The crowdsourcing schemes which utilize the social network to solve complex tasks are an important part of open cooperation over the Internet. Although blockchain-based crowdsourcing schemes have considerable advantages in decentralization and data sharing, there is still a challenge to gurantee the security of crowdsourced-sensitive information and the fairness of crowdsourcing on the blockchain. To this end, this article investigates a crowdsourcing scheme based on blockchain. First, we define the basic requirements of blockchain-based crowdsourcing schemes including fairness, confidentiality, and integrity. And then, using secure hash, commitment, and homomorphic encryption, we propose a blockchain-based secure and fair crowdsourcing scheme, that is, BFC. The analysis results show that our scheme can satisfy the above requirements. Finally, the experimental results show that the computational overhead of the BFC scheme is acceptable to both the requester and the workers. In a word, our proposed crowdsourcing scheme has good expansibility in reality.
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.
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.
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.
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.
Mobile Ad-hoc Cloud (MAC) is the constellation of nearby mobile devices to serve the heavy computational needs of the resource-constrained edge devices. One of the major challenges of MAC is to convince the mobile devices to offer their limited resources for the shared computational pool. Credit-based rewarding system is considered as an effective way of incentivizing the arbitrary mobile devices for joining the MAC network and to earn the credits through computational crowdsourcing. The next challenge is to get the reliable computation as incentives attract the malicious devices to submit fake computational results for claiming their reward and we have used the blockchain based reputation system for identifying the malicious participants of MAC. This paper presents a malicious node identification algorithm integrated within the Iroha based permissioned blockchain. Iroha is a project of hyperledger which is focused on mobile devices and thus light-weight in nature. It is used for keeping the track of rewarding and reputation system driven by the malicious node detection algorithm. Experiments are conducted for evaluating the implemented test-bed and results show the effectiveness of algorithm in identifying the malicious devices and conducting reliable data analysis through the blockchain based computational crowdsourcing in MAC.
With the growth in popularity for cryptocurrencies the need for privacy preserving blockchains is growing as well. Zcash is such a blockchain, providing transaction privacy through zero-knowledge proofs. In this paper we analyze transaction linkability in Zcash based on the currency minting transactions (mining). Using predictable usage patterns and clustering heuristics on mining transactions an attacker can link to publicly visible addresses over 84% of the volume of the transactions that use a ZK-proof. Since majority of Zcash transactions are not yet using ZK-proofs, we show that overall 95.5% of the total number of Zcash transactions are potentially linkable to public addresses by just observing the mining activity.
This paper examines the way in which blockchain technology can be used to improve the verification of integrity of evidence in digital forensics. Some background into digital forensic practices and blockchain technology are discussed to provide necessary context. A particular scalable method of verifying point-in-time existence of a piece of digital evidence, using the OpenTimestamps (OTS) service, is described, and tests are carried out to independently validate the claims made by the service. The results demonstrate that the OTS service is highly reliable with a zero false positive and false negative error rate for timestamp attestations, but that it is not suitable for timesensitive timestamping due to the variance of the accuracy of timestamps induced by block confirmation times in the Bitcoin blockchain.
Yongjun Ren, Yan Leng, Fujian Zhu, Jin Wang · 5 authors
Wireless body area networks (WBANs) are expected to play a vital role in the field of patient-health monitoring shortly. They provide a convenient way to collect patient data, but they also bring serious problems which are mainly reflected in the safe storage of the collected data. The privacy and security of data storage in WBAN devices cannot meet the needs of WBAN users. Therefore, this paper adopts blockchain technology to store data, which improves the security of the collected data. Moreover, a storage model based on blockchain in WBAN is proposed in our solution. However, blockchain storage brings new problems, for example, that the storage space of blockchain is small, and the stored content is open to unauthorized attackers. To solve the problems above, this paper proposed a sequential aggregate signature scheme with a designated verifier (DVSSA) to ensure that the user's data can only be viewed by the designated person and to protect the privacy of the users of WBAN. In addition, the new signature scheme can also compress the size of the blockchain storage space.
Roman Overko, Rodrigo Ordóñez-Hurtado, Sergiy Zhuk, Pietro Ferraro · 6 authors
We introduce a permissioned distributed ledger technology (DLT) design for crowdsourced smart mobility applications. This architecture is based on a directed acyclic graph architecture (similar to the IOTA tangle) and uses both Proof-of-Work and Proof-of-Position mechanisms to provide protection against spam attacks and malevolent actors. In addition to enabling individuals to retain ownership of their data and to monetize it, the architecture also is suitable for distributed privacy-preserving machine learning algorithms, is lightweight, and can be implemented in simple internet-of-things (IoT) devices. To demonstrate its efficacy, we apply this framework to reinforcement learning settings where a third party is interested in acquiring information from agents. In particular, one may be interested in sampling an unknown vehicular traffic flow in a city, using a DLT-type architecture and without perturbing the density, with the idea of realizing a set of virtual tokens as surrogates of real vehicles to explore geographical areas of interest. These tokens, whose authenticated position determines write access to the ledger, are thus used to emulate the probing actions of commanded (real) vehicles on a given planned route by "jumping" from a passing-by vehicle to another to complete the planned trajectory. Consequently, the environment stays unaffected (i.e., the autonomy of participating vehicles is not influenced by the algorithm), regardless of the number of emitted tokens. The design of such a DLT architecture is presented, and numerical results from large-scale simulations are provided to validate the proposed approach.
