Nowadays the whole world is immersed in data. Starting from creating the data, using the data, sharing the data, everyone has control of their respective data. Even the companies rely highly on data as all of their storage and analysis have been computerized. In most traditional methods, the database which is being deployed is deemed trustworthy. But the threat always persists no matter how secure the database is. Attackers perform various types of attacks possible to gain access, tamper or perform any kind of compromising action on the database which will hamper the working of a company/individual.So, the goal is to achieve security against those attacks and additionally avoiding users to obtain information which they are not authorized to.However, there exists a series of relational data such as medical databases where there are two or more parties involved in a database and the trust factor automatically takes a hit. In this paper, I propose a model, that provides authorization, confidentiality, accessibility and privacy for a healthcare database. \n\n
Hang Chen, Syed Ali Asif, Jihong Park, Chien-Chung Shen Ā· 5 authors
Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data. However, the centralized architecture of FL is vulnerable to the single point of failure. In addition, FL does not examine the legitimacy of local models, so even a small fraction of malicious devices can disrupt global training. To resolve these robustness issues of FL, in this paper, we propose a blockchain-based decentralized FL framework, termed VBFL, by exploiting two mechanisms in a blockchained architecture. First, we introduced a novel decentralized validation mechanism such that the legitimacy of local model updates is examined by individual validators. Second, we designed a dedicated proof-of-stake consensus mechanism where stake is more frequently rewarded to honest devices, which protects the legitimate local model updates by increasing their chances of dictating the blocks appended to the blockchain. Together, these solutions promote more federation within legitimate devices, enabling robust FL. Our emulation results of the MNIST classification corroborate that with 15% of malicious devices, VBFL achieves 87% accuracy, which is 7.4x higher than Vanilla FL.
Vehicular social networks (VSNs) are the vehicular ad hoc networks (VANETs) that integrate social networks. Compared with traditional VANETs, VSNs are more suitable to serve a group of vehicles with common interests. In VSNs, vehicles can upload the necessary data in the cloud service provider (CSP) and other vehicles can query the data they are interested in through CSP, which enables VSNs to provide more user-friendly services. However, due to the wireless network communication environment, the data sent by the vehicle can easily be monitored. Adversaries are able to violate the privacy of the vehicle based on the collected data, thereby threatening the security of the entire network. In addition, if a vehicle shares malicious or false data with other vehicles, it is easy to mislead drivers and even cause serious traffic accidents. This paper proposes an effective data sharing scheme based on blockchain in VSNs. By integrating an identity based signature mechanism and pseudonym generation mechanism, we first propose an anonymous authentication mechanism as the basis for establishing trust relationships before data transmission between entities in VSNs. Then, a data sharing scheme based on blockchain is described, in which the signature mechanism and the consensus mechanism guarantee the security and traceability of data. The result of the performance analysis and the simulation experiment indicate that VAB can achieve a favourable performance compared with existing schemes.
Hanlin Zhang, Peng Gao, Jia Yu, Jie Lin Ā· 5 authors
Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.
Shiwen Zhang, Tingting Yao, Voundi Koe Arthur Sandor, TienāHsiung Weng Ā· 6 authors
Online social networks (OSNs) are nowadays an important field of applications thanks to the recent surge in online interaction. However, the illegal disclosure of user's private data can cause damaging consequences and even threaten the safety of users' life. The privacy issues of OSNs have become a matter of great concern for many people. In recent years, there are some research works to address this privacy issue, yet they do not always focus on providing the normal social network services for users, such as data sharing, data retrieval and data access services. Therefore, it is a challenge to ensure the security of sensitive data while providing efficient and privacy-preserving social network services for users. In this paper, we propose a novel blockchain-based privacy-preserving framework for online social networks, called BPP. Combined blockchain and public-key cryptography technique, the BPP framework can achieve secure data sharing, data retrieving, and data accessing with fairness and without worrying about potential damage to users' interest. Specifically, based on blockchain and public key encryption with keyword search technique, a secure, fair and efficient keyword search algorithm is proposed, with which the BBP framework realises privacy preservation of user's query and then obtain accurate query results with assurance and without needing for any further verification operation in online social network. Finally, we implement a prototype of our framework and deploy it to a locally simulated network. The extensive experiments and security analysis demonstrate the security, efficacy and efficiency of our proposed framework.
