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The wealth of user data acts as a fuel for network intelligence toward the sixth generation wireless networks (6G). Due to data heterogeneity and dynamics, decentralized data management (DM) is desirable for achieving transparent data operations across network domains, and blockchain can be a promising solution. However, the increasing data volume and stringent data privacy-preservation requirements in 6G bring significantly technical challenge to balance transparency, efficiency, and privacy requirements in decentralized blockchain-based DM. In this paper, we propose blockchain solutions to address the challenge. First, we explore the consensus protocols and scalability mechanisms in blockchains and discuss the roles of DM stakeholders in blockchain architectures. Second, we investigate the authentication and authorization requirements for DM stakeholders. Third, we categorize DM privacy requirements and study blockchain-based mechanisms for collaborative data processing. Subsequently, we present research issues and potential solutions for blockchain-based DM toward 6G from these three perspectives. Finally, we conclude this paper and discuss future research directions.

Next-generation wireless networks (NGWN) aim to support diversified smart applications that require frequent data exchanges and collaborative data processing among multiple stakeholders. Data management (DM), including data collection, storage, sharing, and computation, plays an essential role in empowering NGWN. However, DM for NGWN faces two significant challenges: stakeholders' data cannot be easily managed across different trust domains under a distributed network architecture; and privacy preservation requirements of personal data become more rigorous under new privacy regulations. To explore possible solutions to address the challenges, we first investigate the state-of-the-art architecture designs for DM and emphasize advantages of a blockchain-based DM architecture. Then we summarize existing privacy-preserving techniques in terms of advantages and challenges when being applied to DM. In addition, we review recent privacy regulations with their impacts on DM and discuss the existing solutions with privacy regulation compliance based on blockchain. Finally, we identify further research directions for achieving DM with privacy preservation.

Network provenance is essential for Internet-of-Things (IoT) network administrators to conduct the network diagnostics and identify root causes of network errors. However, the distributed nature of the IoT network results in the management of the provenance data at different trust domains, which poses concerns on the security and trustworthiness of the cross-domain network diagnostics. In this article, we propose a blockchain-based architecture for secure and efficient distributed network provenance (SEDNP) in the IoT. Instead of directly storing and querying the whole provenance data on the blockchain with prohibitive implementation cost, we introduce a unified provenance query model and develop a provenance digest strategy that: 1) enables compact (constant size) on-blockchain digests of provenance data and a multilevel index regardless of provenance data volume and 2) ensures the correctness and integrity of provenance query results through the verification of the on-blockchain digests. We formally define the security requirements as Archiving Security along with thorough security analysis. Moreover, we conduct extensive experiments with the integration of a verifiable computation (VC) framework and a blockchain testing network. The experimental results are provided as performance benchmarks to demonstrate the application feasibility of SEDNP.

Industrial mortgage enables companies to acquire loan for business venture or investment purposes by pledging their industrial assets to financial institutions. To prevent double-mortgage fraud of borrowers, information exchange among different financial institutions is necessary. On the other hand, it results in the privacy leakage of borrowers. In this article, we construct a blockchain-based accountable and privacy-preserving industrial mortgage scheme (BAPIM). BAPIM enables financial institutions to share the mortgage data of borrowers in an efficient and secure manner, that achieves the borrower identity privacy and accountability at the same time. Specifically, borrower identity is concealed on the blockchain by anonymous identity credential, while financial institutions can still uncover the identity of a misbehaving borrower if he pledges the same asset for multiple mortgages. We demonstrate that BAPIM achieves the desirable security properties and has high computational efficiency, so as to be suitable for the industrial mortgage management.

