Abstract
[No abstract available]

Abstract
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.

Abstract
In this letter, we introduce the computation-limited (comp-limited) signals, a communication capacity regime where the computational complexity of signal processing is the primary constraint for communication performance, overriding factors such as power or bandwidth. We present the Spectro-Computational (SC) analysis, a novel mathematical framework designed to enhance classic concepts of information theory -such as data rate, spectral efficiency, and capacity - to accommodate the computational complexity overhead of signal processing. We explore a specific Shannon regime where capacity is expected to increase indefinitely with channel resources. However, we identify conditions under which the time complexity overhead can cause capacity to decrease rather than increase, leading to the definition of the comp-limited signal regime. Furthermore, we provide examples of SC analysis and demonstrate that the Orthogonal Frequency Division Multiplexing (OFDM) waveform falls under the comp-limited regime unless the lower-bound computational complexity of the N-point Discrete Fourier Transform (DFT) problem verifies as ohm (N)$ , which remains an open challenge in the theory of computation.

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
By leveraging the advances in wireless communications networks and their ubiquitous nature, sensing through communication technologies has flourished in recent years. In particular, Human-to-Machine Interfaces have been exploiting WiFi IEEE 802.11 networks to obtain information that allows Human Activity Recognition. In this paper, we propose a classification model to perform Person Identification (PI) through Body Velocity Profile time series, obtained by combining Channel State Information containing gesture knowledge from multiple Access Points. Through this model, we investigate the impact of different gestures on PI classification performance and explore how informing the model about the input gesture can enhance classification accuracy. This information may enable the network to adjust to the absence of features capable of adequately characterizing the desired classes in certain gestures. A simplified stacking model is also presented, capable of combining the softmax outputs of K previously proposed individual models. By having the individual models' evaluations of a gesture and the gesture information relating to it, the number of gestures considered was shown to significantly improve the performance of the PI classification task. This enhancement increased 17% of the average F1 scores when compared to the individual model tested on the same data.