Differential privacy mechanisms can offer a trade-off between privacy and utility by using privacy metrics and utility metrics. The trade-off of differential privacy shows that one thing increases and another decreases in terms of privacy metrics and...
Powerful rising trends of mobile media platforms have also resulted in the escalation of users’ privacy concerns. However, there is a paradox between users’ attitudes towards privacy and their actual privacy disclosure behaviors. This stu...
People use social media to achieve particular gratifications despite expressing concerns about the related privacy risks that may lead to negative consequences. This inconsistency between privacy concerns and actual behaviour has been referred to as...
Multiple studies have demonstrated that the conventional method of learning is suboptimal when our goal is to enhance individuals’ genuine privacy behavior. This study introduces a framework for transforming privacy behavior, with the objective...
Following a series of legislative changes around privacy over the past 25 years, this study highlights data protection regulations and the complexities of applying these frameworks. To address this, we created a privacy framework to guide organisatio...
The increasing complexity of data ecosystems demands advanced methodologies for systematic privacy risk assessment. This work introduces two complementary metrics—the privacy risk expansion factor (PREF) and the privacy exposure index (PEI)&mda...
More and more users openly share their information on online websites, with the resulting privacy issues being under scrutiny. Content such as a user’s personal data and location information is often asked for before posting to enforce the user...
This paper investigates lift, the likelihood ratio between the posterior and prior belief about sensitive features in a dataset. Maximum and minimum lifts over sensitive features quantify the adversary’s knowledge gain and should be bounded to...
The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through privacy-enhancing technologies (PETs), requires a rigorous definition of what...
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...
Determining how to balance data utilities and data privacy when datasets are released to be utilized outside the scope of data-collecting organizations constitutes a major challenge. To achieve this aim in data collection (datasets), several privacy...
Mobile Edge Computing (MEC) has been widely applied in various Internet of Things (IoT) scenarios due to its advantages of low latency and low energy consumption. However, the offloading of tasks generated by terminal devices to edge servers inevitab...
As one of the promising paradigms of decentralized machine learning, multi-party learning has attracted increasing attention, owing to its capability of preventing the privacy of participants from being directly exposed to adversaries. Multi-party le...
This study aimed to examine the effects of perceived privacy risks and benefits on the online privacy protection behaviors of Chinese teens, with information privacy concerns treated as a mediator variable. The questionnaire survey data (N = 1538) we...
Protecting the privacy of individuals is of utmost concern in today’s society, as inscribed and governed by the prevailing privacy laws, such as GDPR. In serial data, bits of data are continuously released, but their combined effect may result...
With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has...
With the development of the mobile internet, service providers obtain data and resources through a large number of terminal user devices. They use private data for business empowerment, which improves the user experience while causing users’ pr...
1 May 2019
Developing an approach to manage privacy risk is critical to the success of e-marketing. Understanding the antecedents of privacy risk is therefore essential for the success of e-business. Because governments, businesses, and consumers are economic a...
The publication of trajectory data provides critical information for various location-based services, and it is critical to publish trajectory data safely while ensuring its availability. Differential privacy is a promising privacy protection technol...
The digital era introduces significant challenges for privacy protection, which grow constantly as technology advances. Privacy is a personal trait, and individuals may desire a different level of privacy, which is known as their “privacy conce...
Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but o...
The article deals with the topic of attention paid to online privacy policy statements by university students. Privacy policy statements were originally intended to mitigate the users’ privacy con-cerns and support trust, but users disregard them. Th...
With the popularity of location-aware devices (e.g., smart phones), a large number of trajectory data were collected. The trajectory dataset can be used in many fields including traffic monitoring, market analysis, city management, etc. The collectio...
The collection and processing of personal data offers great opportunities for technological advances, but the accumulation of vast amounts of personal data also increases the risk of misuse for malicious intentions, especially in health care. Therefo...
Location-based services (LBS) are widely used due to the rapid development of mobile devices and location technology. Users usually provide precise location information to LBS to access the corresponding services. However, this convenience comes with...