Industrial Internet of Things (IIoT) is revolutionizing the retail industry for manufacturers, suppliers, and retailers to improve operational efficiency and consumer experience. In IIoT-enabled retail marketing, reputation systems play a critical role to boost mutual trust among industrial entities and build consumer confidence. In this paper, we focus on reputation management in the consumer–retailer channel, where retailers can accumulate reputations from consumer feedbacks. To encourage consumers to post feedbacks without worrying about being tracked or retaliated, we propose an anonymous reputation system that preserves consumer identities and individual review confidentialities. To increase system transparency and reliability, we further exploit the tamper-proof nature and the distributed consensus mechanism of the blockchain technology. With system designs based on various cryptographic primitives and a Proof-of-Stake consensus protocol, our blockchain-based reputation system is more efficient to offer high levels of privacy guarantees compared with existing ones. Finally, we explore the implementation challenges of the blockchain-based architecture and present a proof-of-concept prototype system by Parity Ethereum. We measure the on/off -chain performance with the scalability discussion to demonstrate the feasibility of the proposed system.

The Zaozigou gold deposit lies in the West Qinling orogenic belt, Gansu Province, China. It is one of the largest gold deposits, and the orebodies are hosted in fine-grained slates intercalated with limestone of the Middle-Triassic Gulangdi Formation and varied dykes. The gold orebodies are strictly controlled by the NE-, NW-, and SN-trending tensional and shearing faults with high dipping angle. The mineralogy and geochemistry of pyrite and arsenopyrite are measured by electron microprobe. Pyrite has up to 0.12 wt.% Au, and arsenopyrite contains up to 0.17 wt.% Au. The antithetic correlation between S and As indicates the substitution of As for S in pyrite, and arsenic occurs in anionic As1- state in the pyrite structure under the reduced conditions. Pyrite has relatively high Co (similar to 364-2248 ppm) but relatively low Ni (similar to 109-497 ppm) contents, with Co/Ni ratios ranging from similar to 1.63 to 10.50, indicating that the deposit originated from a volcanogenic fluid and remobilized by hydrothermal fluid. Au in arsenopyrite occurs as cationic Au in solid solution, whereas Au in pyrite is in solid solution and metal nanoparticles (Au-0). The texture characteristics and trace element geochemistry among cores, transition zones, and rims of pyrites demonstrate that there are at least four pulses of fluid participating in the generation of pyrite in the deposit. The calculated formation temperatures of the Zaozigou deposit vary from 148 degrees C to 304 degrees C, with an average temperature of 213 degrees C based on Au contents in pyrite. The Pb isotopic compositions of pyrite samples suggest that the metallogenic materials of the Zaozigou deposit were derived from the mantle and upper crust. All the characteristics above lead us to draw the conclusion that the Zaozigou gold deposit is classified as an epithermal deposit.

With the proliferation of cloud services, cloud-based image retrieval services enable large-scale image outsourcing and ubiquitous image searching. While enjoying the benefits of the cloud-based image retrieval services, critical privacy concerns may arise in such services since they may contain sensitive personal information. In this paper, we propose an efficient and Privacy-Preserving Image Retrieval scheme with Key Switching Technique (PPIRS). PPIRS utilizes the inner product encryption for measuring Euclidean distances between image feature vectors and query vectors in a privacy-preserving manner. Due to the high dimension of the image feature vectors and the large scale of the image databases, traditional secure Euclidean distance comparison methods provide insufficient search efficiency. To prune the search space of image retrieval, PPIRS tailors key switching technique (KST) for reducing the dimension of the encrypted image feature vectors and further achieves low communication overhead. Meanwhile, by introducing locality sensitive hashing (LSH), PPIRS builds efficient searchable indexes for image retrieval by organizing similar images into a bucket. Security analysis shows that the privacy of both outsourced images and queries are guaranteed. Extensive experiments on a real-world dataset demonstrate that PPIRS achieves efficient image retrieval in terms of computational cost.

Benefiting from the development of network and mobile communication technologies, crowd sensing systems have emerged as new technology to sense and collect data via mobile devices. However, aggregated results of the collected data may not be accurate since information provided by devices may not be reliable. To tackle this problem, approaches based on truth discovery have been proposed to improve accuracy of data aggregation operations in crowd sensing systems. Nevertheless, it should be noted that most existing truth discovery proposals failed to consider user's data privacy. In this paper, we propose an Efficient and Privacy-preserving Truth Discovery (EPTD) scheme in crowd sensing systems. Specifically, we utilize the additive homomorphic privacy-preserving data aggregation and super-increasing sequence techniques to achieve both high performance and strong privacy protection. Security analysis indicates that the EPTD can achieve confidentiality of observed values and privacy protection of users' weights. Furthermore, extensive experiments demonstrate that the EPTD is better than existing proposals in terms of communication and computation overhead.