Sheikh Mohammad Idrees, Mariusz Nowostawski, Roshan Jameel
The COVID-19 pandemic has caused substantial global disturbance by affecting more than 42 million people (as of the end of October 2020). Since there is no medication or vaccine available, the only way to combat it is to minimize transmission. Digital contact tracing is an effective technique that can be utilized for this purpose, as it eliminates the manual contact tracing process and could help in identifying and isolating affected people. However, users are reluctant to share their location and contact details due to concerns related to the privacy and security of their personal information, which affects its implementation and extensive adoption. Blockchain technology has been applied in various domains and has been proven to be an effective approach for handling data transactions securely, which makes it an ideal choice for digital contact tracing apps. The properties of blockchain such as time stamping and immutability of data may facilitate the retrieval of accurate information on the trail of the virus in a transparent manner, while data encryption assures the integrity of the information being provided. Furthermore, the anonymity of the user's identity alleviates some of the risks related to privacy and confidentiality concerns. In this paper, we provide readers with a detailed discussion on the digital contact tracing mechanism and outline the apps developed so far to combat the COVID-19 pandemic. Moreover, we present the possible risks, issues, and challenges associated with the available contact tracing apps and analyze how the adoption of a blockchain-based decentralized network for handling the app could provide users with privacy-preserving contact tracing without compromising performance and efficiency.
A. F. M. Suaib Akhter, Mohiuddin Ahmed, A. F. M. Shahen Shah, Adnan Anwar Ā· 5 authors
Existing research shows that Cluster-based Medium Access Control (CB-MAC) protocols perform well in controlling and managing Vehicular Ad hoc Network (VANET), but requires ensuring improved security and privacy preserving authentication mechanism. To this end, we propose a multi-level blockchain-based privacy-preserving authentication protocol. The paper thoroughly explains the formation of the authentication centers, vehicles registration, and key generation processes. In the proposed architecture, a global authentication center (GAC) is responsible for storing all vehicle information, while Local Authentication Center (LAC) maintains a blockchain to enable quick handover between internal clusters of vehicle. We also propose a modified control packet format of IEEE 802.11 standards to remove the shortcomings of the traditional MAC protocols. Moreover, cluster formation, membership and cluster-head selection, and merging and leaving processes are implemented while considering the safety and non-safety message transmission to increase the performance. All blockchain communication is performed using high speed 5G internet while encrypted information is transmitted while using the RSA-1024 digital signature algorithm for improved security, integrity, and confidentiality. Our proof-of-concept implements the authentication schema while considering multiple virtual machines. With detailed experiments, we show that the proposed method is more efficient in terms of time and storage when compared to the existing methods. Besides, numerical analysis shows that the proposed transmission protocols outperform traditional MAC and benchmark methods in terms of throughput, delay, and packet dropping rate.
The principle of data protection by design, as it is enshrined in article 25 of the GDPR, is difficult to apply in blockchains. This article will assess how the reliance on asymmetric encryption and other privacy enhancing technological architectures -necessary in a blockchainbased system-approach both user control and data protection by design compliance from the single scope of anonymization and unlinkability. Data subjects' rights, accountability, and the potential shortcomings of applied technological constraints are thus sidelined. Ultimately, this limited understanding of technological privacy, acts as a misguiding set of principles for technological co-regulation through standardisation in blockchains. The standardization of these choices without a holistic analysis of data protection by design imperatives could ultimately weaken the position of data subjects, whose trust in the technological protections of personal data might prove to be relatively misplaced.
