相关论文: The Third-Party Access Effect: An Overlooked Chall…

The rapid adoption of digital technologies has greatly increased the volume of real-world data (RWD) in education. While these data offer significant opportunities for advancing learning analytics (LA), secondary use for research is…

As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely…

Modern websites frequently use and embed third-party services to facilitate web development, connect to social media, or for monetization. This often introduces privacy issues as the inclusion of third-party services on a website can allow…

Since its introduction in 2006, differential privacy has emerged as a predominant statistical tool for quantifying data privacy in academic works. Yet despite the plethora of research and open-source utilities that have accompanied its…

Educational technologies are revolutionizing how educational institutions operate. Consequently, it makes them a lucrative target for breach and abuse as they often serve as centralized hubs for diverse types of sensitive data, from…

While secondary use of real-world data (RWD) in education offers substantial research opportunities, data sharing is often limited by privacy constraints. Differentially private synthetic data generation (DP-SDG) has emerged as a possible…

The widespread interest in learning analytics (LA) is associated with increased availability of and access to student data where students' actions are monitored, collected, stored and analysed. The availability and analysis of such data is…

OpenData movement around the globe is demanding more access to information which lies locked in public or private servers. As recently reported by a McKinsey publication, this data has significant economic value, yet its release has…

The new information and communication technology providers collect increasing amounts of personal data, a lot of which is user generated. Unless use policies are privacy-friendly, this leaves users vulnerable to privacy risks such as…

Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to…

This study investigates the acceptability of different artificial intelligence (AI) applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI…

Large, curated datasets are required to leverage speech-based tools in healthcare. These are costly to produce, resulting in increased interest in data sharing. As speech can potentially identify speakers (i.e., voiceprints), sharing…

Diffusion models have recently gained significant attention in both academia and industry due to their impressive generative performance in terms of both sampling quality and distribution coverage. Accordingly, proposals are made for…

Machine unlearning is a newly popularized technique for removing specific training data from a trained model, enabling it to comply with data deletion requests. While it protects the rights of users requesting unlearning, it also introduces…

As the world becomes increasingly dependent on technology, researchers in both industry and academia endeavor to understand how technology is used, the impact it has on everyday life, the artifact life-cycle and overall integrations of…

As Smart Home Personal Assistants (SPAs) evolve into social agents, understanding user privacy necessitates interpersonal communication frameworks, such as Privacy Boundary Theory (PBT). To ground our investigation, our three-phase…

Multiple synthetic data generation models have emerged, among which deep learning models have become the vanguard due to their ability to capture the underlying characteristics of the original data. However, the resemblance of the synthetic…

Differential Privacy (DP) is an important privacy-enhancing technology for private machine learning systems. It allows to measure and bound the risk associated with an individual participation in a computation. However, it was recently…

LDP (Local Differential Privacy) has recently attracted much attention as a metric of data privacy that prevents the inference of personal data from obfuscated data in the local model. However, there are scenarios in which the adversary…

The ability to share social network data at the level of individual connections is beneficial to science: not only for reproducing results, but also for researchers who may wish to use it for purposes not foreseen by the data releaser.…