Hanlin Zhang, Peng Gao, Jia Yu, Jie Lin · 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, Tien‐Hsiung Weng · 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.
Data trading is ubiquitous in today's Internet. User data is often sensitive in nature, for example, medical records and sensitive health related information. Present data trade often does not capture the owner's consent or allow tracking of the sale of her data. Even during a sale, fairness is not guaranteed and both the data owner and the data buyer remains at risk of being cheated. In this paper, we propose an end-to-end protocol to trade sensitive information addressing the key issues of consent, traceability and fairness. We use blockchain as the arbitrator and tamper-resistant log. We implement a prototype of our protocol on Ethereum and show that our proposed solution is practical.
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.
Bitcoin provides pseudo-anonymity to its users, leading to many transactions related to illicit activities. The advent of mixing services like OnionBC, Bitcoin Fog, and Blockchain.info has allowed users to increase their anonymity further. This paper tackles the pseudo-anonymity of the Bitcoin blockchain by developing a scalable spark based framework to find patterns in the transaction data. The efficacy of the framework is demonstrated by performing exploratory analysis. Furthermore, the paper shows the capabilities of bitcoin-based graph representations and addresses the issue of user profiling based on unsupervised learning approaches for analysing Bitcoin transactions and users. The authors convert the transaction graph of the Bitcoin data to contain only Wallet-IDs and generate graph embeddings using Variational Graph Autoencoder [1]. Additionally, the authors use explainable-AI techniques and Kohonen self organizing maps to visualize and understand the results obtained from the unsupervised learning methods.
Industrial Internet of Things (IIoT) is ushering in huge development opportunities in the era of Industry 4.0. However, there are significant data security and privacy challenges during automatic and real-time data collection, monitoring for industrial applications in IIoT. Data security and privacy in IIoT applications are closely related to the reliability of users, which is determined by user authentication that have been widely used as an effective approach. However, the existing user authentication mechanisms in IIoT suffer from single factor authentication and poor adaptability with the rapid growth of the number of users and the diversity of user categories. To solve the aforementioned issues, this article proposes a novel Authentication mechanism based on Transfer Learning empowered Blockchain, coined ATLB. In ATLB, blockchains are applied to achieve the privacy preservation for industrial applications. In addition, by introducing the transfer learning based authentication mechanism, trustworthy blockchains are built such that the privacy preservation for industrial applications is further enhanced. Specifically, ATLB first employs a guiding deep deterministic policy gradient algorithm to train the user authentication model of a specific region, which is then transferred locally for foreign user authentication or cross-regionally for another region's user authentication such that the model training time is significantly reduced. Experimental results show that the proposed ATLB not only provides accurate authentications for IIoT applications but also achieves high throughput and low latency.
With the development of ICT technology, users are actively participated in content creation and sharing. Service providers have provided more diverse and extensive information services. With the recent evolution to a customized service, the demand for the use of personal information is increasing. However, it affects the efficiency and convenience of service provision, as social problems and security threats such as personal information leakage, human trafficking, and misuse increase. In this article, we proposed an intelligent mediator-based enhanced smart contract for privacy protection. The proposed approach performs the tasks, which are mediation transactions, authorization, and transaction record management, with blockchain-based personal information management. Then it is possible to prevent misuse of personal information and to support rational decision-making on the transparency of information and the use of personal information by autonomously performing personal information management.
Chao Lin, Min Luo, Xinyi Huang, Kim‐Kwang Raymond Choo · 5 authors
Credit system is generally associated with the banking and financial institutions, although it has far reaching implications for residents of countries, such as U.S., particularly for those with a poor credit history. Specifically, a credit score computation (CSC) quantifies an individual’s credit value or credit risk, which is used by banking and financial institutions, as well as other entities (e.g., during purchasing of insurance policies and application of rental properties), to facilitate their decision-making (e.g., whether to approve the insurance policy purchase or the level of premium). Although a number of CSC models have been proposed in the literature for supporting different application scenarios, privacy protection of CSC is rarely considered despite the potential for leakage of user private information (e.g., registration, hobbies, credit, relationships, and inquiry). Such information can then be abused for other nefarious activities, such as identity theft and credit card fraud. Thus, in this article, we first analyze the privacy strength of existing CSC models, prior to presenting the formal definition of a privacy-preserving CSC system alongside its security requirements. Then, we propose a concrete construction based on Paillier encryption with three proposed noninteractive zero-knowledge schemes. To demonstrate feasibility of our proposal, we evaluate both its security and performance.
The industrial Internet of Things (IIoT) supports recent developments in data management and information services, as well as services for smart factories. Nowadays, many mature IIoT cloud platforms are available to serve smart factories. However, due to the semicredibility nature of the IIoT cloud platforms, how to achieve secure storage, access control, information update and deletion for smart factory data, as well as the tracking and revocation of malicious users has become an urgent problem. To solve these problems, in this article, a blockchain-enhanced security access control scheme that supports traceability and revocability has been proposed in IIoT for smart factories. The blockchain first performs unified identity authentication, and stores all public keys, user attribute sets, and revocation list. The system administrator then generates system parameters and issues private keys to users. The domain administrator is responsible for formulating domain security and privacy-protection policies, and performing encryption operations. If the attributes meet the access policies and the user's ID is not in the revocation list, they can obtain the intermediate decryption parameters from the edge/cloud servers. Malicious users can be tracked and revoked during all stages if needed, which ensures the system security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption and can resist multiple attacks. The evaluation has shown that the size of the public/private keys is smaller compared to other schemes, and the overhead time is less for public key generation, data encryption, and data decryption stages.
A. F. M. Suaib Akhter, Mohiuddin Ahmed, A. F. M. Shahen Shah, Adnan Anwar · 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 term “digital signature” refers to electronic information that is used to identify signatories and indicate that they have signed a document; such information is either attached to or logically combined with a specific electronic document. However, digital signatures entail privacy infringements because it is possible to verify such signatures only when all the data are disclosed to the verifier. Zero-knowledge proofs are considered to be capable of solving this privacy problem. In general, a zero-knowledge proof can be established even if the prover hides the information required in the verification process from the verifier. Zero-knowledge succinct non-interactive argument of knowledge (ZK-SNARK), a prevalent zero-knowledge proof, has been optimized to generate non-interactive and succinct proofs; nevertheless, the generation of proofs is excessively time consuming, making the application of ZK-SNARK impractical in most scenarios. In this paper, we show that existing cryptographic algorithms, such as a one-way hash function or digital signature, can be combined with a zero knowledge proof. Particularly, we propose the multi-argument zero-knowledge argument (MAZKA) algorithm, which can verify data by exposing only the part to be verified and also verify that the part of data has not been manipulated compared to the original. In addition, the proposed algorithm satisfies the adaptive proof of knowledge, perfect zero-knowledze and combinatorial succinctness conditions.
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 contrast to the traditional cloud-based IoT structure, which imposes high computation and storage demands on the central cloud server, edge computing can process data at the network edge. In this paper, we present an edge computing data storage protocol employing the blockchain and the group signature, where the blockchain works as the trusted third party, offering a convenient platform for the data storage and protection, and the group signature is utilized for privacy identity information protection. Unlike the common group signature scheme, we propose a notion of \emph{simplified group signature}, which removes the traceable property to improve the computational efficiency. In edge computing, the edge devices and the end devices can easily set up a long-term trust relationship through the cloud services, so the traceability may be of little utility in this scenario. In our protocol, we also design a new data validation scheme that has a proof size of only$O(1)$.
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.