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Anonymizing text that contains sensitive information is crucial for a wide range of applications. Existing techniques face the emerging challenges of the re-identification ability of large language models (LLMs), which have shown advanced…

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving…

Organizations are collecting vast amounts of data, but they often lack the capabilities needed to fully extract insights. As a result, they increasingly share data with external experts, such as analysts or researchers, to gain value from…

The application and development of process mining techniques face significant challenges due to the lack of publicly available real-life event logs. One reason for companies to abstain from sharing their data are privacy and confidentiality…

Browser fingerprinting is a growing technique for identifying and tracking users online without traditional methods like cookies. This paper gives an overview by examining the various fingerprinting techniques and analyzes the entropy and…

In the recent time, the problem of protecting privacy in statistical data before they are published has become a pressing one. Many reliable studies have been accomplished, and loads of solutions have been proposed. Though, all these…

Public access to digital data can turn out to be a cause of undesirable information disclosure. That's why it is vital to somehow protect the data before publishing. There exist two main subclasses of such a task, namely, providing…

The emergence of social and technological networks has enabled rapid sharing of data and information. This has resulted in significant privacy concerns where private information can be either leaked or inferred from public data. The problem…

Despite the considerable promise of Retrieval-Augmented Generation (RAG), many real-world use cases may create privacy concerns, where the purported utility of RAG-enabled insights comes at the risk of exposing private information to either…

The growing expanse of e-commerce and the widespread availability of online databases raise many fears regarding loss of privacy and many statistical challenges. Even with encryption and other nominal forms of protection for individual…

Ensuring privacy of sensitive data is essential in many contexts, such as healthcare data, banks, e-commerce, wireless sensor networks, and social networks. It is common that different entities coordinate or want to rely on a third party to…

An increasing amount of mobility data is being collected every day by different means, e.g., by mobile phone operators. This data is sometimes published after the application of simple anonymization techniques, which might lead to severe…

The collection and use of personal data are becoming more common in today's data-driven culture. While there are many advantages to this, including better decision-making and service delivery, it also poses significant ethical issues around…

Data sharing between different organizations is an essential process in today's connected world. However, recently there were many concerns about data sharing as sharing sensitive information can jeopardize users' privacy. To preserve the…

To date publish of a giant social network jointly from different parties is an easier collaborative approach. Agencies and researchers who collect such social network data often have a compelling interest in allowing others to analyze the…

Our behavior (the way we talk, walk, act or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions and health conditions. Hence, techniques to protect individuals privacy against…

Creating anonymity means cutting connections. A common goal in this context is to prevent accountability. This prevention of accountability can be problematic, for example, if it leads to delinquents remaining undetected. However,…

The Domain Name System (DNS) does not provide query privacy. Query obfuscation schemes have been proposed to overcome this limitation, but, so far, they have not been evaluated in a realistic setting. In this paper we evaluate the security…

A firm seeks to analyze a dataset and to release the results. The dataset contains information about individual people, and the firm is subject to some regulation that forbids the release of the dataset itself. The regulation also imposes…

Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. However, their reliance on massive internet-sourced datasets for training brings notable privacy…