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Abstract
Quasi-identifiers (QIDs) are attributes in a dataset that are not directly unique identifiers of the users/entities themselves but can be used, often in conjunction with other datasets or information, to identify individuals and thus present a privacy risk in data sharing and analysis. Identifying QIDs is important in developing proper strategies for anonymization and data sanitization. This paper proposes QIDLEARNINGLIB, a Python library that offers a set of metrics and tools to measure the qualities of QIDs and identify them in data sets. It incorporates metrics from different domains-causality, privacy, data utility, and performance-to offer a holistic assessment of the properties of attributes in a given tabular dataset. Furthermore, QIDLEARNINGLIB offers visual analysis tools to present how these metrics shift over a dataset and implements an extensible framework that employs multiple optimization algorithms such as an evolutionary algorithm, simulated annealing, and greedy search using these metrics to identify a meaningful set of QIDs.

Abstract
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Abstract
Joint Communication and Sensing (JCAS) systems are emerging as a core technology for next-generation wireless systems due to the potential to achieve higher spectral efficiency, energy savings, and new services beyond communications. This paper provides a review of the state-of-the-art in JCAS systems by focusing on obtrusive passive sensing capabilities and inherent security and privacy challenges that arise from the integration of communication and sensing. From this point of view, we discuss existing techniques for mitigating security and privacy issues, as well as important aspects for the designing of secure and privacy-aware JCAS systems. Additionally, we discuss future research directions by emphasizing on new enabling technologies and their integration on JCAS systems along with their role in privacy and security aspects. We also discuss the required modifications to existing systems and the design of new systems with privacy and security awareness, where the challenging trade-offs between security, privacy and performance of the JCAS system must be considered.

Abstract
Location privacy is a major concern in the current digital society, due to the sensitive information that can be inferred from location data. This has led smartphones' Operating Systems (OSs) to strongly tighten access to location information in the last few years. The same tightening has, however, not yet happened when it comes to our second most carried around device: the laptop. In this work, we demonstrate the privacy risks resulting from the fact that major laptop OSs still expose WiFi data to installed software, thus enabling to infer location information from WiFi Access Points (APs). Using data collected in a real-world experiment, we show that laptops are often carried along with smartphones and that a large fraction of our mobility profile can be inferred from WiFi APs accessed on laptops, thus concluding on the need to protect the access to WiFi data on laptops.