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In recent years, location privacy concerns that arise when using the nearest neighbor query services have gained increasing attention, as such services have become pervasive in mobile social networks devices and the IoT environments. State-of-the-art privacy preservation schemes focus on the obfuscation of the location information, which has suffered from various privacy attacks and the tradeoff of the quality of service. By noticing the fact that the user’s location could be replaced by their surrounding wireless sensor infrastructures in proximity, in this paper, we propose a wireless sensor access point-based scheme for the nearest neighbor query, without using the location of the user. Then, a noise-addition-based method that preserves user’s location privacy was proposed. To further strengthen the adaptability of the approach to real-world environments, several performance-enhancing methods are introduced, including an R-tree-based Noise-Data Retrieval Algorithm (RNR), and a nearest neighbor query method based on our research. Both performance and security evaluations are conducted to validate our approach. The results show the effectiveness and the practicality of our work.

With the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network (WSN), which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different. The application of the method opens a new way for traffic flow data collection and analysis.

Many epidemiological studies find that people exposed to aircraft, road or railway traffic noise are at increased risk of illness, including cardiovascular disease (CVD) and depression. It is unclear how the combined exposure to these different types of traffic noise affects disease risks. This study addresses this question with a large secondary data-based case-control study (“NORAH disease risk study”). The Akaike information criterion (AIC) is used to compare two different models estimating the disease risks of combined traffic noise. In comparison with the conventional energetic addition of noise levels, the multiplication of CVD risks as well as depression risks reveals a considerably better model fit as expressed by much lower AIC values. This is also the case when risk differences between different types of traffic noise are taken into account by applying supplements or reductions to the single traffic noise pressure levels in order to identify the best fitting energetic addition model. As a consequence, the conventionally performed energetic addition of noise levels might considerably underestimate the health risks of combined traffic noise. Based on the NORAH disease risk study, “epidemiological risk multiplication” seems to provide a better estimate of the health risks of combined traffic noise exposures compared to energetic addition. If confirmed in further studies, these results should imply consequences for noise protection measures as well as for traffic planning.

Data privacy is a major concern in data mining. Privacy-preserving data mining algorithms have been used for preserving privacy in data mining. However, privacy-preserving data mining on high dimensional continuous data leads to high data loss, information loss and identifying clusters are very difficult. In this paper, a novel technique Anonymized Noise Addition in Subspaces (ANAS) is proposed, which reduces data loss, information loss and enhances identification of clusters and privacy. Anonymization using aggregation is performed in dense and non-dense subspaces considering Euclidean distances to reduce data loss and enhance privacy. Random noise within the subspace limits is then applied to anonymized subspaces to enhance identification of clusters and reduce data loss. ANAS is run on benchmark datasets, and results show that ANAS can identify 80% of the original dataset clusters on sparse datasets, whereas the existing techniques do not identify any clusters. ANAS reduces data loss by 50%, information loss by 20% and enhances privacy by 40%.

To solve the difficulty of balancing privacy and availability in big data privacy protection technology, this study integrates the powerful feature extraction ability of convolutional neural network models with the efficiency of differential privacy technology in data privacy protection. An innovative privacy protection method combining gradient adaptive noise and adaptive step size control is proposed. The experiment findings denote that the research method outperforms existing advanced privacy protection technologies in terms of performance, with an average accuracy of 97.68% and a performance improvement of about 20% to 30%. In addition, for larger privacy budgets, increasing the threshold appropriately can further optimize the effectiveness of research methods. This indicates that through refined noise control and step size adjustment, not only can the privacy protection process be optimized, but also the high efficiency and accuracy of data processing can be maintained. In summary, while ensuring data utility, research methods can not only significantly reduce the risk of privacy breaches, but also optimize privacy protection mechanisms, achieving an ideal balance between protecting personal privacy and maximizing data utility. This innovative approach provides an efficient probability distribution function solution for the field of privacy protection, with the potential to promote further development of related technologies and applications.

Data stream mining is a critical process utilized by organizations to derive insights from real-time data. Consequently, preserving the privacy of sensitive information while maintaining high accuracy remains a persistent challenge. Privacy-preserving data mining techniques modify data to increase privacy, a process that invariably decreases the accuracy of data mining algorithms. Though different techniques have been proposed to preserve privacy, there is a lack of well-formulated frameworks to optimize the trade-off between accuracy and privacy. This paper introduces a novel Accuracy-Privacy Optimization Framework (APOF) that allows users to define privacy requirements and predicts achievable accuracy levels, enabling fine-tuning of this balance. The logistic cumulative noise addition was used as the data perturbation method that has experimentally shown better performance and Hoeffding trees as the classifier. Additionally, a data fitting module using kernel regression is integrated, a unique approach that predicts accuracy levels based on user-defined privacy thresholds. Experimental results show that the proposed framework archives an optimal privacy level above 97% while minimising the accuracy loss across various datasets. By addressing critical gaps in privacy-preserving data mining, this study offers significant contributions to real-world applications, facilitating secure and efficient data utilization in dynamic environments.

In this study, a dynamic stochastic resonance (DSR)-based technique in spatial domain has been proposed for the enhancement of dark- and low-contrast images. Stochastic resonance (SR) is a phenomenon in which the performance of a system (low-contrast image) can be improved by addition of noise. However, in the proposed work, the internal noise of an image has been utilised to produce a noise-induced transition of a dark image from a state of low contrast to that of high contrast. DSR is applied in an iterative fashion by correlating the bistable system parameters of a double-well potential with the intensity values of a low-contrast image. Optimum output is ensured by adaptive computation of performance metrics – relative contrast enhancement factor (F), perceptual quality measures and colour enhancement factor. When compared with the existing enhancement techniques such as adaptive histogram equalisation, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, edge-preserving multi-scale decomposition and automatic controls of popular imaging tools, the proposed technique gives significant performance in terms of contrast and colour enhancement as well as perceptual quality. Comparison with a spatial domain SR-based technique has also been illustrated.

Some inverse problems of Stokes flow, including noisy boundary conditions, unknown angular velocity, and dynamic viscous constant identification are studied in this paper. The interpolation equations for those inverse problems are constructed using the method of fundamental solutions (MFS). Based on the noise addition technique, the inverse problems are solved using MFS and a Kalman filter. It is seen from numerical experiments that these approaches and algorithms are valid and have strong robustness and high accuracy in solving inverse Stokes problems.

There are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks.