An intelligent logistics system is a production system based on the Internet of Things (IoT), and the logistics information of humans has a high degree of privacy. However, the current intelligent logistics system only protects the privacy of shipper...
1 July 2025
While data-driven digital technologies (e.g., mobile applications) have brought convenience to users, they have also raised data privacy concerns. Regulators have taken various actions that ensure user data privacy to balance the protection and explo...
Mobile cloud computing (MCC) is a domain that stemmed from advances in mobile technology and cloud computing. Although debate continues about the best strategies to preserve privacy and personal data protection in MCC, it is essential to explore the...
Dynamic consent management allows a data subject to dynamically govern her consent to access her data. Clearly, security and privacy guarantees are vital for the adoption of dynamic consent management systems. In particular, specific data protection...
A popular bumper sticker states: “There is no cloud. It’s just someone else’s computer.” Despite the loss of control that comes with its use, critical records are increasingly being entrusted to the cloud, generating ever-growing concern about the pr...
To address the issues of privacy-utility imbalance, insufficient incentives, and lack of verifiable computation in current medical data sharing, this paper proposes a blockchain-based fair verification and adaptive differential privacy mechanism. The...
Blockchain, with its characteristics of non-tamperability and decentralization, has had a profound impact on various fields of society and has set off a boom in the research and application of blockchain technology. However, blockchain technology fac...
Current anomaly detection methods for charging stations primarily rely on centralized network architectures with federated learning frameworks. However, the rapid increase in the number of charging stations and the expanding scale of these networks i...
Being able to obtain various environmental and driving data from vehicles is becoming more and more important for current and future intelligent transportation systems (ITSs) to operate efficiently and economically. However, the limitations of privac...
Mobile crowd sensing (MCS) frequently relies on truth discovery to aggregate noisy, conflicting reports into reliable estimates. Existing approaches often either risk exposing user data or overlook heterogeneous privacy needs and task-specific reliab...
Against the backdrop of the continuous advancement of the market-oriented allocation of data factors, problems such as high data circulation costs, unclear pricing mechanisms, insufficient value conversion, and unreasonable profit allocation have con...
The current uneven deployment of charging stations for electric vehicles (EVs) requires a reliable prediction solution for smart grids. Existing traffic prediction assumes that users’ charging durations are constant in a given period and may no...
Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the m...
The increasing use of AI systems for face, object, action, scene, and emotion recognition raises significant privacy risks, particularly when processing Personally Identifiable Information (PII). Current privacy-preserving methods lack adaptability t...
In the development process of the power industry, accurately assessing the level of development of the electricity business environment is of great significance. However, traditional evaluation systems have limitations, with the issue of “data...
This paper addresses the critical challenge of secure computing in the context of deep learning, focusing on the pressing need for effective data privacy protection during transmission and storage, particularly in sensitive fields such as finance and...
Keyword-based remote integrity auditing schemes effectively address the integrity of electronic medical records (EMRs) stored in the cloud. In practice, users expect to be able to perform flexible dynamic data updates while also protecting data priva...
The real-time data collected by wearable devices enables personalized health management and supports public health monitoring. However, sharing these data with third-party organizations introduces significant privacy risks. As a result, protecting an...
The primary motivation is to address difficulties in data interpretation or a reduction in model accuracy. Although differential privacy can provide data privacy guarantees, it also creates problems. Thus, we need to consider the noise setting for di...
Individual users’ sensitive information, such as heart rate, calories burned, or even sleep patterns, are casually tracked by smart wearable devices to be further processed or exchanged, utilizing the ubiquitous capabilities of Internet of Things (Io...
Recently, with the great development of e-health, more and more countries have made certain achievements in the field of electronic medical treatment. The digitization of medical equipment and the structuralization of electronic medical records are t...
There is an increasing need to provide explainability for machine learning models. There are different alternatives to provide explainability, for example, local and global methods. One of the approaches is based on Shapley values. Privacy is another...
With the rapid development of social networks, personal privacy leakage has become more and more serious. A social network is a shared platform. Resources in a social network may be shared by multiple owners. In order to prevent privacy leakage, each...
The Internet of Things (IoT) is an emerging field consisting of Internet-based globally connected network architecture. A subset of IoT is the Internet of Healthcare Things (IoHT) that consists of smart healthcare devices having significant importanc...
