Adoption of the Internet of Things (IoT) in education brings many benefits. However, the poor implementation of access control of educational data produced by the IoT devices has brought students’ and teachers’ privacy into danger. Attackers can access educational data that they are not permitted to access and even erase the records during access. To tackle this problem, we employ blockchain technology to guarantee the integrity of access control rules and trace the records of access events. In this paper, we propose a blockchain‐based access control scheme for the data produced by IoT devices. The scheme consists of three components: (1) a well‐implemented data collection module that is deployed in smart classrooms, which collects and uploads data about the real‐time situation inside the smart classroom to the data center; (2) a MongoDB‐based data center and its control module that makes access control decisions based on the verification of the permissions of visitors, where the permissions are managed by blockchain; and (3) a customized blockchain system that stores and keeps security policy updates of the role‐based access control module and records access events in a trusted way. Our analysis indicates that the proposed access control scheme guarantees the correctness of the access control process and makes the access of collected educational data auditable and responsible. Our system collectively analyzes the context of the smart classroom and is capable of detecting multiple scenarios such as absence, lateness, and gunshot. We show how the scheme preserves students’ and teachers’ privacy by carrying out extensive experimental studies. The results indicate that the proposed data management system can give correct responses as quickly as a traditional data server does while preserving privacy.
Xiaoge Huang, Xuesong Deng, Chengchao Liang, Weiwei Fan
To address the data security and user privacy issues in the task offloading process and resource allocation of the fog computing network, a blockchain‐enabled fog computing network task offloading model is proposed in this paper. Furthermore, to reduce the network utility which is defined as the total energy consumption of the fog computing network and the total delay of the blockchain network, a blockchain‐enabled fog computing network task offloading and resource allocation algorithm (TR‐BFCN) is proposed to jointly optimize the task offloading decision and resource allocation. Finally, the original nonconvex optimization problem is converted into two suboptimization problems, namely, task offloading decisions and computational resource allocations. Moreover, a two‐stage Stackelberg game model is designed to obtain the optimal amount of purchased resource and the optimal resource pricing. Simulation results show that the proposed TR‐BFCN algorithm can effectively reduce the network utility compared with other algorithms.
Data supervision is an effective method to ensure the legality of user data on blockchain. However, the massive growth of data makes it difficult to achieve data supervision in existing blockchain applications. Also, data supervision often leads to problems such as disclosure of transaction data and user privacy information. To address these issues, this paper proposes a privacy‐preserving blockchain supervision system (BSS) in the multiparty setting, where a supervision chain is introduced to realize data supervision on blockchain. All sensitive information such as user information in the supervising data is encrypted by the attribute‐based encryption (ABE) technology, so that both privacy protection and access control on user data can be achieved. Theoretical analysis and comparison show that the proposed BSS scheme is efficient, and experimental analysis indicates the practicality of our BSS scheme.
Auto electronic insurance policy and electronic maintenance list record the entire process of auto owners purchasing auto insurance and repairs after accident, respectively. They play a vital role in auto owners’ applications for claims and insurance company’s judgment on whether to settle the claims. However, the privacy of insurance policy and the “information island” resulting from the nonsharing of data between users make the claim has low efficiency. The notable features of blockchain technology are decentralization and tamper‐proof, which can well solve data sharing and privacy protection. This paper proposes a blockchain‐based auto insurance data sharing scheme to improve the existing auto insurance claim system. The scheme includes four main bodies: auto owner, insurance company, 4S Shop, and government authority. In the proposed scheme, the data sharing of authorized users is realized through proxy reencryption. Finally, we have analyzed the security and performance of the solution. The analysis results show that the proposed scheme can meet many security features such as user access control and data tamper resistance and has an ideal calculation and communication cost.
The maintenance and sharing of electronic medical records are one of the essential tasks in the medical treatment combination. Traditional cloud-based electronic medical record storage system is difficult to realize data security sharing. The tamper resistance and traceability of blockchain technology provide the possibility for the sharing of highly sensitive medical data. This paper proposes a safe sharing scheme of stroke electronic medical records based on the consortium blockchain. The scheme adopts the storage method of ciphertext of medical records stored in the cloud and index of medical records stored on the blockchain. The privacy protection mechanism proposed in this paper innovatively combines proxy reencryption and searchable encryption which supports patient pseudoidentity search. The mechanism could achieve controllable sharing of medical records and precise search. According to the organizational characteristics of the stroke medical treatment combination, this paper proposes an improved Practical Byzantine Fault Tolerance mechanism to reach a consensus between consensus nodes. Then, the proposed scheme is analyzed and evaluated from three aspects of medical record integrity, user privacy, and data security. The results show that the scheme can not only ensure the privacy of patient identity information and private key data but also resist the tampering and deletion attacks of internal and external malicious nodes on the medical record data. Therefore, the proposed scheme is conducive to the improvement of the timeliness of stroke treatment and the safe sharing of electronic medical records in stroke medical treatment combination.