Privacy policies play an important part in informing users about their privacy concerns by operating as memorandums of understanding (MOUs) between them and online services providers. Research suggests that these policies are infrequently read becaus...
Vehicle-to-grid (V2G) is an important component of smart grids and plays a significant role in improving grid stability, reducing energy consumption and generating cost. However, while electric vehicles are being charged, it is possible to expose the...
Social Networks (SNs) bring new types of privacy risks threats for users; which developers should be aware of when designing respective services. Aiming at safeguarding users’ privacy more effectively within SNs, self-adaptive privacy preserving sche...
The increasing digitization of educational data poses critical challenges in balancing predictive accuracy with privacy protection for sensitive student information. This study introduces DP-TabNet, a pioneering framework that integrates the interpre...
With the prevalence of cloud computing, the outsourcing of computation has gained significant attention. Clients with limited computing power often outsource complex computing tasks to the cloud to save on computing resources and costs. In outsourcin...
A privacy-constrained information extraction problem is considered where for a pair of correlated discrete random variables (X,Y) governed by a given joint distribution, an agent observes Y and wants to convey to a potentially publi...
Edge computing has emerged as an innovative paradigm that decentralizes computation to the network’s periphery, empowering edge servers to manage user-initiated complex tasks. This strategy alleviates the computational load on end-user devices...
Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major probl...
Federated learning is a widely applied distributed machine learning method that effectively protects client privacy by sharing and computing model parameters on the server side, thus avoiding the transfer of data to third parties. However, informatio...
As federated learning continues to increase in scale, the impact caused by device and data heterogeneity is becoming more severe. FedProx, as a comparison algorithm, is widely used as a solution to deal with system heterogeneity and statistical heter...
A user’s devices such as their phone and computer are constantly bombarded by IoT devices and associated applications seeking connection to the user’s devices. These IoT devices may or may not seek explicit user consent, thus leaving the...
2 February 2026
With the rapid expansion of the Internet, it is crucial to be aware of the different aspects of privacy, especially in light of rising global cybersecurity threats and data breaches. While previous research has identified various factors when studyin...
Over time, the many different ways in which we collect and use data have become more complex as we communicate and interact with an ever-increasing variety of modern technologies. Although people often say they care about their privacy, they do not h...
After the enactment of the GDPR in 2018, many companies were forced to rethink their privacy management in order to comply with the new legal framework. These changes mostly affect the Controller to achieve GDPR-compliant privacy policies and managem...
In response to the growing relevance of shared housing models such as co-living and co-housing, this study investigates how spatial configuration affects the experience and negotiation of privacy in shared domestic environments. While privacy is ofte...
As traditional federated learning algorithms often fall short in providing privacy protection, a growing body of research integrates local differential privacy methods into federated learning to strengthen privacy guarantees. However, under a fixed p...
In web-based services, users are often required to submit personal data, which may be shared with third parties. Although privacy regulations mandate the disclosure of intended recipients in privacy policies, this does not fully alleviate users&rsquo...
In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer bro...
An individual’s location data is very sensitive geoinformation. While its disclosure is necessary, e.g., to provide location-based services (LBS), it also facilitates deep insights into the lives of LBS users as well as various attacks on these...
The introduction of Data Protection by Default and Design (DPbDD) brought in as part of the General Data Protection Regulation (GDPR) in 2018, has necessitated that businesses review how best to incorporate privacy into their processes in a transpare...
2 March 2020
Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns ab...
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...
The development of big data analytics in healthcare has created a demand for privacy-conscious and scalable machine learning algorithms that can allow the use of patient information across different healthcare organizations. In this study, the diffic...
14 July 2025
As data becomes a core driver of modern business innovation, mobile applications increasingly collect and process users’ personal information, posing significant challenges to the effectiveness of informed consent and the legitimacy of user aut...
With the development of the mobile Internet, location-based services are playing an important role in everyday life. As a new location-based service, Spatial Crowdsourcing (SC) involves collecting and analyzing environmental, social, and other spatio...
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