Crowdchain: A Location Preserve Anonymous Payment System Based on Permissioned Blockchain

Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of multimedia content to be cached in proximity to vehicles. Since vehicles are equipped with a certain amount of caching resource, they can be regarded as edge nodes to expand the caching capacity of the network edge. However, with much sensitive personal information, vehicles may be not willing to cache their content to an untrusted vehicle. Permission blockchain has the potential to address such an issue. In this paper, we utilize permissioned blockchain to design a secure content caching scheme between vehicles. Since high mobility of vehicles makes a dynamic caching environment, we exploit deep reinforcement learning approach to design the content caching scheme. Moreover, we propose a new block verifier selection metric, Proof-of-Utility (PoU), to enable a lightweight permissioned blockchain. Security analysis shows that our proposed blockchain empowered content caching can achieve security and privacy protection. Numerical results based on the Uber dataset indicate the DRL-inspired content caching scheme significantly outperforms two benchmark policies.

In the project-oriented construction industry, recruiting qualified workers who can finish the required tasks in a limited time is important. However, the high turnover rate in the construction workforce poses a challenge in verifying applicant information, leading to potential issues like information falsification and inaccurate assessments due to information asymmetry. Additionally, the industry’s male-dominated nature may foster stereotype-based biases, particularly concerning sensitive attributes (e.g., gender). Such situations contribute to unfair competition among applicants. The construction industry is also experiencing new technologies like BIM, AI, and Blockchain. Their integration shows potential for automation, fairness, information security, and trustworthiness in recruitment. To build a diverse and competent workforce, we propose a decentralized digital resume-based job applicant screening and appraisal framework via BIM, AI, and Blockchain. First, we develop a blockchain job applicant data model that distinguishes between personal privacy data and work-related data for record and storage. A permissioned Blockchain is then designed to facilitate partial transparency for potential employers while ensuring the confidentiality of applicants’ sensitive information. Specifically, for personal privacy data, sensitive attributes (gender, race, etc.) are safeguarded via encryption, and data (address, etc.) about company preferences (the desired distance range from the company, etc.) is also secured while allowing for employer verification via Zero-Knowledge Proofs and smart contracts for information protection. Utilizing time-stamped authentication, applicants’ work history (reference network-based and performance-based information) remains immutable and is securely accessible by potential employers. Based on the validated applicant data and diverse company requirements, the digital resume is generated and customized for each position through smart contracts. For validation, a prototype system is developed with the data from LinkedIn. The results show its feasibility for trusted, fair, secure, and effective construction recruitment.

Cloud computing has emerged as the dominant platform for contemporary data management and service provision. However, its centralized nature poses significant risks to security, privacy, and trust. Distributed systems can enhance data integrity and auditability by incorporating blockchain technology, which offers a decentralized and tamper-resistant approach. Nevertheless, the inherent transparency of blockchain conflicts with the confidentiality requirements of cloud environments. This review paper analyzes existing studies on privacy-preserving blockchain architectures designed to secure cloud-based information systems. A systematic literature review methodology was adopted, examining forty-eight peer-reviewed studies published between 2018 and 2024. The findings reveal that researchers have explored approaches such as encryption, zero-knowledge proofs, homomorphic encryption, and hybrid on/off-chain models to balance transparency and privacy. Scalability, interoperability, and regulatory compliance remain key challenges, particularly in permissioned blockchains, which nevertheless offer advantages in governance and compliance. The study identifies research gaps and future directions, including the development of common privacy frameworks, integration of confidential computing, and establishment of standardized evaluation metrics. Overall, privacy-sensitive blockchain architectures hold strong potential for creating trustworthy and secure cloud systems.

The digital age, e-commerce platforms can accurately capture users' consumption habits, browsing history, geographic location, and other personal information through big data, artificial intelligence and other technical means, thereby providing users with a personalized shopping experience. However, behind this convenience, there are also huge privacy risks. Once a user's personal information is improperly used or leaked, it will not only lead to the infringement of personal privacy rights but may also lead to serious consequences such as identity theft and financial fraud, causing huge economic losses and psychological pressure on users. This paper deeply explores the current status of user privacy protection on e-commerce platforms and the main problems it faces and proposes multi-dimensional solutions, including improving laws and regulations, strengthening technical means, enhancing user privacy protection awareness, and optimizing rights protection channels. Only through the multi-party collaboration and continuous improvement of e-commerce platforms, users, and regulatory agencies can a solid privacy protection system be built to effectively deal with the risk of privacy leakage and promote the healthy development of the e-commerce industry.

As an essential means of privacy protection technology, zero-knowledge proof has gradually been applied into various fields with the development of blockchain technology, such as the Internet of Vehicles and Bitcoin. Personal comprehensive credit score as a measure to promote social governance is closely related to personal privacy. Although there are currently credit score calculation systems for various application scenarios, these systems almost ignore user privacy protection, which leads to user information leakage or abuse. The combination of zero-knowledge proof and personal credit score calculation has been studied by a number of researchers at the present stage. However, all data are provided by users directly in the current schemes, which did not consider the data authenticity under the situation that users provided false data. In this paper, we utilize zero-knowledge proof to design a novel privacy protection scheme for personal credit score calculation, taking into account the authenticity verification of multi-dimensional user data. In addition, our scheme also proposes the concept of a universal verification platform based on blockchain for personal credit scores. This platform has more substantial applicability and versatility for any qualified institution that requires querying and verifying user’s credit scores. At the end of the paper, we conducted a security analysis and performance evaluation for the overall scheme.