In order to ensure the security of logistics information and to query information quickly and efficiently, using searchable encryption algorithms, combined with the characteristics of the blockchain, a searchable and encrypted logistics information blockchain data query algorithm is proposed. First, the logistics information is divided into multiple data files, encrypted with an asymmetric searchable encryption algorithm, and then stored in the cloud server. The keyword index value is extracted from each data file and uploaded to the blockchain. This solution can be used at any time Update and query data. Finally, analyze the correctness, completeness and safety of the scheme of this article, which proves the feasibility of this scheme.
Blockchain technologies have been benefiting many industries by being decentralized, secure and confidential. They offer great potential in pandemic impacted scenarios as well. Contact tracing helps to mitigate the transmission of disease by alerting people who may have been exposed so they can act on time to protect themselves. Contact tracing systems face some challenges related to issues of medical privacy, data security and transparency. Multiple researches show concern that contact tracing discourages people to seek medication because of the fear of loss of data, subsequent stigma, discrimination, or abuse. In this paper, we discuss how contact tracing can be improved using blockchain technology and could be able to solve these issues. The aim of our proposed system would be to reduce the impact of pandemic, to implement this merger of the blockchains and the Contact tracing app and to ensure user privacy and avoid data misuse by incorporating a symmetric key cryptographic mechanism. To tackle the scalability related issues of Blockchain this framework uses IPFS, a distributed file storage system also known as Interplanetary File System. This blockchain based solution system will enhance contact tracing by making it more stable, secure, performant, highly useable and above all effective in the fight against any pandemic.
Nowadays, sensors inserted in mobile applications are used for gathering data for an explicit assignment that can effectively save cost and time in crowd sensing networks (CSNs). The true value and essence of gathered statistics depend on the participation level from all the members of a CSN, i.e., service providers, data collectors, and service consumers. In comparison with the centralized conventional mechanisms that are susceptible to privacy invasion, attacks, and manipulation, this article proposes a decentralized incentive and reputation mechanism for CSN. The monetary rewards are used to motivate the data collectors and to encourage the participants to take part in the network activities. Whereas the issue of privacy leakage is dealt with using Advanced Encryption Standard (AES128) technique. Additionally, a reputation system is implemented to tackle issues like data integrity, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent, and reliable data. Furthermore, simulations are performed for analyzing the gas consumed by smart contracts. Similarly, the encryption technique is ratified by comparing its execution time with other techniques that are previously used in literature. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.
Public blockchains have reached high popularity among technically inclined people, laypersons and researchers alike. Similarly, privacy has gained much attention in the same circles. This attention and high sensitivity of information transmitted in blockchains, lead more and more blockchain-based systems, especially cryptocurrencies, to provide privacy for their users. Popular approaches include ring signatures or zero-knowledge proofs to achieve unlinkable payments within the blockchain. However, these systems solely examine privacy by considering the blockchain and its embedded transactions. The underlying peer-to-peer network of a public blockchain is rarely considered. This leaves the dissemination of transactions open for privacy attacks, as the IP address of the originator of a transaction can be mapped to their real-world identity. In this thesis, we look into the important privacy aspects of broadcasting blockchain transactions. We collect and analyse data of a large blockchain network and construct a privacy-preserving latency estimator. Building on the insights gained from the analysis, we combine dining-cryptographers networks with a secret sharing technique and layer a flood-and-prune broadcast on top, to provide enforced k-anonymity to network participants. To increase the flexibility of this approach, we extend two established privacy protocols. First, we extend a dining-cryptographers based group messaging protocol to transmit arbitrary length messages. Further, we optimize the protocol for common cases, to improve its performance for various environments, especially blockchain transaction dissemination. As a performant intermediate privacy layer, we transform adaptive diffusion from a contact graph protocol to a computer network protocol. We achieve this by changing the underlying network assumptions and the attacker model. We derive optimal forwarding probabilities based on a statistical network model of unstructured peer-to-peer networks. These two sub-protocols are combined in an intertwined layering approach to create 3P3, a flexible privacy-preserving broadcast protocol. Lastly, to manage the groups required for 3P3 and other proposed and common protocols, we propose Pixy. Pixy is a privacy increasing group creation scheme, allowing for filtering and testing of group participants to establish trust. The system allows for smaller group sizes while maintaining privacy guarantees of previous systems, or better privacy for same-sized groups. The software, concepts, data and models in this thesis help researchers and developers of privacy preserving network protocols. Developers can use 3P3, tuning its parameters to the needs of their network. Researchers can build on the data, concepts and models to create novel schemes and generalizations of our insights. This improves privacy for all users of modern and future networks.
