This study explores how the environmental conditions of sustainable classrooms influence the concentration and academic performance of students in university environments, integrating the use of sustainable design strategies. Within the framework of education for sustainability, the importance of sustainable urban spaces, buildings, interiors, and green infrastructure products in communicating and promoting scientific and environmental knowledge is recognized. Using a systematic mapping methodology, the research examines how real-time monitoring of environmental variables such as air quality, humidity, temperature, CO₂, particulate matter, and lighting, through the Internet of Things (IoT), can enhance learning. The study focuses on examining the impact of environmental factors on students’ academic performance, as well as exploring how sustainable educational spaces can promote greater awareness and favorable attitudes towards the environment. Based on an analysis of 454 articles and success stories on green educational infrastructure projects, the results reveal a significant correlation between optimal environmental conditions such as good ventilation and temperature control and increased student concentration and performance. This study also highlights the role of educational interventions, both formal and informal, that integrate sustainably built environments to reinforce occupants’ environmental engagement. The conclusion is clear: improving classroom environmental conditions, especially in terms of ventilation and temperature control, not only optimizes learning, but also acts as a powerful environmental education tool, fostering education for sustainable development and strengthening ecological attitudes among students.

We introduce an innovative approach for secure communication in the Internet of Things (IoT) environment using a one-time pad (OTP) protocol. This protocol is augmented by incorporating a secure multiparty sum protocol to produce OTP keys from genuine random numbers obtained from the physical phenomena observed in each device. We have implemented our method using ZeroC-Ice v.3.7, dependable middleware for distributed computing, demonstrating its practicality in various hybrid IoT scenarios, particularly in devices with limited processing capabilities. The security features of our protocol are evaluated under the Dolev–Yao threat model, providing a thorough assessment of its defense against potential cyber threats.

The process of establishing secure communication between devices in the Internet of Things (IoT) can be approached by using a One-time-Pad (OTP) protocol. We propose using a known secure multiparty sum protocol for generating a One-time-Pad key using true (physical) random numbers in each device (party). We implemented the proposal using ZeroC-Ice, a middleware for distributed computing. The protocol security properties were analyzed under the assumptions of the Dolev-Yao threat model.

Privacy-preserving secure multi-party computation protocols are known to face scalability and efficiency challenges in environments where participants hold distinct attributes of the same records (vertical partitioning) or controls a subset of complete records (horizontal partitioning), as in cross-institutional health data analysis or federated IoT analytics, mostly because of communication overhead and the need to address adaptability to large scale or heterogeneous settings. This work introduces a novel MPC protocol based on the Damgård–Jurik cryptosystem and Schnorr zero-knowledge proofs (ZKP), designed to securely aggregate private data distributed across a number of parties. By combining homomorphic encryption with non-interactive ZKP’s, the protocol ensures privacy, correctness, and scalability, aligning with the principles of privacy-enhancing technologies (PETs). Our approach minimizes data exposure, allowing participants to audit results, and achieves linear $O\left(N\right)$ communication complexity, thus making it suitable for large-scale applications in secure data analytics and collaborative computing.

This paper introduces a secure sum protocol using the Damgrd-Jurik cryptosystem, implemented and evaluated within an experimental setup that considers both vertical and horizontal partitioning. The proposed protocol offers significant improvements in privacy and efficiency due to its homomorphic properties and the conceptual enhancement of the protocol, where only a single piece of data needs to be encrypted per party, since the sum of each party’s data is performed locally. Furthermore, the practical feasibility of this protocol is reinforced by the proof-of-concept and performance evaluation, demonstrating its effectiveness and efficiency in handling large datasets. The findings underscore the protocol’s ability to maintain data privacy through encryption and ensure result integrity via Non-Interactive Zero-Knowledge Proofs (NIZK). The results and evaluations presented herein support its potential practical applicability.