Abstract
The ever-increasing number of interconnected devices in smart environments, i.e., homes and cities, is bolstering the amount of data generated and exchanged. These devices can range from small embedded platforms, such as those included in home appliances, to critical operational systems, such as traffic lights. However, this increasing adoption is raising significant security and privacy concerns. Although some researchers have already solved some of these issues, data privacy still lacks a viable solution, especially when considering a flexible, decentralized approach to avoid a central overseer. One of the biggest challenges regarding privacy is the lack of transparency about how data flows are mediated and regulated as, often, these resources share data with external entities without the users' knowledge. We argue that a novel data-sharing control mechanism is required to properly control users' privacy and their respective Internet of Things (IoT) devices. This work focuses on a middleware layer solution for the IoT devices, which allows the control of the data generated by the device by its owner. The platform places the user as an active participant in the data market, behaving as its own data intermediary for potential consumers by monitoring, controlling, and negotiating the usage of their data.

Abstract
Over the last years, it has become clear that online games are one of the most used applications on the Internet. This increasing popularity has attracted an influx of players, with some of them trying to gain an unfair advantage for economic reasons, e.g., eSports tournaments, through the use of cheats and exploits. From a different perspective, it is of utmost importance to start analyzing attacks from a defensive perspective to create novel mechanisms that can stop such behaviors. In this work, we introduce a novel solution that extends current anti-cheat solutions through Intel SGX. Our solution moves the core cheat detection engine to a secure enclave provided by SGX while making use of a kernel module for the necessary primitives for system-wide protection. With this, we can prevent client-side tampering in both game code and configuration data by creating a trusted execution environment isolated from the hosting operating system. We are making it capable of preventing the attacker from modifying the cheat detection engine and associated game files. This solution blocks known attacks in games such as CS:GO while maintaining the performance, ensuring gameplay integrity and fairness.

Abstract
The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincare plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

Abstract
In this work, we present an optimal mapper for OFDM with index modulation (OFDM-IM). By optimal we mean the mapper achieves the lowest possible asymptotic computational complexity (CC) when the spectral efficiency (SE) gain over OFDM maximizes. We propose the spectro-computational (SC) analysis to capture the trade-off between CC and SE and to demonstrate that an -subcarrier OFDM-IM mapper must run in exact time complexity. We show that an OFDM-IM mapper running faster than such complexity cannot reach the maximal SE whereas one running slower nullifies the mapping throughput for arbitrarily large . We demonstrate our theoretical findings by implementing an open-source library that supports all DSP steps to map/demap an-subcarrier complex frequency-domain OFDM-IM symbol. Our implementation supports different index selector algorithms and is the first to enable the SE maximization while preserving the same time and space asymptotic complexities of the classic OFDM mapper.

Abstract
AbstractLocation 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.

Abstract
In this letter, we demonstrate a mapper that enables all waveforms of OFDM with Index Modulation (OFDM-IM) while preserving polynomial time and space computational complexities. Enabling all OFDM-IM waveforms maximizes the spectral efficiency (SE) gain over the classic OFDM but, as far as we know, the computational overhead of the resulting mapper remains conjectured as prohibitive across the OFDM-IM literature. We show that the largest number of binomial coefficient calculations performed by the original OFDM-IM mapper is polynomial on the number of subcarriers, even under the setup that maximizes the SE gain over OFDM. Also, such coefficients match the entries of the so-called Pascal's triangle (PT). Thus, by assisting the OFDM-IM mapper with a PT table, we show that the maximum SE gain over OFDM can be achieved under polynomial (rather than exponential) time and space complexities.