Related papers: Evaluating the Utility of Anonymized Network Trace…
In medical organizations large amount of personal data are collected and analyzed by the data miner or researcher, for further perusal. However, the data collected may contain sensitive information such as specific disease of a patient and…
Over the recent years, the availability of datasets containing personal, but anonymized information has been continuously increasing. Extensive research has revealed that such datasets are vulnerable to privacy breaches: being able to…
Anonymous Single-Sign-On authentication schemes have been proposed to allow users to access a service protected by a verifier without revealing their identity which has become more important due to the introduction of strong privacy…
Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, reduced cost, and it drives innovative solutions within the healthcare sector. However, health data is highly sensitive…
This paper systematizes log based Transparency Enhancing Technologies. Based on work on transparency from multiple disciplines we outline the purpose, usefulness, and pitfalls of transparency. We describe the mechanisms that allow log based…
This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP…
Anonymizing sensitive information in user text is essential for privacy, yet existing methods often apply uniform treatment across attributes, which can conflict with communicative intent and obscure necessary information. This is…
The unprecedented capture and application of face images raise increasing concerns on anonymization to fight against privacy disclosure. Most existing methods may suffer from the problem of excessive change of the identity-independent…
Social networks may contain privacy-sensitive information about individuals. The objective of the network anonymization problem is to alter a given social network dataset such that the number of anonymous nodes in the social graph is…
The development of artificial intelligence has significantly transformed people's lives. However, it has also posed a significant threat to privacy and security, with numerous instances of personal information being exposed online and…
We show that while anonymization effectively obscures firm identity, it significantly reduces the power of textual understanding, thereby diminishing models' ability to extract meaningful economic signals from financial texts. This…
Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. This paper investigates anonymization methods based on representation…
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,…
There are currently two approaches to anonymization: "utility first" (use an anonymization method with suitable utility features, then empirically evaluate the disclosure risk and, if necessary, reduce the risk by possibly sacrificing some…
The protection of private information is a crucial issue in data-driven research and business contexts. Typically, techniques like anonymisation or (selective) deletion are introduced in order to allow data sharing, e. g. in the case of…
Over the last two decades, the scale and complexity of Anonymous networks and its associated technologies grows exponentially as privacy has become a major concern of individuals. Also, some cyber attackers make use of privacy…
Anomaly detection has emerged as a popular technique for detecting malicious activities in local area networks (LANs). Various aspects of LAN anomaly detection have been widely studied. Nonetheless, the privacy concern about individual…
Data protection algorithms are becoming increasingly important to support modern business needs for facilitating data sharing and data monetization. Anonymization is an important step before data sharing. Several organizations leverage on…
Companies are looking to data anonymization research $\unicode{x2013}$ including differential private and synthetic data methods $\unicode{x2013}$ for simple and straightforward compliance solutions. But data anonymization has not taken off…
Biometric data contains distinctive human traits such as facial features or gait patterns. The use of biometric data permits an individuation so exact that the data is utilized effectively in identification and authentication systems. But…