With the advent of cloud computing, individuals and organizations have become interested in moving their databases from local to remote cloud servers. However, data owners and cloud service providers are not in the same trusted domain in practice. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective database utilization a very challenging task. To address this challenge, in this paper, we propose L-EncDB, a novel lightweight encryption mechanism for database, which i) keeps the database structure, and ii) supports efficient SQL-based queries. To achieve this goal, a new format-preserving encryption (FPE) scheme is constructed in this paper, which can be used to encrypt all types of character strings stored in database. Extensive analysis demonstrates that the proposed L-EncDB scheme is highly efficient and provably secure under existing security model.

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Recently, compressive hyperspectral imaging (CHI) has received increasing interests, which can recover a large range of scenes with a small number of sensors via compressed sensing (CS) theory. However, most of the available CHI methods separate and vectorize hyperspectral cubes into spatial and spectral vectors, which will result in heavy computational and storage burden in the recovery. Moreover, the complexity of real scene makes the sparsifying difficult and thus requires more measurements to achieve accurate recovery. In this paper, these two issues are addressed, and a new CHI approach via sparse tensors and nonlinear CS (NCS) is advanced for accurate maintenance of image structure with limited number of sensors. Based on a multidimensional multiplexing (MDMP) CS scheme, the observed measurements are denoted as tensors and a nonlinear sparse tensor coding is adopted, to develop a new tensor-NCS (T-NCS) algorithm for noniterative recovery of hyperspectral images. Moreover, two recovery schemes are advanced for T-NCS, including example-aided and self-learning CHI approaches. Finally, some experiments are performed on three real hyperspectral data sets to investigate the performance of T-NCS, and the results demonstrate its efficiency and superiority to the counterparts.Index Terms-Compressive hyperspectral imaging (CHI), joint spatial-spectral, multidimensional multiplexing (MDMP), nonlinear compressed sensing (NCS), sparse tensor.