Biblio

Differential privacy is a concept to quantity the disclosure of private information that is controlled by the privacy parameter ε. However, an intuitive interpretation of ε is needed to explain the privacy loss to data engineers and data subjects. In this paper, we conduct a worst-case study of differential privacy risks. We generalize an existing model and reduce complexity to provide more understandable statements on the privacy loss. To this end, we analyze the impact of parameters and introduce the notion of a global privacy risk and global privacy leak.

While location management is a key component of cellular networks, it is also a major privacy issue: location management empowers the network operator to track users. In today's public and scientific discussion, the centralized storage of location data is mostly taken as a fact, and users are expected to trust the network operator. With ANOTEL we present a novel, clean-slate approach of location management in cellular networks that challenges this assumption. The design is able to route calls to users who move through cellular networks, without violating their location privacy.