The comprehensive adoption of Electronic Medical Records (EMRs) offers numerous benefits but also introduces risks of privacy leakage, particularly for patients with Sexually Transmitted Infections (STI) who need protection from social secondary harm. Despite advancements in privacy protection research, the effectiveness of these strategies in real-world data remains debatable. The objective is to develop effective information extraction and privacy protection strategies to safeguard STI patients in the Chinese healthcare environment and prevent unnecessary privacy leakage during the data-sharing process of EMRs. The research was conducted at a national healthcare data center, where a committee of experts designed rule-based protocols utilizing natural language processing techniques to extract STI information. Extraction Protocol of Sexually Transmitted Infections Information (EPSTII), designed specifically for the Chinese EMRs system, enables accurate and complete identification and extraction of STI-related information, ensuring high protection performance. The protocol was refined multiple times based on the calculated precision and recall. Final protocol was applied to 5,000 randomly selected EMRs to calculate the success rate of privacy protection. A total of 3,233,174 patients were selected based on the inclusion criteria and a 50% entry ratio. Of these, 148,856 patients with sensitive STI information were identified from disease history. The identification frequency varied, with the diagnosis sub-dataset being the highest at 4.8%. Both the precision and recall rates have reached over 95%, demonstrating the effectiveness of our method. The success rate of privacy protection was 98.25%, ensuring the utmost privacy protection for patients with STI. Finding an effective method to protect privacy information in EMRs is meaningful. We demonstrated the feasibility of applying the EPSTII method to EMRs. Our protocol offers more comprehensive results compared to traditional methods of including STI information.

Mobile devices with Global Positioning System enable various kinds of location information searches using location based services (LBS). Location information and query contents of searchers are privacy information. Privacy protection requires to hide location information and query contents of searchers from not only third parties such as eavesdroppers but also location database servers which answer queries. This paper focuses on privacy protection in nearest neighbor search. There are several researches of secure computation in nearest neighbor search for other than LBS. This paper proposes a new method of privacy protection in LBS. The method is based on Asymmetric Scalar-product Preserving Encryption. This paper also shows that the proposed method is faster and more secure than conventional privacy protection methods in LBS.

With the increasing convenience of location-based services (LBSs), there have been growing concerns about the risk of privacy leakage. We show that existing techniques fail to defend against a statistical attack meant to infer the user’s location privacy and query privacy, which is due to continuous queries that the same user sends in the same location in a short time, causing the user’s real location to appear consecutively more than once and the query content to be the same or similar in the neighboring query. They also fail to consider the hierarchical structure of the address, so locations in an anonymous group may be located in the same organization, resulting in leaking of the user’s organization information and reducing the privacy protection effect. This paper presents a dummy generation scheme, considering the hierarchical structure of the address (DGS-HSA). In our scheme, we introduce a novel meshing method, which divides the historical location dataset according to the administrative region division. We also choose dummies from the historical location dataset with the two-level grid structure to realize the protection of the user’s location, organization information, and query privacy. Moreover, we prove the feasibility of the presented scheme by solving the multi-objective optimization problem and give the user’s privacy protection parameters recommendation settings, which balance the privacy protection level and system overhead. Finally, we evaluate the effectiveness and the correctness of the DGS-HSA through theoretical analysis and extensive simulations.

Traditional centralized scholarship evaluation processes typically require students to submit detailed academic records and qualification information, which exposes them to risks of data leakage and misuse, making it difficult to simultaneously ensure privacy protection and transparent auditability. To address these challenges, this paper proposes a scholarship evaluation system based on Decentralized Identity (DID) and Zero-Knowledge Proofs (ZKP). The system aggregates multidimensional ZKPs off-chain, and smart contracts verify compliance with evaluation criteria without revealing raw scores or computational details. Experimental results demonstrate that the proposed solution not only automates the evaluation efficiently but also maximally preserves student privacy and data integrity, offering a practical and trustworthy technical paradigm for higher education scholarship programs.

Influence maximization is one of the most fundamental problems in social network analysis due to its significant impact on viral marketing and targeted advertisements. Considerable amount of works has extensively studied to analyze the influence in social networks, but most of the existing works unfortunately neglected the fact that the location information in the cyber-physical world could also play an important role in the influence prorogation. Furthermore, even though a few works consider a little location information to enhance the influence maximization, they do not discuss any privacy issue of the model and expose user's location information directly to the public. This paper considers the problem of influence maximization in both GPS-enabled cyber-physical and online social networks with privacy reservation. We propose one novel model merging both GPS data of cyber-physical network and relationship data of online social network together in a unified framework, then we provide an efficient algorithm to solve the influence maximization problem in the framework. Besides the influence maximization problem, our framework could also support other applications involving both cyber-physical and online social networks. Further more, to protect the sensitive location and link information, we also provide corresponding techniques to protect the privacy during the whole influence maximization process. Empirical studies of real life datasets demonstrate the power of engaging location information to influence maximization, and suggest that our resolution outperforming most existing alternative algorithms with the protection of location privacy.

