Relational databases are widely applied in various industries, such as government departments, medical institutions and enterprises, for data storage and relationship management. It is convenience to data maintenance, however, the data in it maybe vulnerable to be forged or tampered with. Therefore, protecting the copyright of relational databases is a critical issue. Fortunately, watermarking technology can be used to prove copyright ownership. Since the digital watermarking technology can solve this problem effectively, based on semantic, we propose two watermarking approaches with high security and strong robustness for numeric and non-numeric data of relational databases in this paper. On the one hand, we propose a reversible numeric watermarking approach. It performs the replacement operation at the semantic level and retain the statistical characteristics of the data. On the other hand, based on word segmentation and word embedding, the non-numerical attributes of the relational database with natural language are chosen to embed watermark. It is noteworthy that the proposed mechanism can be applied to both Chinese and English with minimum distortion. Additionally, we also propose the virtual splitting of attribute column and the principle of modification minimum to guarantee the capacity of watermark and reduce the data modification rate. Additionally, the BCH (31,16,7) error control code is added to the binary watermark string to improve the detection rate of watermark. Based on the above innovations, the security of watermarking algorithm is improved successfully by double encryption (chaos encryption and hash encryption) with two keys (the user's private key and the attribute column key). The simulation results demonstrate that the proposed two algorithms in this paper have stronger robustness on defensing malicious attacks than previous works.

The opportunistic routing has great advantages on improving packet delivery probability (P _sc ) between the source node and candidate forwarding set. For improving P _sc and reducing energy consumption and network interference, in this paper, we propose an efficient and reliable transmission power control-based opportunistic routing (ERTO) for wireless ad hoc networks. In ERTO, the network interference and fading channel, which are critical to routing performance but not considered in previous works, are investigated during the prediction of P _sc . The P _sc , the expected energy consumption, and the relationship between transmission power and node degree are applied to optimize the transmission power and forwarding node degree jointly. For improving routing effectiveness and reducing computational complexity, we introduce multi-objective optimization and Pareto optimal into ERTO. During the routing process, nodes calculate the optimal transmission power and forwarding node degree according to the properties of Pareto optimal solution set. Based on these innovations, the energy consumption, the transmission delay, and the network throughput have been improved greatly in ERTO compared with previous work. For instance, when the number of nodes is 120, the network throughput of ERTO is 1.41, 1.42, and 2.63 times larger than that in EEOR, TCOR, and ExOR, respectively.

Federated Learning ensures that clients can collaboratively train a global model by uploading local gradients, keeping data locally, and preserving the security of sensitive data. However, studies have shown that attackers can infer local data from gradients, raising the urgent need for gradient protection. The differential privacy technique protects local gradients by adding noise. This paper proposes a federated privacy-enhancing algorithm that combines local differential privacy, parameter sparsification, and weighted aggregation for cross-silo setting. Firstly, our method introduces Renyi differential privacy by ´ adding noise before uploading local parameters, achieving local differential privacy. Moreover, we dynamically adjust the privacy budget to control the amount of noise added, balancing privacy and accuracy. Secondly, considering the diversity of clients’ communication abilities, we propose a novel Top-K method with dynamically adjusted parameter upload rates to effectively reduce and properly allocate communication costs. Finally, based on the data volume, trustworthiness, and upload rates of participants, we employ a weighted aggregation method, which enhance the robustness of the privacy framework. Through experiments, we validate the effective trade-off among privacy, accuracy, communication costs and robustness achieved by the proposed method.

In contrast to images taken on land scenes, images taken over water are more prone to degradation due to the influence of the haze. However, existing image dehazing methods are mainly developed for land-scene images and perform poorly when applied to overwater images. To address this problem, we collect the first overwater image dehazing dataset and propose a Generative Adversial Network (GAN)-based method called OverWater Image Dehazing GAN (OWI-DehazeGAN). Due to the difficulties of collecting paired hazy and clean images, the dataset contains unpaired hazy and clean images taken over water. The proposed OWI-DehazeGAN is composed of an encoder–decoder framework, supervised by a forward-backward translation consistency loss for self-supervision and a perceptual loss for content preservation. In addition to qualitative evaluation, we design an image quality assessment neural network to rank the dehazed images. Experimental results on both real and synthetic test data demonstrate that the proposed method performs superiorly against several state-of-the-art land dehazing methods. Compared with the state-of-the-art, our method gains a significant improvement by 1.94% for SSIM, 7.13% for PSNR and 4.00% for CIEDE2000 on the synthetic test dataset.