Existing ciphertext-policy attribute-based encryption (CP-ABE) schemes primarily determine who is authorized to decrypt, yet they do not guarantee privacy once ciphertexts are decrypted. Differential privacy (DP) protects released results through noise perturbation, but its privacy budget ε is usually configured independently of access attributes, which hinders fine-grained multi-level privacy protection in hierarchical IoT data sharing. To bridge this gap, a single-encryption multi-level output framework is proposed, where an attribute-driven noise key derivation function establishes a chained mapping among access attributes, noise keys, and noise intensity, enabling one ciphertext to yield differently perturbed outputs at distinct ε levels for users with varying privileges. Authenticated encryption with associated data (AEAD) is further incorporated to enforce strong cross-version and cross-policy binding, preventing low-noise outputs from being forged or replayed across authorization levels. Theoretical analysis proves that the framework achieves IND-CPA confidentiality, ε-differential privacy and tamper-resistant noise binding, while experiments demonstrate superior privacy–utility trade-offs, multi-level adaptability, and tamper resistance, indicating that the framework is well suited for practical IoT data sharing scenarios.

Inner product functional encryption (IPFE) offers strong privacy protection for smart devices by outputting only the results of function computations, minimizing data leakage. This makes it well-suited for privacy-preserving operations in edge cloud environments, however, the edge cloud increases the complexity of the information authorization system, and traditional static authorization methods hinder collaboration between smart devices. Moreover, existing IPFE schemes fail to revoke computational authorizations when a smart device switches edge clouds. To address these challenges, we propose the two-step authentication IPFE scheme (TSA-IPFE), specifically designed for the three-tier “cloud-edge cloud-smart device” architecture. TSA-IPFE enables dynamic authorization management through a two-step identity-matching process. This process revokes computational authorizations as devices move between edge clouds. It also facilitates dynamic authorization by setting up identity validation processes at different network locations, enabling seamless collaboration between smart devices from different vendors within the same cloud architecture. Finally, we prove the semantic security and anonymity of TSA-IPFE through a simulation-based proof, utilizing indistinguishable transformations in the dual system, a comparison with other algorithms demonstrates the efficiency of TSA-IPFE on a common platform.

Fractional vegetation cover (FVC) is a critical component of ecosystems, global climate change and the carbon cycle. Several FVC products have been released, the most widely used of which are the GLASS FVC products (including the GLASS-MODIS and GLASS-AVHRR FVC products). Specifically, the GLASS-MODIS FVC product covers the period from 2000 to present with a 500 m spatial resolution, whereas the GLASS-AVHRR FVC product is available from 1982 to present with a coarser spatial resolution of 5 km. For local monitoring of patterns of change in vegetation, however, there is a great need for fine spatial resolution (e.g., 500 m in this paper) and long-term time-series FVC datasets. To this end, we proposed to reconstruct a 500 m, 8-day historical MODIS FVC dataset (1982–2000) by making full use of the advantages of the existing GLASS-MODIS FVC (fine spatial resolution of 500 m) and GLASS-AVHRR FVC (long-term coverage from 1982 to the present) products covering China in this paper. The known GLASS-AVHRR FVC product was first used to fit the relationship between the FVC data after 2000 and before 2000, based on a random forest (RF) model. The trained relationship was migrated to the GLASS-MODIS FVC product, that is, predicting the MODIS FVC before 2000 based on the input of MODIS FVC after 2000. The validation using 64 scenes of Landsat FVC reference data revealed that the predicted historical MODIS FVC dataset has a reliable accuracy with a correlation coefficient (CC) value of 0.84, root mean square error (RMSE) of 0.14, Bias of 0.04 and unbiased RMSE (ubRMSE) of 0.12. Moreover, an accuracy evaluation in seven different regions in 1999 suggested that the historical MODIS FVC is closer to the Landsat FVC than the GEOV2 FVC product. Overall, the 500 m, 8-day MODIS FVC dataset (1982–2000) in China can provide important historical data for long-term, local monitoring of vegetation, which has great potential in supporting studies in a range of applications areas including ecology, hydrology and climatology. This dataset is available at https://doi.org/10.6084/m9.figshare.24616446.v1.

Soil moisture (SM) plays a significant role in many natural and anthropogenic systems. Thus, accurate assessment of changes in SM globally is of great value, including long-term historical assessment. The European Space Agency established the Climate Change Initiative (CCI) program to produce long time-series surface SM datasets starting from 1978 to the present. However, the Soil Moisture Active Passive (SMAP) mission, launched in 2015, has shown more satisfactory performance in both spatial accuracy and in capturing the pattern of temporal changes. In this paper, a random forest (RF) model was proposed to extend the SMAP dataset historically (named Hist_SMAP), using the corresponding CCI SM time-series. We assumed that the temporal changes in the SMAP SM dataset are similar generally to those in the available CCI dataset. Accordingly, the RF model was constructed using the temporal (extracted from the CCI SM data), coupled with terrain and location characteristics, and migrated to predict the Hist_SMAP dataset. The availablein-situand the real SMAP data were used as references for validation. Compared with the CCI dataset, the predicted Hist_SMAP dataset is closer to thein-situSM data and the real SMAP data. Moreover, the historical Hist_SMAP dataset is more accurate than the widely used Global Land Evaporation Amsterdam Model (GLEAM) dataset. Thus, the Hist_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset. The new long time-series Hist_SMAP dataset is provided with free access and will be of great value for research and practical application in a range of fields.

Access control schemes in predicate encryption can effectively reduce the risk of information leakage in the inner product function encryption (IPFE). However, when we try to transition from identity-based IPFE to attribute-based IPFE, the fine-grained nature of attributes induces some unprecedented access control problems. These problems not only lead to incorrect inner product computation results from attribute-based IPFE but also inevitable passive information leakage induced by large attribute set secret key. At the same time, since the above problems are not common in identity-based IPFE and traditional ABEs, they cannot be properly addressed by existing schemes. To address the above problems, we introduce a new scheme - Ciphertext Policy Based Inner Product Function Encryption (CP-IPFE). In this scheme, we propose to use a "label vector" to label the attributes of n-dimensional vectors and encode them onto the relevant information, so as to ensure that the attribute-based IPFE will not output incorrect inner-product computation results; establishing a leaf node set-based "reverse access control policy" to realize reverse access control on "risky ciphertext", ensures that "risky ciphertext" will not be leaked encrypted-information by the secret key of large attribute sets. In addition, CP-IPFE also has the characteristics of traditional attribute encryption and supports more fine-grained access control. Finally, we prove the CPA security of the CP-IPFE in the GGM model and show a detailed application of the CP-IPFE on a general-purpose platform.

Laplacian Embedding (LE) is a powerful method to reveal the intrinsic geometry of high-dimensional data by using graphs. Imposing the orthogonal and nonnegative constraints onto the LE objective has proved to be effective to avoid degenerate and negative solutions, which, though, are challenging to achieve simultaneously because they are nonlinear and nonconvex. In addition, recent studies have shown that using the p-th order of the L2-norm distances in LE can find the best solution for clustering and promote the robustness of the embedding model against outliers, although this makes the optimization objective nonsmooth and difficult to efficiently solve in general. In this work, we study LE that uses the p-th order of the L2-norm distances and satisfies both orthogonal and nonnegative constraints. We introduce a novel smoothed iterative reweighted method to tackle this challenging optimization problem and rigorously analyze its convergence. We demonstrate the effectiveness and potential of our proposed method by extensive empirical studies on both synthetic and real data sets.