Umer Majeed, Latif U. Khan, Abdullah Yousafzai, Zhu Han · 6 authors
Federated Learning (FL) relies on on-device training to avoid the migration of devices’ data to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios (e.g., autonomous cars) where an enormous amount of data is generated every day. Transferring only local model updates in the case of FL is highly communication-efficient compared to transferring all data in the case of centralized machine learning (ML). Although FL offers many advantages, it also has some challenges. A malicious aggregation server can infer device information via local model updates. Another downside of FL is the centralized aggregation server that can malfunction due to an attack or physical damage. To address these issues, we propose a novel Structured Transparency empowered cross-silo Federated Learning on the Blockchain (ST-BFL) framework. In ST-BFL, homomorphic encryption, FL-aggregators, FL-verifiers, and smart contract are employed, which satisfy various structured transparency components, such as input privacy, output privacy, output verification, and flow governance. We present the framework architecture, algorithms, and sequence diagram of our ST-BFL framework to show how different entities interact in ST-BFL for the FL process. We also present a simplified class diagram of ST-BFL’s smart contract for an FL task. Finally, we perform a simulation to analyze our framework from the perspective of aggregation time, accuracy, and storage size. The qualitative and quantitative evaluation shows that ST-BFL has the same accuracy as traditional FL. However, ST-BFL provides input privacy, output privacy, input verification, output verification, and flow governance at the expense of relatively higher computation and communication costs than traditional FL.
Jiansheng Zhang, Yang Xin, Gao Yu-long, Xiaohui Lei · 5 authors
Internet of Things (IoT) has been widely used in various fields of our daily life in the past years. Obviously, in the future, the significance of its security will be more evident and enormous. However, there are still many security issues in the IoT, which makes it vulnerable to be attacked easily by some potential factors. Targeting at these issue, in this paper, we propose a secure access management scheme in IoT based on blockchain. Firstly, in our work, by using the Decisional Learning with Error (DLWE) problem, we propose a secure Key-policy Attribute-based Encryption (KP-ABE) scheme, it can implement fine-grained access control in IoT. Secondly, we use blockchain to solve the authentication and access management challenges. In addition, the smart contract in blockchain can process transactions automatically, which greatly reduces the management cost and running time for this scheme. Last but not the least, we further analyze the security of our proposed scheme. The result shows that our scheme can resist some common attacks in IoT, and it is more secure and efficient.
Since digital forensics becomes more and more popular, more and more attention has been paid to the originality and validity of data, and data preservation technology emerges as the times require. However, the current data preservation models and technologies are only the combination of cryptography technology, and there is a risk of being attacked and cracked. And in the process of data preservation, human participation is also needed, which may lead to data tampering. To solve problems given, this paper presents a data preservation model based on blockchain and multidimensional hash. With the decentralization and smart contract characteristics of blockchain, data can be automatically preserved without human participation to form a branch chain of custody in the unit of case, and blockchain has good antiattack performance, which is the so‐called 51% attack. Meanwhile, in order to solve the problem of data confusion and hard to query caused by the excessive number of cases, hash, cryptography, and timestamps are used to form a serialized main chain of custody. Because of the confliction problem of hash and judicial trial needs to absolutely guarantee the authenticity and validity of data, multidimensional hash is used to replace regular hash. In this way, the data preservation becomes an automatic, nonhuman‐interventional process. Experiments have been carried out to show the security and effectiveness of the proposed model.
