Preventing Location-Based Identity Inference in Anonymous Spatial Queries

The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of Location-Based Services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to Location-Based Services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users

Anonymizing cybersecurity data in critical infrastructures: the CIPSEC approach

Cybersecurity logs are permanently generated by network devices to describe security incidents. With modern computing technology, such logs can be exploited to counter threats in real time or before they gain a foothold. To improve these capabilities, logs are usually shared with external entities. However, since cybersecurity logs might contain sensitive data, serious privacy concerns arise, even more when critical infrastructures (CI), handling strategic data, are involved. We propose a tool to protect privacy by anonymizing sensitive data included in cybersecurity logs. We implement anonymization mechanisms grouped through the definition of a privacy policy. We adapt said approach to the context of the EU project CIPSEC that builds a unified security framework to orchestrate security products, thus offering better protection to a group of CIs. Since this framework collects and processes security-related data from multiple devices of CIs, our work is devoted to protecting privacy by integrating our anonymization approach.Peer ReviewedPostprint (published version

Towards trajectory anonymization: a generalization-based approach

Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques

Time Distortion Anonymization for the Publication of Mobility Data with High Utility

An increasing amount of mobility data is being collected every day by different means, such as mobile applications or crowd-sensing campaigns. This data is sometimes published after the application of simple anonymization techniques (e.g., putting an identifier instead of the users' names), which might lead to severe threats to the privacy of the participating users. Literature contains more sophisticated anonymization techniques, often based on adding noise to the spatial data. However, these techniques either compromise the privacy if the added noise is too little or the utility of the data if the added noise is too strong. We investigate in this paper an alternative solution, which builds on time distortion instead of spatial distortion. Specifically, our contribution lies in (1) the introduction of the concept of time distortion to anonymize mobility datasets (2) Promesse, a protection mechanism implementing this concept (3) a practical study of Promesse compared to two representative spatial distortion mechanisms, namely Wait For Me, which enforces k-anonymity, and Geo-Indistinguishability, which enforces differential privacy. We evaluate our mechanism practically using three real-life datasets. Our results show that time distortion reduces the number of points of interest that can be retrieved by an adversary to under 3 %, while the introduced spatial error is almost null and the distortion introduced on the results of range queries is kept under 13 % on average.Comment: in 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Aug 2015, Helsinki, Finlan