User's digital identity information has privacy and security requirements. Privacy requirements include confidentiality of the identity information itself, anonymity of those who verify and consume a user's identity information and unlinkability of online transactions which involve a user's identity. Security requirements include correctness, ownership assurance and prevention of counterfeits of a user's identity information. Such privacy and security requirements, although conflicting, are critical for identity management systems enabling the exchange of users' identity information between different parties during the execution of online transactions. Addressing all such requirements, without a centralized party managing the identity exchange transactions, raises several challenges. This paper presents a decentralized protocol for privacy preserving exchange of users' identity information addressing such challenges. The proposed protocol leverages advances in blockchain and zero knowledge proof technologies, as the main building blocks. We provide prototype implementations of the main building blocks of the protocol and assess its performance and security.
David Hawig, C. Zhou, Sebastian Fuhrhop, Andre S Fialho · 5 authors
BACKGROUND: Distributed ledger technology (DLT) holds great potential to improve health information exchange. However, the immutable and transparent character of this technology may conflict with data privacy regulations and data processing best practices. OBJECTIVE: The aim of this paper is to develop a proof-of-concept system for immutable, interoperable, and General Data Protection Regulation (GDPR)-compliant exchange of blood glucose data. METHODS: Given that there is no ideal design for a DLT-based patient-provider data exchange solution, we proposed two different variations for our proof-of-concept system. One design was based purely on the public IOTA distributed ledger (a directed acyclic graph-based DLT) and the second used the same public IOTA ledger in combination with a private InterPlanetary File System (IPFS) cluster. Both designs were assessed according to (1) data reversal risk, (2) data linkability risks, (3) processing time, (4) file size compatibility, and (5) overall system complexity. RESULTS: The public IOTA design slightly increased the risk of personal data linkability, had an overall low processing time (requiring mean 6.1, SD 1.9 seconds to upload one blood glucose data sample into the DLT), and was relatively simple to implement. The combination of the public IOTA with a private IPFS cluster minimized both reversal and linkability risks, allowed for the exchange of large files (3 months of blood glucose data were uploaded into the DLT in mean 38.1, SD 13.4 seconds), but involved a relatively higher setup complexity. CONCLUSIONS: For the specific use case of blood glucose explored in this study, both designs presented a suitable performance in enabling the interoperable exchange of data between patients and providers. Additionally, both systems were designed considering the latest guidelines on personal data processing, thereby maximizing the alignment with recent GDPR requirements. For future works, these results suggest that the conflict between DLT and data privacy regulations can be addressed if careful considerations are made regarding the use case and the design of the data exchange system.
We present models that utilize smart contracts and interledger mechanisms to\nprovide decentralized authorization for constrained IoT devices. The models\ninvolve different tradeoffs in terms of cost, delay, complexity, and privacy,\nwhile exploiting key advantages of smart contracts and multiple blockchains\nthat communicate with interledger mechanisms. These include immutably recording\nhashes of authorization information and policies in smart contracts, resilience\nthrough the execution of smart contract code on all blockchain nodes, and\ncryptographically linking transactions and IoT events recorded on different\nblockchains using hash and time-lock mechanisms. The proposed models are\nevaluated on the public Ethereum testnets Rinkeby and Ropsten, in terms of\nexecution cost (gas), delay, and reduction of data that needs to be sent to the\nconstrained IoT devices.\n
Koosha Mohammad Hossein, Mohammad Esmaeili, Tooska Dargahi, Ahmad Khonsari
Since the introduction of Internet of Things (IoT), e-health has become one of the main research topics. Due to the sensitivity of patient data, preserving the privacy of patients appears to be challenging. In healthcare applications, patient data are usually stored in the cloud, which makes it difficult for the users to have enough control over their data. However, due to the General Data Protection Regulation (GDPR), it is the data subject's right to know where and how his data has been stored, who can access his data and to what extent. In this paper, we propose a blockchain-based architecture for e-health applications which provides an efficient privacy-preserving access control mechanism. We take advantage of Blockchain (BC) special features, i.e., immutability and anonymity of users, while modifying the classic blockchain structure in order to overcome its challenges in IoT applications (i.e., low throughput, high overhead and latency). To this end, we cluster the miners of BC, store and process data at the nearest cluster to the patient. While our proposal is a work in progress, we provide a security analysis of our proposed architecture.
Liang Wang, Gilad Asharov, Rafael Pass, Thomas Ristenpart · 5 authors
We explore how to build a blind certificate authority (CA). Unlike conventional CAs, which learn the exact identity of those registering a public key, a blind CA can simultaneously validate an identity and provide a certificate binding a public key to it, without ever learning the identity. Blind CAs would therefore allow bootstrapping truly anonymous systems in which no party ever learns who participates. In this work we focus on constructing blind CAs that can bind an email address to a public key. To do so, we first introduce secure channel injection (SCI) protocols. These allow one party (in our setting, the blind CA) to insert a private message into another party's encrypted communications. We construct an efficient SCI protocol for communications delivered over TLS, and use it to realize anonymous proofs of account ownership for SMTP servers. Combined with a zero-knowledge certificate signing protocol, we build the first blind CA that allows Alice to obtain a X.509 certificate binding her email address alice@domain.com to a public key of her choosing without ever revealing ``alice'' to the CA. We show experimentally that our system works with standard email server implementations as well as Gmail.
In Blockchain networks involving multiple applications, the quality of service of an application is affected by the transaction ordering. We study a setting where each application is represented by a node, which might attempt to prioritize its own transactions through including them early in blocks added to the blockchain. A fair block proposal of a node follows a random selection of the transactions among the set of pending transactions the node is aware of. On the contrary, a dishonest node includes more of its transactions at the expense of transactions of other applications. In this work, we propose a toolbox of techniques to enforce such a fair block selection. First, we design an accurate statistical testing for the honesty of a proposal and explain it. Next, we describe a reputation system, documenting honesty of nodes to encourage fairness. Our last technique enforces fair block selection through concise commitments on the set of pending transactions known to a node.