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. Traditionalk-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 Firstk-Member Clustering (GFKMC), a novelk-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 varyingkvalues and better preserves machine learning model performance, especially in lowkscenarios. 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.

The increasing volume of sensitive data generated by various domains demands robust approaches to privacy protection. Anonymization based on k-anonymity stands out for mitigating the risks of re-identification of personal data. However, the impact on the performance of machine learning models is commonly neglected. This work proposes a novel comparative method to evaluate the effects of anonymization on the performance of machine learning models, considering privacy, information loss, and performance metrics. The empirical results show how generalization based on k-anonymity impacts federated learning solutions and provides insights for developing and improving methods that reconcile data privacy and efficiency.

The growing prevalence of interconnected devices in the Internet of Things (IoT) has intensified concerns about data security and user authentication. This study investigates a multimodal biometric authentication approach integrating Photoplethysmography (PPG) and Electroencephalography (EEG) signals to enhance accuracy and robustness. We adapted a previously validated PPG and Electrocardiogram (ECG) model, replacing the ECG component with an EEG-based method optimized through hyperparameter tuning. Our findings demonstrate that combining brain and heart signals improves authentication performance, with the multimodal approach surpassing unimodal methods. The optimized model achieved a precision of $97.05 \%$, a recall of $97.19 \%$, a F1-Score of $96.96 \%$, and an accuracy of $97.05 \%$, highlighting the potential of EEG-PPG fusion in secure biometric authentication.

Technological advances enable the integration of Internet of Things (IoT) devices to perform continuous and proactive patient monitoring. These devices collect a large volume of sensitive data that requires privacy. Anonymization provides privacy by removing or modifying information that identifies an individual. However, traditional anonymization techniques, such as k-anonymity, depend on a fixed and pre-defined k value, susceptible to attribute disclosure attacks. This article presents Dynamic Anonymization by Separatrices (DAS), an approach for defining the ideal value k and for dynamic grouping of data to be anonymized using separatrices measurements. Results show that the proposed approach efficiently mitigates attribute disclosure attacks.

In an increasingly connected world, ensuring security in e-health is a challenge. Traditional security models based on perimeter trust are insufficient to guarantee the protection of these systems. Since these models work by directly assigning trust to the user, the entire network becomes vulnerable if the user’s credentials or device are compromised. Thus, this work proposes and evaluates a model based on Zero Trust to considerably increase security in e-health environments. The proposed model uses privilege reduction and user confidence analysis to perform access control. The evaluation follows simulation in different scenarios, assessing their assertiveness in delegating access. The results show the effective detection of anomalies in accesses by the model.

Summary Dataflow‐based FPGA accelerators have become a promising alternative to deliver energy‐efficient high‐performance computing. However, FPGA programming is still a challenge. This paper presents Accelerator Design and Deploy (ADD), a high‐level framework to specify, to simulate, and to implement dataflow accelerators for streaming applications. The framework includes an open dataflow operator library, and templates are provided to easily design new operators. The framework also provides a high‐level and an accurate simulation at circuit level with short execution times. Moreover, ADD provides software and hardware APIs to simplify the integration process, extending the benefits of portability from low‐cost FPGA boards to high performance datacenter FPGA platforms. Our framework supports coupling with high‐level programming languages, and it has been validated on two FPGA platforms: the Intel high‐performance CPU‐FPGA heterogeneous computing platform and an educational FPGA kit. We show that our simple approach presents competitive performance, both in time and energy, when compared to multi‐core and GPU accelerators.