital signature in an effective way. Based on zk-DASNARK, we design zk-AuthFeed, a zero-knowledge authenticated data feed scheme to achieve both data privacy and authenticity for smart contract. We implement zk-AuthFeed and conduct comprehensive experiments on Ethereum. The experiments show that zk-AuthFeed is highly efficient: key generation takes about 7 seconds only, proof generation takes less than 2 seconds, and proof verification takes less than 40 ms.
Matthias Lohr, Jonathan Hund, Jan Jürjens, Steffen Staab
In railway incidents, data from sensors installed on railway tracks can help finding the cause of the incident and identifying the responsible parties. Since the data collected may contain business-relevant information, it is usually treated as confidential by the companies collecting it. However, this data can only be considered as evidence if it can be proven that the data is genuine and unaltered, even if it is only accessible for involved companies in the first place. In this paper, we present an approach to ensure the genuineness of confidential railway measurement data using distributed ledgers and describe an approach for selectively sharing parts of the data without compromising confidentiality or the verifiability of genuineness. We discuss the specific characteristics of rail wayside measurement data, existing approaches to ensure data genuineness and the necessary modifications to apply them to rail wayside measurement data. We also discuss how our approach can be generalized beyond the railway domain to show how distributed ledger-based approaches can be used to ensure the genuineness of confidential and selectively shared data.
Machine learning has recently enabled large advances in artificial intelligence, but these tend to be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and re-train them. We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model. This model will be shared publicly on a blockchain where it can be free to use for inference. Ideal learning problems include scenarios where a model is used many times for similar input such as personal assistants, playing games, recommender systems, etc. In order to maintain the model's accuracy with respect to some test set we propose both financial and non-financial (gamified) incentive structures for providing good data. A free and open source implementation for the Ethereum blockchain is provided at https://github.com/microsoft/0xDeCA10B.
In this paper, we have achieved privacy protection and high transparency in a permissioned blockchain. There is a sidechain that connects the permissionless blockchain and the permissioned blockchain. The behavior in the permissioned blockchain is almost a black box from the perspective of the permissionless blockchain. While this fact is useful for privacy protection, there is room for improvement in terms of transparency. To improve the transparency of the permissioned blockchain under privacy protection, we consider traceability in the permissioned blockchain consisting of the following three properties: trade privacy (who trades with whom and at what asset amount), preservation (the total amount inside the permissioned blockchain, including deposits and withdrawals to the permissionless blockchain, is immutable), and noninvolvement (some members in the permissioned blockchain are not involved in some trades, and it is possible to prove that specified members performed the transaction). To the best of our knowledge, we are the first to achieve both preservation and noninvolvement while protecting the privacy of transactions. Our approach is as follows. We model traceability based on the hidden Markov model. Because the proof of traceability requires the calculation of more than quadratic degrees, we encrypt this model by homomorphic encryption. The number of participants in the permissioned blockchain corresponds to the number of additions in the model. Then, we can construct the encrypted model by employing somewhat homomorphic encryption. The establishment of the original model is verifiable by applying the noninteractive zero-knowledge proof of the knowledge that the plaintext is equal to zero. This is an adaptation of Benhamouda et al. (Asiacrypt 2014).
This paper looks at the development of blockchain technologies that promise to bring new tools for the management of private data, providing enhanced security and privacy to individuals. Particular interest presents solutions aimed at reorganizing data flows in the Internet of Things (IoT) architectures, enabling the secure and decentralized exchange of data between network participants. However, as this paper argues, the promised benefits are counterbalanced by a significant shift towards the propertization of private data, underlying these proposals. Considering the unique capacity of blockchain technology applications to imitate and even replace traditional institutions, this aspect may present certain challenges, both of technical and ethical character. In order to highlight these challenges and associated concerns, this paper identifies the underlying techno-economic factors and normative assumptions defining the development of these solutions amounting to technologically enabled propertization. It is argued that without careful consideration of a wider impact, such blockchain applications could have effects opposite to the intended ones, thus contributing to the erosion of privacy for IoT users.
Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan · 5 authors
Internet-of-Things (IoT) companies strive to get feedback from users to improve their products and services. However, traditional surveys cannot reflect the actual conditions of customers' due to the limited questions. Besides, survey results are affected by various subjective factors. In contrast, the recorded usages of IoT devices reflect customers' behaviours more comprehensively and accurately. We design an intelligent system to help IoT device manufacturers to take advantage of customers' data and build a machine learning model to predict customers' requirements and possible consumption behaviours with federated learning (FL) technology. The FL consists of two stages: in the first stage, customers train the initial model using the phone and the edge computing server collaboratively. The mobile edge computing server's high computation power can assist customers' training locally. Customers first collect data from various IoT devices using phones, and then download and train the initial model with their data. During the training, customers first extract features using their mobiles, and then add the Laplacian noise to the extracted features based on differential privacy, a formal and popular notion to quantify privacy. After achieving the local model, customers sign on their models respectively and send them to the blockchain. We use the blockchain to replace the centralized aggregator which belongs to the third party in FL. In the second stage, miners calculate the averaged model using the collected models sent from customers. By the end of the crowdsourcing job, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. Besides, to attract more customers to participate in the crowdsourcing FL, we design an incentive mechanism, which awards participants with coins that can be used to purchase other services provided by the company.
Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan · 8 authors
Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers' activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers' privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.
Personal health record system (PHR system) stores health-related information of an individual. PHR system allows the data owner to manage and share his/her data with selected individuals. The originality or tamper resistance feature is crucial for PHR system because of the irreversible consequence of incorrect information. Blockchain technology becomes a potential solution due to its immutability and irreversibility properties. Unfortunately, some technical impediments such as limited storage, privacy concern, consent irrevocability, inefficient performance, and energy consumption exist. This work aims to handle these blockchain drawbacks and propose a blockchain-based PHR model. The proposed model is built using the blockchain technology to support a tamper resistance feature. Proxy reencryption and other cryptographic techniques are employed to preserve privacy. Features of the proposed model include fine-grained and flexible access control, revocability of consent, auditability, and tamper resistance. A detailed security analysis shows that the proposed model is provably secure for privacy and tamper resistance. The performance analysis shows that the proposed model achieves a better overall performance compared with the existing approach in the literature. Thus the proposed model is more suitable for the PHR system usage.
The Covid-19 pandemic has made individuals and organizations to rethink the way of handling identity verification and credentials sharing particularly in quarantined situations. In this study, we investigate the inefficiencies of traditional identity systems, and discuss how a proper implementation of Blockchain technology would result in safer, more secure, privacy respecting and remote friendly identity systems. As a result, we propose a Blockchain based framework for digital identity verification, record attestation and record sharing, and we explain the framework in details with certain use cases. Our proposed framework promotes individuals to fully control their identity data and govern the level of the identity data sharing.
Offloading computation-intensive tasks (e.g., blockchain consensus processes and data processing tasks) to the edge/cloud is a promising solution for blockchain-empowered mobile edge computing. However, the traditional offloading approaches (e.g., auction-based and game-theory approaches) fail to adjust the policy according to the changing environment and cannot achieve long-term performance. Moreover, the existing deep reinforcement learning-based offloading approaches suffer from the slow convergence caused by high-dimensional action space. In this paper, we propose a new model-free deep reinforcement learning-based online computation offloading approach for blockchain-empowered mobile edge computing in which both mining tasks and data processing tasks are considered. First, we formulate the online offloading problem as a Markov decision process by considering both the blockchain mining tasks and data processing tasks. Then, to maximize long-term offloading performance, we leverage deep reinforcement learning to accommodate highly dynamic environments and address the computational complexity. Furthermore, we introduce an adaptive genetic algorithm into the exploration of deep reinforcement learning to effectively avoid useless exploration and speed up the convergence without reducing performance. Finally, our experimental results demonstrate that our algorithm can converge quickly and outperform three benchmark policies.
Abstract Vehicular social network is emerging as a new promising concept, combining two types of network paradigms, namely, vehicular networks and social networks. In order to manage efficiently the security and the control of the network, this paper proposes a new framework based on the emerging concepts of software‐defined vehicular network (SDVN) and blockchain. Using the SDVN makes the network more programmable, virtualized, and partitionable. However, on the other hand, it also creates a well‐known vulnerability of a single point of failure. Hence, we propose to introduce the blockchain paradigm that will enable the certification of transactions and ensure data anonymity in a fully distributed manner. To this end, three levels of controllers are needed: a principal controller (PC), roadside units (RSUs), and a local controller. In order to dynamically select miners, a distributed miners connected dominating set algorithm (DM‐CDS) has been proposed. The DM‐CDS is a single‐phase distributed algorithm that supports a dynamic topology based on a trust model and some other network parameters, such as the connectivity degree, the average link quality indicator, and the rank. The performance of the proposed DM‐CDS is evaluated throughout multiple scenarios using different parameters, such as trust metric, node density, node mobility, and radio range. The obtained results highlight the importance of such proposed architecture, especially in terms of number of required miners. For instance, when the density of nodes increases, the number of selected miners increases similarly to when the network length increases. The node mobility impacts also on the stability of the selected miners, in terms of withdrawing and joining, showing a variation between 0% and 10%. The trust metric has also an important impact on the selection of miners, as only nodes with a higher trust level are selected to endorse the roles of miners.
