The advent of the Internet of Things (IoT) has revolutionized the way we, as a society, perform different daily tasks, such as healthcare. The Internet of Health Things (IoHT) is an example of the IoT specialization handling sensitive user data and applications requiring solutions to address different security and privacy issues. IoHT requires security mechanisms in communication. However, these mechanisms need to consider the limitations of the IoHT devices and communication. Hence, symmetric cryptography is suitable to IoHT once it uses less computational and communication resources than asymmetric cryptography. But, symmetric cryptography relies on the agreement of the cryptography material (e.g., the cryptography key) among the devices, a challenge in networks with resource constraints, such as IoHT. Therefore, this article presents a Low memORy symmEtric-key geNerAtion (LORENA) method based on group secret key agreement protocol for IoHT environments. Evaluations have focused on computational efficiency, data security requirements, and scalability in a network with up to ten devices per group using a simulator and a device with limited computational resources. Results show that the protocol is lightweight, secure, and feasible to IoHT networks, presenting a linear growth in the 128-bit key distribution time for each device entering the group.

Human-body communication (HBC) has increasingly gained attention from academia and industry. Most current works focus on characterizing the use of human-body tissues as a physical medium to enable reliable communication. However, designing coupling hardware and communication circuits for reliable data transmission (e.g., high throughput and low latency) is a demanding task, especially for achieving a compact full electronic implementation. For this purpose, there are few commercial devices, mainly differential probes and balun transformers, employed with electrical analysis instruments such as oscilloscopes and vector network analyzers. Although these devices are widely used, they are expensive and are difficult to miniaturize and integrate into real-world HBC-specific applications (e.g., data security). This article presents a low-cost electronic system that transfers collected data using a secondary channel: the ionic environment (the primary channel would be the wireless environment). We design an electronic system as an experimental setup for studying HBC, allowing the communication between instruments, sensors, and actuators by human-body tissues. The experimental evaluation of the proposed system follows (i) a phantom composed of saline (0.9%) and (ii) a real human forearm through adhesive surface electrodes.

Advances in microelectronics and the evolution of IoHT devices allow the collection and, consequently, generation of a greater volume of health data, intensifying the need for robust data privacy solutions. Traditional k-anonymity-based anonymization techniques often suffer from high information loss, especially as the anonymity parameter k increases. To address these challenges, this article proposes Generalization First k-Member Clustering (GFKMC), a novel k-anonymity method that applies early generalization to quasi-identifiers, reducing computational overhead and minimizing information loss. Unlike traditional methods (e.g., Mondrian, Top-Down Greedy (TDG), and Clustering-Based (CB)), GFKMC maintains nearly constant information loss (≈ 25%) across varying k values and better preserves machine learning model performance, especially in low k scenarios. Empirical evaluations demonstrate that GFKMC outperforms baseline methods by significantly minimizing the trade-off between data utility and privacy. Moreover, GFKMC preserves the performance of machine learning models more effectively.