In recent years, with many devices continuously joining the Internet of Things (IoT), data sharing as the main driver of the IoT market has become a research hotspot. However, the users are reluctant to participate in data sharing due to security concerns and lacking incentive mechanisms in the current IoT. In this context, blockchain is introduced into the data sharing of IoT to solve the trust problem of users and provide secure data storage. However, in the exploration of building a secure distributed data sharing system based on the blockchain, how to break the inherent performance bottleneck of blockchain is still a major challenge. For this reason, the efficient blockchain-based data sharing incentive scheme is studied for IoT. In the scheme, an efficient data sharing incentive framework based on blockchain is proposed, named ShareBC. Firstly, ShareBC uses sharding technology to build asynchronous consensus zones that can process data sharing transactions in parallel and deploy efficient consensus mechanisms on the cloud/edge servers and asynchronous consensus zones in sharding, thus improving the processing efficiency of data sharing transactions. Then, a sharing incentive mechanism based on a hierarchical data auction model implemented by a smart contract is presentedto encourage IoT users to participate in data sharing. The proposed mechanism can solve the problem of multi-layer data allocation involved in IoT data sharing and maximize the overall social welfare. Finally, the experimental results show that the proposed scheme is economically efficient, incentive-compatible, and real-time, with scalability, low cost, and good practicability.
In the existing Electronic Health Records (EHRs), the medical information of patients is completely controlled by various medical institutions. As such, patients have no dominant power over their own EHRs. These personal data are not only inconvenient to access and share, but are also prone to cause privacy disclosure. The blockchain technology provides a new development direction in the medical field. Blockchain-based EHRs are characterized by decentralization, openness and non-tampering of records, which enable patients to better manage their own EHRs. In order to better protect the privacy of patients, only designated receivers can access EHRs, and receivers can authenticate the sharer to ensure that the EHRs are real and effective. In this study, we propose an identity-based signcryption scheme with multiple authorities for multiple receivers, which can resist N-1 collusion attacks among N authorities. In addition, the identity information of receivers is anonymous, so the relationship between them and the sharer is not disclosed. Under the random oracle model, it was proved that our scheme was secure and met the unforgeability and confidentiality requirements of signcryption. Moreover, we evaluated the performance of the scheme and found that it had the moderate signcryption efficiency and excellent signcryption attributes.
With the rapid development of cloud computing, a large number of web services have been emerging quickly, which brings a heavy burden for users to choose the services they preferred. In order to suggest web services for users, recommendation algorithms are needed and many of them have been investigated recently. However, most of the existing recommendation schemes are based on centralized historical data, which may lead to single point of failure. Generally, the data contains a lot of sensitive information that cloud may expose the privacy of users, which makes most cloud platforms reluctant to share their own data. In order to solve the above issues, the secure data sharing among cloud platforms is necessary for better recommendation, which can maximize the profits. In this paper, we propose a blockchain-assisted collaborative service recommendation scheme ($BC - SRDS$). Specifically, we adopt the ciphertext-policy attribute-based encryption (CP-ABE) algorithm to encrypt the data, which ensures the data confidentiality and realizes secure data sharing. Then, we utilize the blockchain to share data, such that the DoS attack, DDoS attack and single point of failure can be avoided. Meanwhile, the data integrity, tampering-proof of data are guaranteed through the blockchain. And we use locality-sensitive hashing algorithm to recommend the services for users. Finally, it is proved through the security analysis that$BC - SRDS$is capable of achieving data confidentiality, data integrity and tampering-proof. A series of experiments show that$BC - SRDS$achieves better recommendation accuracy compared with the existing schemes.
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 studentsā and teachersā 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 blockchainābased access control scheme for the data produced by IoT devices. The scheme consists of three components: (1) a wellāimplemented data collection module that is deployed in smart classrooms, which collects and uploads data about the realātime situation inside the smart classroom to the data center; (2) a MongoDBābased 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 roleābased 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 studentsā and teachersā 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 blockchaināenabled 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 blockchaināenabled fog computing network task offloading and resource allocation algorithm (TRāBFCN) 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 twoāstage Stackelberg game model is designed to obtain the optimal amount of purchased resource and the optimal resource pricing. Simulation results show that the proposed TRāBFCN 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 privacyāpreserving 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 attributeābased 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 ownersā applications for claims and insurance companyās judgment on whether to settle the claims. However, the privacy of insurance policy and the āinformation islandā resulting from the nonsharing of data between users make the claim has low efficiency. The notable features of blockchain technology are decentralization and tamperāproof, which can well solve data sharing and privacy protection. This paper proposes a blockchainābased 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 Ā· 6 authors
Federated Learning (FL) relies on on-device training to avoid the migration of devicesā 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-BFLās 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.