Abstract
In an era dominated by Location-Based Services (LBSs), the concern of preserving location privacy has emerged as a critical challenge. To address this, Location Privacy-Preserving Mechanisms (LPPMs) were proposed, in where an obfuscated version of the exact user location is reported instead. Adding to noise-based mechanisms, location discretization, the process of transforming continuous location data into discrete representations, is relevant for the efficient storage of data, simplifying the process of manipulating the information in a digital system and reducing the computational overhead. Apart from enabling a more efficient data storage and processing, location discretization can also be performed with privacy requirements, so as to ensure discretization while providing privacy benefits. In this work, we propose a Privacy-Aware Remapping mechanism that is able to improve the privacy level attained by Geo-Indistinguishability through a tailored pre-processing discretization step. The proposed remapping technique is capable of reducing the re-identification risk of locations under Geo-Indistinguishability, with limited impact on quality loss.

Abstract
Due to the pervasiveness of Interconnected devices, large amounts of heterogeneous data types are being continuously collected. Regardless of the benefits that come from sharing data, exposing sensitive and private information arises serious privacy concerns. To prevent unwanted disclosures and, hence, to protect users' privacy, several privacy-preserving mechanisms have been proposed. However, the data heterogeneity and the inherent correlations among the different data types have been disregarded when developing such mechanisms. Our goal is to develop privacy-preserving mechanisms that are suitable for data heterogeneity and data correlation. These aspects will also be considered to develop mechanisms to achieve private learning. © 2021 Owner/Author.

Abstract
Service applications are increasingly being deployed in virtualized environments, such as virtual machines (VMs) as a means to provide elasticity and to allow fast recovery from failures. The recent trend is now to deploy applications in containers (e.g., Docker or RKT containers), which allow, among many other benefits, to further reduce recovery time, since containers are much more lightweight than VMs. Although several performance benchmarks exist for web services (e.g., TPC-App and SPEC SPECjEnterprise2010) or even virtualized environments (e.g., SPEC Cloud IaaS 2016, TPCx-V), understanding the behavior of containerized services in the presence of faults has been generally disregarded. This paper proposes an experimental approach for evaluating the performance of containerized services in presence of operator faults. The approach is based on the injection of a simple set of operator faults targeting the containers and middleware. Results show noticeable differences regarding the impact of operator faults in Docker and RKT, with the latter one allowing for faster recovery, despite showing the lowest throughput.

Abstract
Location Privacy-Preserving Mechanisms (LPPMs) have been proposed to mitigate the risks of privacy disclosure yielded from location sharing. However, due to the nature of this type of data, spatio-temporal correlations can be leveraged by an adversary to extenuate the protections. Moreover, the application of LPPMs at collection time has been limited due to the difficulty in configuring the parameters and in understanding their impact on the privacy level by the end-user. In this work we adopt the velocity of the user and the frequency of reports as a metric for the correlation between location reports. Based on such metric we propose a generalization of Geo-Indistinguishability denoted Velocity-Aware Geo-Indistinguishability (VA-GI). We define a VA-GI LPPM that provides an automatic and dynamic trade-off between privacy and utility according to the velocity of the user and the frequency of reports. This adaptability can be tuned for general use, by using city or country-wide data, or for specific user profiles, thus warranting fine-grained tuning for users or environments. Our results using vehicular trajectory data show that VA-GI achieves a dynamic trade-off between privacy and utility that outperforms previous works. Additionally, by using a Gaussian distribution as estimation for the distribution of the velocities, we provide a methodology for configuring our proposed LPPM without the need for mobility data. This approach provides the required privacy-utility adaptability while also simplifying its configuration and general application in different contexts.

Abstract
We consider privacy of obfuscated location reports that can be correlated through time/space to estimate the real position of a user. We propose a user-centric Location Privacy Preserving Mechanism (LPPM) that protects users not only against single reports, but also over time, against continuous reports. Our proposed mechanism, designated clustering geo-indistinguishability, creates obfuscation clusters to aggregate nearby locations into a single obfuscated location. To evaluate the utility of the mechanism, we resorted to a real use-case based on geofencing. Our evaluation results have shown a suitable privacy-utility trade-off for the proposed clustering geo-indistinguishability mechanism.