Abstract k-Anonymity is one of the most well-known privacy models. Internal and external
attacks were discussed for this privacy model, both focusing on categorical data. These …

In the modern era, data of diverse types (medical, financial, etc.) are outsourced from data
owner environments to the public domains for data mining and knowledge discovery …

As widely known in the literature, location-based services can seriously threaten users'
privacy. Privacy-aware location-based services can be obtained by protecting the user's …

Data anonymization is a technique that safeguards individuals' privacy by modifying attribute
values in published data. However, increased modifications enhance privacy but diminish …

This study delves into the privacy risks associated with user interactions in complex
networks such as those generated on social media platforms. In such networks, potentially …

Data processors increasingly rely on external data sources to improve strategic or
operational decision taking. Data owners can facilitate this by releasing datasets directly to …

Time is an often recurring quasi-identifying attribute in many datasets. Anonymizing such
datasets requires generalizing the time attribute (s) in the dataset. Examples are start dates …

This paper introduces and investigates k-unmatchability, a counterpart of k-anonymity for
knowledge graphs. Like kanonymity, k-unmatchability enhances privacy by ensuring that …

Reproducibility is a key aspect of the scientific method as it provides evidence for research
claims. It is essential to promote openness, accessibility, and collaboration within the …

k-anonymity [1],[2], aims to ensure that individual data cannot be distinguished from that of at
least (k− 1) others in the same database, regardless of additional knowledge. However, this …