Federated learning (FL) permits a vast number of connected to construct deep learning models while keeping their private training data on the device. Rather than uploading the training data and model to the server, FL only sends the local gradients gradually. Hence, FL preserves data privacy by design. FL leverages a decentralised approach where the training data is no longer concentrated. Similarly, blockchain uses the same approach by providing a digital ledger that can cover the flaws in the centralised system. Motivated by the merits of a decentralised approach, we construct a collaborative model of simultaneous distributed learning by employing multiple computing devices over shared memory with blockchain smart contracts as a secure incentive mechanism. The collaborative model preserves a value-driven of distributed learning in enhancing users' privacy. It is supported by blockchain with a secure decentralised incentive technique without having a single point of failure. Furthermore, potential vulnerabilities and plausible defences are also outlined. The experimental results positively recommend that the collaborative model satisfies the design goals.
Medical files can help people prevent diseases, increase cure rates, promote medical development and help solve major public health crises. However, medical files are strongly private. It is an urgent problem needed to be solved that how to share medical files with privacy and data security. The existing models based on the centralized certificate authority is a feasible method, but it is possible to experience a single point failure. Besides there is a mismatch between the models and real-life scenario since they are only suitable for single patient. Therefore, this paper proposes a practical medical file sharing scheme based on blockchain and decentralized attribute-based encryption. The blockchain is used to record application and grant of authorizations. Smart contracts provide an interactive platform for all users in the system. By utilizing decentralized attribute-based encryption, fine-grained access control of medical files is carried out to ensure privacy and security as well as avoiding single point failure. Attribute-based algorithm that support multi-person democratic decision making and dynamic personnel changes are designed to make the model much closer to the real scene. Finally, through security, performance and comparative analysis with other solutions, the scheme in this paper can meet the needs of real-life scenarios in terms of security and practicability, and provides a new practical model for medical file sharing.
Cellular networks connect nearly every human on the planet; they consequently have visibility into location data and voice, SMS, and data contacts and communications. Such near-universal visibility represents a significant threat to the privacy of mobile subscribers. In 5G networks, end-user mobile device manufacturers assign a Permanent Equipment Identifier (PEI) to every new device. Mobile operators legitimately use the PEI to blocklist stolen devices from the network to discourage device theft, but the static PEI also provides a mechanism to uniquely identify and track subscribers. Advertisers and data brokers have also historically abused the PEI for data fusion of location and analytics data, including private data sold by cellular providers. In this paper, we present a protocol that allows mobile devices to prove that they are not in the blocklist without revealing their PEI to any entity on the network. Thus, we maintain the primary purpose of the PEI while preventing potential privacy violations. We describe a provably-secure anonymous proof of blocklist non-membership for cellular network, based on the RSA accumulators and zero-knowledge proofs introduced by Camenisch and Lysyanskaya (Crypto'02) and expanded upon by Li, Li and Xue (ACNS'07). We show experimentally that this approach is viable for cellular networks: a phone can create a blocklist non-membership proof in only 3432 milliseconds of online computation, and the network can verify the proof in less than one second on average. In total this adds fewer than 4.5 seconds to the rare network attach process. This work shows that PEIs can be attested anonymously in 5G and future network generations, and it paves the way for additional advances toward a cellular network with guaranteed privacy.
A feasible approach commonly discussed in the literature for mitigating location privacy threats in vehicular ad hoc networks (VANETs) is the use of pseudonyms instead of real vehicle identifications. However, for relevant authorities to be able to identify misbehaving vehicles through their pseudonyms, it is essential that the privacy protection mechanisms only allow for conditional anonymity and not complete anonymity. In this paper, we propose the use of a permissioned consortium blockchain system with smart contract feature to facilitate secure and conditional privacy-preserving vehicular pseudonym issuance and management in a multi-jurisdictional road network. The use of a permissioned consortium blockchain helps mitigate security risks associated with the complexities in interorganizational data handling, such as in the areas of access control, data integrity, confidentiality, and availability. The proposed system architecture takes advantage of the predicted wide availability of Roadside Units (RSUs), and the highly viable, flexible and mature Public Key Infrastructure (PKI) technology for usage in vehicular pseudonymous communications. We successfully carried out a small-scale simulation of the proposed architecture using the Vehicles in Network Simulation (Veins) platform for integrated traffic and network simulation services (SUMO as the traffic simulator and OMNeT++ as the network simulator), and the Hyperledger Fabric platform as the permissioned consortium blockchain system. Simulation and performance analysis results reveal the feasibility of practical deployment of the scheme, and show that the scheme addresses the identified shortfalls of existing works, including the ability to achieve a better balance between connectivity and storage requirements.