In the era of big data, data sharing not only boosts the economy of the world but also brings about problems of privacy disclosure and copyright infringement. The collected data may contain users’ sensitive information; thus, privacy protection should be applied to the data prior to them being shared. Moreover, the shared data may be re-shared to third parties without the consent or awareness of the original data providers. Therefore, there is an urgent need for copyright tracking. There are few works satisfying the requirements of both privacy protection and copyright tracking. The main challenge is how to protect the shared data and realize copyright tracking while not undermining the utility of the data. In this article, we propose a novel solution of a reversible database watermarking scheme based on order-preserving encryption. First, we encrypt the data using order-preserving encryption and adjust an encryption parameter within an appropriate interval to generate a ciphertext with redundant space. Then, we leverage the redundant space to embed robust reversible watermarking. We adopt grouping and K-means to improve the embedding capacity and the robustness of the watermark. Formal theoretical analysis proves that the proposed scheme guarantees correctness and security. Results of extensive experiments show that OPEW has 100% data utility, and the robustness and efficiency of OPEW are better than existing works.

Traffic-sensitive navigation systems in vehicular networks help drivers avoid traffic jams by providing several realtime navigation routes. However, drivers still encounter privacy concerns because their sensitive locations, i.e., their start point and endpoint, are submitted to an honest-but-curious navigation service provider (NSP). Previous privacy-preserving studies exhibit serious deficiencies under similar queries: if a driver makes several similar queries, i.e., periodically makes requests for the same start point and endpoint to the NSP, these requests will eventually reveal the areas of the two points as well as the route. In this paper, we present a novel privacy-preserving navigation scheme PiSim, which supports similar queries in navigation services. Intuitively, we transform the typical navigation approach into a traffic congestion querying approach. Instead of sending two locations to the NSP and awaiting a navigation route, drivers query the traffic congestion along the navigation route. Specifically, PiSim is characterized by extending anonymous authentication, facilitating privacy-preserving multi-keyword fuzzy search, and constructing weighted proximity graphs. Our scheme protects location privacy and route privacy, and defends against multiple requesting, spurious reporting, and collusion attacks from malicious drivers. Finally, a detailed analysis confirms the privacy and security properties of PiSim. Extensive experiments are conducted to demonstrate the feasibility, performance, and privacy protection level.

With the development of cloud server, reversible data hiding in encrypted domain has received widespread attention for management of encrypted media. Most of existing reversible data hiding methods are based on stream ciphers, which are mainly concerned with data storage security. While homomorphic ciphers focus on data processing security, which is popular in cloud and other third-party platforms. Reversible data hiding with homomorphic cryptography has been an active topic in the research filed. In this paper, we propose a new lossless data hiding method based on homomorphic cryptosystem. After a content owner generates encrypted media with homomorphic ciphers, a data hider embeds secret data by establishing a mapping between secret bits and losslessly modifying encrypted media. At the receiver side, the embedded data is extracted from encrypted domain with a little auxiliary message. And original media is recovered by directly decrypting marked encrypted media. During the embedding process, the modification of encrypted media does not change the corresponding original media owing to homomorphic and probabilistic properties. Thus, no distortion is introduced in the whole procedures of data embedding. And directly decrypted media containing embedded data is the same as the original media. Meanwhile, the proposed method achieves a high embedding rate through efficient mapping and skillful use of expanded pixel values. The experimental results show that compared with state-of-the-art methods, the proposed method has higher embedding rate without distortion.

In the complex and dynamic economic environment, the growing pain of small and micro enterprises is long‐standing. It is urgent to strengthen the research on the endogenous growth mechanism of small and micro enterprises. Based on the background of the era of knowledge‐driven economy, this paper explores the relationship between knowledge network embeddedness and dynamic capabilities of small and micro enterprises with environmental munificence as the regulating variable. We have the structural equation empirical research with the data from 260 questionnaires of small and micro enterprises. The results show that structural embeddedness and relational embeddedness have a positive driving effect on the dynamic capability of small and micro enterprises, and environmental munificence plays a positive regulatory role in the positive impact of knowledge network embedding on the dynamic capability. The research conclusion is helpful for the small and micro enterprise to develop dynamic capacity and for the supportive policy making as well.

Nowadays information is crucial in many fields such as medicine, science and business, where databases are used effectively for information sharing. However, the databases face the risk of being pirated, stolen or misused, which may result in a lot of security threats concerning ownership rights, data tampering and privacy protection. Watermarking is utilized to enforce ownership rights on shared relational databases. Many reversible watermarking methods are proposed recently to protect rights of owners along with recovering original data. Most state-of-the-art methods modify the original data to a large extent, result in data quality degradation, and cannot achieve good balance between robustness against malicious attacks and data recovery. In this paper, we propose a robust and reversible database watermarking technique, Genetic Algorithm and Histogram Shifting Watermarking (GAHSW), for numerical relational database. The genetic algorithm is used to select the best secret key for grouping database, where the watermarking can be embedded with balanced distortion and capacity. The histogram of the prediction error is shifted to embed the watermark with good robustness. Experimental results demonstrate the effectiveness of GAHSW and show that it outperforms state-of-the-art approaches in terms of robustness against malicious attacks and preservation of data quality.

Research on adaptive steganography mainly focuses on how to design a reasonable cost function and how to utilize that cost function to achieve embedding in a stego image with the minimal distortion based on syndrome-trellis codes. Because previous adaptive steganographic methods use convolution with filters to obtain the residuals, these methods do not make good use of the textures of the image itself in the design of the cost function. In this letter, we define a new cost function that uses nonnegative matrix factorization to predict the image pixels and utilizes the mutual dependencies among the pixels to calculate the costs. We present a novel cost function in which the residuals are not calculated via convolution with constant filters. Experimental results show that our method outperforms the state-of-the-art MiPOD, spatial universal wavelet relative distortion, wavelet obtained weights, and HUGO-BD methods in resisting steganalysis based on the spatial rich model and is slightly superior to the high-pass, low-pass, low-pass method.