Although the Crowd-Sensing perception system brings great data value to people through the release and analysis of high-dimensional perception data, it causes great hidden danger to the privacy of participants in the meantime. Currently, various priv...
Inference attacks and protection measures are two sides of the same coin. Although the former aims to reveal information while the latter aims to hide it, they both increase awareness regarding the risks and threats from social media apps. On the one...
In order to solve the problem of privacy disclosure when publishing high-dimensional data and to protect the privacy of frequent itemsets in association rules, a high-dimensional data publishing method based on frequent itemsets of association rules...
Hyperledger Fabric is a distributed ledger solution platform based on a modular architecture. The cryptographic algorithm is the core of the platform to ensure the security and tamper-resistant of the data on the chain. However, the original Fabric p...
This paper provides a systematic analysis of existing resource platforms, evaluating their advantages and drawbacks with respect to data privacy protection. To address the privacy and security risks associated with resource platform data, we propose...
As the rapid development of the industrial Internet of Things (IIoT) progresses, some data in the IIoT start to present the following characteristics: huge volume, high dimensions, distributed storage across multiple devices, and restricted data shar...
With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the d...
Unmanned aerial vehicle semantic communications are increasingly required in low-altitude sensing, intelligent inspection, and emergency response, where raw image transmission is difficult to sustain under limited onboard resources and time-varying a...
The Personal Information Protection Law (PIPL) was launched on 1 November 2021 in China. This article provides a state-of-the-art review of PIPL through a policy analysis. This paper aims to compare the three main worldwide data privacy paradigms tha...
The widespread use of location-based services (LBSs) has led to the continuous collection of user location and trajectory data, increasing the risk of privacy leakage and creating a persistent tradeoff between privacy protection and data utility. To...
This paper introduces a user-centered data privacy protection framework utilizing large language models (LLMs) and user attention mechanisms, which are tailored to address urgent privacy concerns in sensitive data processing domains like financial co...
One of the industries with the fastest rate of growth is healthcare, and this industry’s enormous amount of data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it fo...
To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, w...
Due to the openness of the vehicle-to-grid (V2G) network, the upload of charging and discharging data faces severe security challenges such as eavesdropping, tampering, and forgery. These challenges can lead to privacy breaches, transmission delays,...
Wireless body area networks (WBANs) are expected to play a vital role in the field of patient-health monitoring shortly. They provide a convenient way to collect patient data, but they also bring serious problems which are mainly reflected in the saf...
This paper examines the profitable digital afterlife industry (DAI), whose aim is to monetize the digital remains of departed internet users. Among the different services offered by such an industry, attention is drawn to AI and humanoid robots that...
Recently, the rapid development of the Internet of Things (IoT) has led to an increasing exponential growth of non-scalar data (e.g., images, videos). Local services are far from satisfying storage requirements, and the cloud computing fails to effec...
In recent years, cloud computing has attracted extensive attention from industry and academia due to its convenience and ubiquity. As a new Internet-based IT service model, cloud computing has brought revolutionary changes to traditional computing an...
Frequent gradient exchange and heterogeneous data distribution in federated learning can lead to serious privacy leakage risks. Traditional privacy-preserving strategies fail to meet the personalized privacy needs from different users and may cause a...
In recent years, the collection, mining, and utilization of data have become a new profit growth point for enterprises, and these events have also accelerated the pace of enterprises to collect users’ data. However, the relevance of personal da...
The protection of personal information has become a defining challenge for higher education institutions, particularly in developing contexts where regulatory frameworks are often strong on paper but weak in practice. This study investigates student...
The EU’s General Data Protection Regulation (GDPR) has recently come into effect and insofar as Internet of Things (IoT) applications touch EU citizens or their data, developers are obliged to exercise due diligence and ensure they undertake Da...
During software development activities, it is important for Information and Communication Technology (ICT) practitioners to know and understand practices and guidelines regarding information privacy, as software requirements must comply with data pri...
K-means, as a commonly used clustering method, has been widely applied in data analysis for smart meters. However, this method requires repeatedly computing the similarity between all data points and cluster centers in each iteration, which leads to...
As widely adopted privacy models in privacy-preserving data publishing (PPDP), k-anonymity and ℓ-diversity have been extensively studied by researchers to enable the release of useful information while preserving data privacy. However, existing...
Data governance for responsible AI systems remains challenged by the lack of automated tools that can apply robust privacy-preserving techniques without destroying analytical value. We propose AnonymAI, a novel methodological framework that integrate...
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