With the massive data collection from different devices, spanning from mobile devices to all sorts of IoT devices, protecting the privacy of users is a fundamental concern. In order to prevent unwanted disclosures, several Privacy-Preserving Mechanisms (PPMs) have been proposed. Nevertheless, due to the lack of a standardized and universal privacy definition, configuring and evaluating PPMs is quite challenging, requiring knowledge that the average user does not have. In this paper, we propose a privacy toolkit - Privkit - to systematize this process and facilitate automated configuration of PPMs. Privkit enables the assessment of privacy-preserving mechanisms with different configurations, while allowing the quantification of the achieved privacy and utility level of various types of data. Privkit is open source and can be extended with new data types, corresponding PPMs, as well as privacy and utility assessment metrics and privacy attacks over such data. This toolkit is available through a Python Package with several state-of-the-art PPMs already implemented, and also accessible through a Web application. Privkit constitutes a unified toolkit that makes the dissemination of new privacy-preserving methods easier and also facilitates reproducibility of research results, through a repository of Jupyter Notebooks that enable reproduction of research results.

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.

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.

Abstract Location privacy has became an emerging topic due to the pervasiveness of Location-Based Services (LBSs). When sharing location, a certain degree of privacy can be achieved through the use of Location Privacy-Preserving Mechanisms (LPPMs), in where an obfuscated version of the exact user location is reported instead. However, even obfuscated location reports disclose information which poses a risk to privacy. Based on the formal notion of differential privacy, Geo-indistinguishability has been proposed to design LPPMs that limit the amount of information that is disclosed to a potential adversary observing the reports. While promising, this notion considers reports to be independent from each other, thus discarding the potential threat that arises from exploring the correlation between reports. This assumption might hold for the sporadic release of data, however, there is still no formal nor quantitative boundary between sporadic and continuous reports and thus we argue that the consideration of independence is valid depending on the frequency of reports made by the user. This work intends to fill this research gap through a quantitative evaluation of the impact on the privacy level of Geo-indistinguishability under different frequency of reports. Towards this end, state-of-the-art localization attacks and a tracking attack are implemented against a Geo-indistinguishable LPPM under several values of privacy budget and the privacy level is measured along different frequencies of updates using real mobility data.