The metal–nonmetal transition is of fundamental interest in contemporary physics. We present a combination of the optical reflectivity measurement of an expanded gold fluid produced by gold L-band x-rays from a Hohlraum and the corresponding ab initio calculations to study the metal–nonmetal transition. The averaged density and temperature of the reflective layer are derived from 1D radiation hydrodynamic simulations constrained by matching the expansion velocity obtained from the fringe shift. A sharp change in time-resolved reflectivity occurs at one third of the initial density, near where the metal–nonmetal transition is predicted to occur. The reflectivity calculated based on the Helmholtz equations with the dielectric function from the density functional theory calculations is compared with the experimental measurement. Disagreement of reflectivity between calculation and measurement probably originates from the energy shift of the dielectric function due to the inaccurate Kohn–Sham eigenvalue. More accurate methods on calculating the dielectric function are still needed to improve the calculated reflectivity.

Cellular vehicle-to-everything (C-V2X) in 5G enables device-to-device (D2D) group communications to support a variety of proximity-based services. While D2D group communication can improve mobile network spectrum efficiency and user experience by offloading local traffic and extending the coverage of cellular base stations, it has also raised new privacy challenges on balancing UE anonymity and message linkability. In this paper, we first identify the limitations of current standardization and literature on addressing this issue. Then, we design a Service-oriented Authentication Framework (SAF) that supports secure service delegation and anonymous message authentication. Specifically, a UE-controlled linking method is introduced to support adaptive linkability for different V2X use cases. Security analysis demonstrates the SAF is secure under cryptographic assumptions. Moreover, we conduct extensive simulations based on Vissim and NS3 to show that the SAF is more efficient compared with the state-or-art literature and is feasible for 3GPP standardized V2X group communications.

Vehicle-to-Everything (V2X) assisted proximity marketing is one of the most promising V2X services due to its huge potential, and has attracted a lot of research efforts recently. In proximity marketing, roadside merchants rely on third-party ad networks to target their advertisements to nearby vehicles or pedestrians with related interests, and pay ad networks according to some pricing mechanisms, such as cost per-view. It is therefore important for merchants to learn ad conversion rate (how much of their revenue can be attributed to proximity marketing) such that merchants can adjust their advertising strategy. For ad conversion, two-party private set intersection (PSI) technique has been widely adopted, where ad networks and merchants can jointly compute ad conversion rate without leaking sensitive customer information. However, state-of-art literature on PSI either assumes the involved two parties honestly follow the protocol or only tolerates limited adversarial behaviors. In this paper, we first design a novel and efficient PSI scheme that is secure in the presence of malicious adversaries, where two parties can arbitrarily deviate from the scheme. By integrating an efficient input certification mechanism into the designed PSI scheme, we propose a privacy-preserving ad conversion protocol for V2X-assisted proximity marketing, that can achieve input privacy, unlinkability, unforgeability, and output verifiability. Security analysis demonstrates that the proposed ad conversion protocol is secure under cryptographic assumptions. Finally, we show that the proposed ad conversion protocol outperforms the state-of-art approaches when considering both security strength and computation complexity.

In this Letter, we demonstrated an intensity-modulated directional torsion sensor based on an in-line Mach–Zehnder interferometer in single-mode fiber. A non-circular symmetric perturbation is created to excite non-circular symmetric cladding mode and then interference with the core mode at the second perturbation. An initial rotation angle is designed between two perturbations for the purpose of discriminating the torsion direction. Both experimental and theoretical results enforce that the spectral peak/dip turns to be the dip/peak when the fiber is twisted from the counter-clockwise to the clockwise direction. Benefiting from the reversal between peak and dip, an intensity-modulated directional torsion sensor is realized in the range from −50 rad/m to 50 rad/m with a sensitivity of 45.3%/(rad/cm).

