Evaluating and exploring regional eco-environmental quality (EEQ), economic development equality (EDE) and the coupling coordination degree (CCD) at multiple scales is important for realizing regional sustainable development goals. The CCD can reflect both the development level and the interaction relationship of two or more systems. However, relevant previous studies have ignored non-statistical data, lacked multiscale analyses, misused the coupling coordination degree model or have not sufficiently considered economic development equality. In response to these problems, this study integrated multisource remote sensing datasets to calculate and analyse the remote sensing ecological index (RSEI) and then used nighttime light data and population density data to calculate the proposed nighttime difference index (NTDI). Next, a modified coupling coordination degree (MCCD) index was proposed to analyse the MCCD between EEQ and EDE. Then, spatiotemporal and multiscale analyses at the county, city, province, urban agglomeration and country levels were performed. Global and local spatial autocorrelation and trend analyses were performed to evaluate the spatial aggregation degree and change trends from 2001 to 2020. The main conclusions are as follows: (1) The EEQ of China displayed a fluctuating upwards trend (0.0048 a⁻¹), with average RSEI values of 0.5950, 0.6277, 0.6164, 0.6311 and 0.6173; the EDE of China showed an upwards trend (0.0298 a⁻¹), with average NTDI values of 0.1271, 0.1635, 0.1642, 0.2181 and 0.2490; and China’s MCCD indicated an upwards trend (0.0220 a⁻¹), with values of 0.4614, 0.5027, 0.4978, 0.5401 and 0.5525. (2) The highest global Moran’s I of NTDI and MCCD was achieved at the city scale, while the highest RSEI was achieved at the county scale. From 2001 to 2020, the spatial agglomeration effect of the RSEI decreased, while that of the NTDI and MCCD increased. (3) A power function relationship occurred between NTDI and MCCD at different scales. Furthermore, the NTDI had a higher contribution to improving the MCCD than the RSEI and the R² of the fitted curve at different scales ranged from 0.8183 to 0.9915.

With the development of remote sensing techniques and the increasing need for soil contamination monitoring, we estimated soil heavy metal zinc (Zn) content using hyperspectral imaging. Geographically weighted regression (GWR), an extension of the ordinary least squares (OLS) regression framework, was proposed. By estimating a set of parameters for any number of locations in a study area, GWR can probe the spatial heterogeneity in data relationships, whereas the regression parameters of an OLS model are global and aspatially-varied. The objectives of this study were: (1) To find the possible relationships between hyperspectral data and soil Zn content, and (2) to investigate the existence of their spatial heterogeneity. In this study, 67 soil samples collected from Pingtan Island, Fujian Province, China, were used to conduct laboratory hyperspectral modeling for soil Zn content estimation. Four transformations of square root, logarithm, reciprocal of logarithm, and reciprocal, as well as the fractional-order differential operations were applied to increase the amount of reflectance data in which the effective variables for modeling might be involved, and to enhance the spectral characteristics of soil Zn content. To find sensitive variables and to remove redundancy and multicollinearity in the spectra, a data sifting process was applied by selecting wavelengths with local maximum in the absolute values of the correlation coefficients with Zn content in one type of spectral data and by employing Variance Inflation Factors. Since a modeling sample size of 46 is insufficient to construct the appropriate OLS and GWR models, four methods are proposed using all 67 samples to choose explanatory variables. A random process to select 57 samples for modeling and 10 samples for validation was applied to assess model performance, in which the mean verification R² (R_v²) was used as an indicator. The results show that GWR stepwise regression is the most effective method to select better variables. As the mean R_v² converges toward the OLS value when the bandwidth of the GWR model increases, the four variables selected by the GWR stepwise regression were used to establish the representative OLS and GWR models. The representative OLS model has the best mean verification effect among all studied models, which had a mean R_v² value that is 44.6% higher than the OLS model constructed using OLS stepwise regression.

Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users’ data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user’s privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners’ dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

Device-to-device (D2D) network utilizes various communication methods and regional resource sharing mechanisms, which, despite the efficient and effective communications, may lead to a variety of security threats. Therefore, without the assistance from the base station, effective managing regional resources and protecting private data on mobile devices have become a major challenge in D2D networks. In this paper, we propose a reliability-based central node election mechanism in a D2D network, where the attribute information is collected, normalized, and weight-summed to acquire the reliability of each mobile device. The central node can therefore be elected by sorting the reliability of all mobile devices. Furthermore, we propose a security protection mechanism of private data based on homomorphic encryption in a D2D network, where homomorphic encryption is employed to implement secure data aggregate of ciphertext in the elected central node. Finally, theoretical analyses and simulation experiment verify the superior effectiveness and efficiency of the proposed schemes.

Deep Neural Networks (DNNs) have been extensively employed for automatic face recognition, enabling the extraction of compact and discriminative representations from facial images. However, these representations typically encode a multitude of information ranging from individual identities to sensitive soft-biometric attributes such as gender, race, or age. This raises concerns regarding the privacy disclosure of soft-biometric as these attributes should be protected. To address this issue, we propose a novel Face Information Bottleneck Network (FIBNet), which is a representation-level privacy-enhancing framework based on the Information Bottleneck (IB) principle. The proposed FIBNet differs significantly from previous representation-level privacy-enhancing techniques in three key aspects. First, it generates a privacy-enhanced face representation, providing novel insights through an information-theoretic privacy framework. Second, we formulate the privacy protection of soft-biometric attributes as an IB optimization problem by striking a tradeoff between preserving a controlled amount of identity information within face representations and suppressing soft-biometric attribute information. Last, the proposed approach protects soft-biometric privacy from adversaries interested in specific sensitive attributes that are unknown to the biometric system designers or users. Detailed experimental results obtained on widely recognized facial recognition datasets demonstrate that the proposed FIBNet significantly outperforms the state-of-the-art methods in terms of both biometric performance for face verification and its soft-biometric attribute suppression efficiency. These notable results verify FIBNet as a novel and effective approach for ensuring representation-level soft-biometric privacy.