Recently, crowdsourcing has emerged as a new computing paradigm to solve problems that need human intrinsic, such as image annotation. However, there are two limitations in existing crowdsourcing platforms, i.e. non-transparent incentive mechanism and isolated profiles of workers, which harms the interests of both requesters and workers. Meanwhile, Blockchain technology introduces a solution to build a transparent, immutable data model in the Byzantine environment. Moreover, Blockchain systems (e.g. Ethereum) can also support the Tuning-complete script called smart contracts. Thus, we are motivated to use the feature of the transparent data model and smart contract in Blockchain to address the two limitations. Based on the proposed solutions, we have designed a Blockchain based framework which supports foundations of general crowdsourcing platforms. In addition, our framework also has following novel features: (1) it provides the transparent incentive mechanisms; (2) it supports a trusted worker's profile sharing in a cross-platform mode.
With the dramatically increasing deployment of the Internet of Things (IoT), remote monitoring of health data to achieve intelligent healthcare has received great attention recently. However, due to the limited computing power and storage capacity of IoT devices, users' health data are generally stored in a centralized third party, such as the hospital database or cloud, and make users lose control of their health data, which can easily result in privacy leakage and single-point bottleneck. In this paper, we propose Healthchain, a large-scale health data privacy preserving scheme based on blockchain technology, where health data are encrypted to conduct fine-grained access control. Specifically, users can effectively revoke or add authorized doctors by leveraging user transactions for key management. Furthermore, by introducing Healthchain, both IoT data and doctor diagnosis cannot be deleted or tampered with so as to avoid medical disputes. Security analysis and experimental results show that the proposed Healthchain is applicable for smart healthcare system.
Blockchain has the potential to render the transaction of information more secure and transparent. Nowadays, transportation data are shared across multiple entities using heterogeneous mediums, from paper collected data to smartphone. Most of this data are stored in central servers that are susceptible to hacks. In some cases shady actors who may have access to such sources, share the mobility data with unwanted third parties. A multi-layered Blockchain framework for Smart Mobility Data-market (BSMD) is presented for addressing the associated privacy, security, management, and scalability challenges. Each participant shares their encrypted data to the blockchain network and can transact information with other participants as long as both parties agree to the transaction rules issued by the owner of the data. Data ownership, transparency, auditability and access control are the core principles of the proposed blockchain for smart mobility data-market. In a case study of real-time mobility data sharing, we demonstrate the performance of BSMD on a 370 nodes blockchain running on heterogeneous and geographically-separated devices communicating on a physical network. We also demonstrate how BSMD ensures the cybersecurity and privacy of individual by safeguarding against spoofing and message interception attacks and providing information access management control.
Siming Wang, Xumin Huang, Rong Yu, Yan Zhang · 5 authors
With the fast expanding scale of vehicular networks, vehicular edge computing (VEC) has emerged and attracted growing attention from both industry and academia. Parked vehicles (PVs) have great potential to join vehicular networks for sharing their idle computing and networking resources. However, due to the underlying security and privacy threats, it is challenging to fairly motivate PVs for resource sharing in an efficient and secure way. In this paper, we propose a permissioned vehicular blockchain for secure and efficient resource sharing in VEC, namely, Parkingchain. We first design smart contract to achieve secure resource sharing and efficient service provisioning between PVs and service requesters (SRs). A multi-weight subjective logic based delegated Byzantine Fault Tolerance (DBFT) consensus mechanism is presented to improve the consensus process in Parkingchain. Further, we design a contract theory-based incentive mechanism to model the interactions between SR and PVs under asymmetric information scenario. Finally, numerical results demonstrate that the proposed incentive mechanism is effective and efficient compared with existing schemes.
Giancarlo Fortino, Fabrizio Messina, Domenico Rosaci, Giuseppe M. L. Sarnè
The Internet of Things (IoT) makes attractive services available to smart objects and humans. To aim this, IoT devices need high sensing, reasoning, and real-time acting capabilities that can be also obtained by promoting adaptive forms of cooperation machine-to-machine among smart objects. The convergence of IoT and multiagent systems also relies on the association between software agents with IoT devices for exploiting their social attitude of interacting and cooperating for services. However, the choice of reliable partners for cooperation can be very difficult when IoT devices migrate across different environments, where the most part of their members will be unreferenced with respect to their trustworthiness. It is well known that agents reputation can be a viable aspect to consider to form social groups; therefore, a possible solution to this problem is to form groups of agents in each IoT environment, based on their social capabilities. In this respect, the first contribution of this paper is represented by a reputation model focused on building the reputation capital of each agent. Second, an algorithm capable to form groups of agents in IoT environments on the basis of their reputation capital was designed. Finally, since in this contest, it is important to spread reliable and certified information about the device/agent reputation in a distributed environment, the third contribution is represented by the adoption of the blockchain technology to certify the reputation capital. Some experiments we have performed show that the model is capable to detect almost all the misleading agents if their percentage is under a high enough threshold, and that good results in term of group composition are obtained. Moreover, the simulations show that, by adopting our model, malicious devices always pay for services significantly more than honest ones. We argue that the individual reputation capital of devices and, consequently, the overall reputation capital of the IoT community can take benefit from the adoption of the proposed approach.
Blockchain technology has been devised from the first cryptocurrency known as Bitcoin and was soon noted to have a much more full range of applications beyond serving as the platform for the digital cryptocurrency. Conceptually, a blockchain (BC) is defined as a decentralized ledger that registers every transactional operation made in encrypted data format. The implementation of such concept bring about augmented security, enforce accountability, and could potentially accelerate a shift in the work environment dynamics from the current centralized structure to a decentralized, cooperative chain of transactions by encouraging trust and transparency. This paper presents an overview of BC technology and its applications in the AEC industry and its potential integration with the building information (BIM) process. Furthermore, the study explores how BC technologies can improve the BIM working environment by reinforcing network security, providing more reliable data storage and management of permissions, ensuring change tracing and data ownership. The paper discusses the fundamental principles of distributed ledgers, their current applications and potential future advances, and their classification based on inherent characteristics of consensus reaching and permission management. The study presents some examples the prospective application of BC technologies in enhancing the framework for automating the code compliance checking process such as smart contract technologies and Hyperledger Fabric, as well as discussing the pros, cons, possible directions, and future research directions.
The global Electronic Health Record (EHR) market is growing dramatically and expected to reach $39.7 billions by 2022. To safe-guard security and privacy of EHR, access control is an essential mechanism for managing EHR data. This paper proposes a hybrid architecture to facilitate access control of EHR data by using both blockchain and edge node. Within the architecture, a blockchain-based controller manages identity and access control policies and serves as a tamper-proof log of access events. In addition, off-chain edge nodes store the EHR data and apply policies specified in Abbreviated Language For Authorization (ALFA) to enforce attribute-based access control on EHR data in collaboration with the blockchain-based access control logs. We evaluate the proposed hybrid architecture by utilizing Hyperledger Composer Fabric blockchain to measure the performance of executing smart contracts and ACL policies in terms of transaction processing time and response time against unauthorized data retrieval.
Gianmarco Baldini, José L. Hernández-Ramos, Gary Steri, Sara N. Matheu
The future deployment of vehicular networks for road transportation (the so called Cooperative Intelligent Transport System (C-ITS) in Europe or Connected Vehicles program in USA) should be based on the secure exchange of messages among the vehicles and the infrastructure communication nodes. Deployment projects in various parts of the world are setting up Public Key Infrastructures (PKI) to support the security and privacy aspects on vehicular communications. While the use of PKI is a known technology to build a security framework for C-ITS and Connected Vehicles deployments and it will provide the basic needed services for integrity and authentication, research communities around the world are exploring extensions of these frameworks to implement specific functions like misbehavior detection and revocation. In addition, new techniques to mitigate privacy risks in vehicular networks are explored. In this paper, we address these aspects by proposing the use of blockchain in combination with a zone keys concept where the authorization certificates produced by the PKI are provided to the vehicles only if specific conditions stored in the blockchain are valid. We show how the concepts described in this paper can enhance the PKI-based frameworks through an efficient revocation mechanism, and mitigating privacy risks as well.