Internet of Things (IoT) is reshaping our daily lives by bridging the gaps between physical and digital world. To enable ubiquitous sensing, seamless connection and real-time processing for IoT applications, fog computing is considered as a key component in a heterogeneous IoT architecture, which deploys storage and computing resources to network edges. However, the fog-based IoT architecture can lead to various security and privacy risks, such as compromised fog nodes that may impede developments of IoT by attacking the data collection and gathering period. In this paper, we propose a novel privacy-preserving and reliable scheme for the fog-based IoT to address the data privacy and reliability challenges of the selective data aggregation service. Specifically, homomorphic proxy re-encryption and proxy re-authenticator techniques are respectively utilized to deal with the data privacy and reliability issues of the service, which supports data aggregation over selective data types for any type-driven applications. We define a new threat model to formalize the non-collusive and collusive attacks of compromised fog nodes, and it is demonstrated that the proposed scheme can prevent both non-collusive and collusive attacks in our model. In addition, performance evaluations show the efficiency of the scheme in terms of computational costs and communication overheads.

Mobile crowd sensing (MCS) represents one of the most promising approaches for improving life quality of individuals with sensing and computing devices. MCS is playing a more and more important role in various fields of service, such as traffic monitoring and commercial advertisement. Security and privacy of communication in MCS attract increasing attention from the academia and industry since the sensing data are usually sensitive for users. Some users worry about the leakage of their private information when they share their data to the third parties. To address this issue, in this paper, we propose a practical blacklist-based anonymous authentication scheme in which users can enjoy an anonymous environment and share their information without worrying about any information leakage. Security analysis shows that our scheme can achieve anonymity, blacklistability, nonrepudiation and unlinkability. Performance evaluation demonstrates that our scheme is more efficient in terms of computation overhead compared with the existing works.

Mobile cloud computing has been involved as a key enabling technology to overcome the physical limitations of mobile devices toward scalable and flexible mobile services. In the mobile cloud environment, searchable encryption, which enables direct search over encrypted data, is a key technique to maintain both the privacy and usability of outsourced data in cloud. On addressing the issue, many research efforts resolve to using the searchable symmetric encryption (SSE) and searchable public-key encryption (SPE). In this paper, we improve the existing works by developing a more practical searchable encryption technique, which can support dynamic updating operations in the mobile cloud applications. Specifically, we make our efforts on taking the advantages of both the SSE and SPE techniques, and propose PSU, a Personalized Search scheme over encrypted data with efficient and secure Updates in mobile cloud. By giving thorough security analysis, we demonstrate that the PSU can achieve a high security level. Using extensive experiments in a real-world mobile environment, we show that the PUS is more efficient compared with the existing proposals.

In big data era, data are usually stored in databases for easy access and utilization, which are now woven into every aspect of our lives. However, traditional relational databases cannot address users' demands for quick data access and calculating, since they cannot process data in a distributed way. To tackle this problem, non-relational databases such as MongoDB have emerged up and been applied in various Scenarios. Nevertheless, it should be noted that most MongoDB products fail to consider user's data privacy. In this paper, we propose a practical encrypted MongoDB (i.e., CryptMDB). Specifically, we utilize an additive homomorphic asymmetric cryptosystem to encrypt user's data and achieve strong privacy protection. Security analysis indicates that the CryptMDB can achieve confidentiality of user's data and prevent adversaries from illegally gaining access to the database. Furthermore, extensive experiments demonstrate that the CryptMDB achieves better efficiency than existing relational database in terms of data access and calculating.

With the rapid development of portable mobile devices, crowd sensing systems have been recognized as a key technology to utilize the data collected by the portable mobile devices towards scalable and flexible mobile services. However, since the information provided by devices may not be reliable, the aggregated results of the collected data may not be accurate. To tackle this challenge, various truth discovery schemes have been proposed. On the other hand, a practical issue of privacy protection is not considered in most existing truth discovery schemes. In this paper, we propose an Efficient Privacy-preserving Truth Discovery (EPTD) in Crowd Sensing Systems, which can protect the privacy of users' observed values and weights in truth discovery process. Finally, we show the performance of our scheme is better than existing models in terms of computation overhead.