Aiming at the existing problems of insufficient security and poor scalability of existing blockchain data management solutions for the Internet of Vehicles, we use IPFS distributed technology to design a data privacy protection method for the Internet of Vehicles based on zero-knowledge proof. First, we use the Schnorr identity authentication protocol, a classic non-interactive zero-knowledge proof scheme, to complete the authentication and authorization of vehicles by RSU under the premise of protecting vehicle privacy. Secondly, we store encrypted data on IPFS-based edge distributed servers, and use Hyperledger Fabric to store authentication records and data indexes, which solves the problem of insufficient blockchain scalability. Finally, in order to improve the controllability and security of the data, we use the proxy re-encryption mechanism to manage the Symmetric-key of the data, and let RSU act as a proxy to perform re-encryption. Compared with the existing schemes, the proposed scheme reduces the communication overhead of the vehicle identity authentication phase, which is only 1728 bits. And while improving the scalability of the blockchain, it also enhances the privacy protection of the data of the Internet of Vehicles.

In response to the privacy leakage risks inherent in the big data processing of power user personas, propose a collaborative optimization‐based joint privacy protection mechanism for Differential Privacy (DP) and Secret Sharing (SS). This method reveals the inherent correlation between DP noise parameters and SS sharding strategy and designs an adaptive noise sharding mapping mechanism to achieve joint optimization of privacy protection and communication overhead. Based on this mechanism, a specific privacy protection scheme for power user personas is designed. Experimental results demonstrate that the DP + SS privacy protection method designed in this study achieves an accuracy rate of 93.14% on the MNIST dataset and maintains an accuracy rate of 92.06% across multiple datasets, indicating that the scheme effectively quantifies the level of privacy protection. The privacy protection level of the scheme is quantitatively defined through differential privacy budget ε ∈ [0.11,0.15], secret sharing ( k = 3, n = 5) threshold mechanism, and privacy leakage probability δ = 10 −5 . The privacy budget is dynamically allocated through the correlation between differential privacy noise variance and the minimum reconstruction threshold of secret sharing, ensuring that privacy risks are controllable and quantifiable. At the same time, the average training time for servers is about 186.4 s, which refers to the total end‐to‐end training time in a four‐server cluster environment. Compared with a single server encryption scheme, the efficiency is improved by 48.6%. The communication overhead has increased to 10.6GB, which is the total chip transmission overhead per 10,000 users. However, the calculation stability can be maintained through load balancing, indicating that the scheme has certain feasibility. The aforementioned research results not only provide a viable applied cryptographic technology solution for privacy protection in power big data but also offer new insights into balancing privacy protection strength and data availability. More importantly, the joint privacy protection mechanism proposed by the research institute can serve as a fundamental enabling technology for the infrastructure of a secure smart grid, providing quantifiable and trustworthy privacy and security guarantees for advanced data‐driven applications such as precise load prediction, demand‐side response, and collaborative optimization of distributed energy, thereby supporting the reliable and intelligent development of the smart grid.

Summary form only given. Recent advances in the Internet enabling technologies and wide deployment of broadband optical and wireless networks in both the core and access areas have led to rapid growth of real-time multimedia communications and collaborative computing applications over the Internet anytime and anywhere. The mobile multimedia collaboration enabled applications include networked healthcare, distributed data management for digital content distribution, telematics, and 3G/4G/wireline network convergence. These mobile multimedia collaborative networking and computing applications are recently made possible due to widely deployed broadband Internet (e.g. IP-over-SONET) and last-mile broadband technologies (e.g., ADSL/DSL and cable modem), the maturity of a widely accepted application layer signaling protocol (session initiation protocol (SIP)), and availability of high-power and smart mobile devices. These mobile multimedia collaboration enabled applications share a common core application requirement set, referred to as requirements, i.e., right data sent to person at time in form at cost. This 5R requirement set basically covers technical challenges of security, QoS, privacy protection, performance, mobility and costs from layer 1 to layer 7. In this talk, we first discuss the concept of mobile multimedia collaboration and its enabled applications as described above. We then use distributed data management (DRM) for secure digital content distribution as an example to illustrate 5R requirements. We conclude the talk by identifying challenges of meeting the 5R requirements as well as reviewing the progress of 5R implementation under mobile broadband Internet networking infrastructure.

AutoPrivacy: Automatic privacy protection and tagging suggestion for mobile social photo

This work shows the evolution regarding regulation of location services in both the USA and the EU. The article explains why these services were first regulated and the regulation itself is then explained. Two of the most important issues regarding location information are privacy protection and unsolicited communications. Shown here is the huge commercial value of location information and how the user’s approval can be manipulated to obtain consent to process and use as much information as possible.