Audit logs are a critical component in today’s enterprise business systems as they provide several benefits such as records transparency and integrity and security of sensitive information by creating a layer of evidential support. However, current implementations are vulnerable to attacks on data integrity or availability. This paper presents a Blockchain-based audit trail mechanism that leverages the security features of Blockchain to enable secure and reliable audit trails and to address the aforementioned vulnerabilities. The architecture design and specific implementation are described in detail, resulting in a real prototype of a reliable, secure, and user-friendly audit trail mechanism.

This paper presents a generic methodology to optimize constellations based on their geometrical shaping for bit-interleaved coded modulation (BICM) systems. While the method can be applicable to any wireless standard design it has been tailored to two delivery scenarios typical of broadcast systems: 1) robust multimedia delivery and 2) UHDTV quality bitrate services. The design process is based on maximizing the BICM channel capacity for a given power constraint. The major contribution of this paper is a low complexity optimization algorithm for the design of optimal constellation schemes. The proposal consists of a set of initial conditions for a particle swarm optimization algorithm, and afterward, a customized post processing procedure for further improving the constellation alphabet. According to the broadcast application cases, the sizes of the constellations proposed range from 16 to 4096 symbols. The BICM channel capacities and performance of the designed constellations are compared to conventional quadrature amplitude modulation constellations for different application scenarios. The results show a significant improvement in terms of system performance and BICM channel capacities under additive white Gaussian noise and Rayleigh independently and identically distributed channel conditions.

In the era of Big Data and the advancement of the Internet of Things, there is an increasing amount of valuable information. It is important to emphasize that this data is usually sensitive or confidential, so security and privacy are two of the highest priorities for organizations when performing Data Mining. Researchers have explored techniques such as secure multi-party computation (SMPC) in the last decades. Nevertheless, there is still a significant gap between the theory of SMPC and its applicability, especially when working with resource-constrained devices or massive data. This work has been conducted with a systematic literature review, and it intends to analyze the open issues of adapting SMPC to those scenarios, by classifying the studies to answer two research questions: (1) how has the use of SMPC attempted to be adapted to constrained devices? and (2) how have traditional techniques fitted with Big Data? At the end of the process, after analyzing a total of 637 studies, 19 papers were selected. Regarding constrained devices, solutions are grouped into three main techniques: secure outsourcing, hardware-based trusted execution, and intermediate representations. As for Big Data, the selected studies use mixed protocols to change over cleartext and ciphertext, combine different types of SMPC protocols, or modify existing protocols through optimizations.

This paper presents the field trials carried out in Bilbao (Spain) for testing and analyzing the performance of the DVB-T2 indoor (fixed and pedestrian) reception. Test services with different DVB-T2 and T2-Lite configuration modes have been broadcasted and indoor measurements have been carried out. As a result, signal to noise ratio threshold values for DVB-T2 indoor reception have been obtained. These values update those provided by previous studies and are also valid for planning tools to be used by broadcasters in the development of DVB-T2 networks.