With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.

This abstract introduces a Next-Gen runtime tool research direction for neural network programming, which was presented in the Programming 2024 event. These runtime revolutionary tools allow users to conduct neural model evaluations and understand neuron behavior in run-time, neural network design, and training processes. By eliminating the need for extensive backend coding, one of the previously introduced runtime simulators called RAVSim, simplified the integration of custom datasets and enabled developers to focus on high-level tasks, accelerating advancements in artificial intelligence (AI) and computational neuroscience. With its innovative features and user-friendly interface, RAVSim supports researchers and developers in using SNNs full potential in diverse applications.

The era of digitization and IoT devices is marked by the constant storage of massive amounts of data. The growing adoption of smart home environments, which use sensors and devices to monitor and control various aspects of daily life, underscores the need for effective privacy and security measures. HE is a technology that enables computations on encrypted data, preserving confidentiality. As a result, researchers have developed methodologies to protect user information, and HE is one of the technologies that make it possible to perform computations directly on encrypted data and produce results using this encrypted information. Thus, this research study compares the performance of three ML models, XGBoost, Random Forest, and Decision Classifier, on a real-world smart home dataset using both with and without FHE. Practical results demonstrate that the Decision Classifier showed remarkable results, maintaining high accuracy with FHE and even surpassing its plaintext performance, suggesting that encryption can enhance model accuracy under certain conditions. Additionally, Random Forest showed efficiency in terms of execution time and low prediction errors with FHE, making it a strong candidate for encrypted data processing in smart homes. These findings highlight the potential of FHE to set new privacy standards, advancing secure and privacy-preserving technologies in smart environments.

In recent years, as urban AI applications increasingly rely on sensitive data, ensuring the privacy and security of machine learning (ML) models has become essential. The proposed research study evaluates the performance and security trade-offs of seven encryption techniques applied to ML models used in urban AI settings. We compare encryption methods, including mixed homomorphic encryptions using Convolutional Neural Networks (CNNs) trained on the MNIST dataset, we analyze how these encryption methods affect model performance in terms of accuracy, error rate, and information leakage. The CNN models, after being trained with encrypted data, are deployed on embedded devices to evaluate real-time performance. We measure key metrics, including execution time, memory usage, and classification accuracy, to assess the feasibility of each encryption method in urban AI scenarios. Additionally, the impact of encryption on model interpretability and robustness is considered, particularly when used in urban applications like intelligent transportation systems, smart city sensors, and surveillance systems. By evaluating error rates, mutual information scores, and statistical properties such as mean and variance, this research aims to explore the practical trade-offs between security, privacy, and performance. Our findings highlight the importance of selecting appropriate encryption techniques for urban AI tasks to maintain both data privacy and model efficiency in real-world settings.

Early detection of neurodegenerative diseases, such as Alzheimers and Parkinsons, is essential for timely intervention, which can improve patients' quality of life and slow down disease progression. Traditional diagnostic methods rely heavily on clinical tests, which can be infrequent and may not capture slight behavioral changes that indicate early cognitive or motor decline. This work presents a novel approach using smart home data to detect early signs of neurodegeneration through continuous monitoring of sleep patterns and daily activity routines. In a smart home environment, sensors passively monitor daily routines, sleep quality, and mobility patterns of the elderly persons. This paper introduces a novel framework combining the Sleep Deviation Patterns (SDP) and the Weighted Activity Deviation Index (WADI) to comprehensively analyze deviations in sleep and daily routines. The SDP framework captures deviations in sleep onset, duration, interruptions, and consistency using metrics such as Sleep Onset Deviation, Sleep Duration Deviation, Sleep Interruption Index, and Sleep Consistency Index–aggregated into a weighted Sleep Deviation Score. WADI quantifies deviations in daily activities by computing weighted absolute deviations of activity proportions relative to a reference routine. Thus, we applied our framework to real-world smart home datasets (TM001-TM004) from the CASAS project, which include labeled activity data from both single- and multi-resident households. Experimental findings reveal a distinct stratification: TM001 and TM002 exhibited Low WADI and Low SDP with an average of >0.015 − >0.2 scores, suggesting stable routines, whereas TM003 and TM004 demonstrated elevated with an average of >0.03 − >0.4 values, indicating disrupted behaviors. In TM004, up to 28% of days were flagged as anomalous, correlating with patterns consistent with early neurodegeneration such as fragmented sleep and disorganized activity routines. Finally, experimental results demonstrate that the combined SDP and WADI frameworks effectively identify irregularities in sleep and activity patterns on real-world datasets. The proposed approach offers a robust and scalable solution for health monitoring, with potential applications in neurodegenerative disease detection, personalized healthcare, and smart home systems.