Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural networks (CNNs) can effectively extract the features describing the image content, the difficulty lies in extracting the subtle features that describe the existence of hidden information. Considering this concern, this paper introduces separable convolution and adversarial mechanism, and proposes a new network structure that effectively solves the problem. The separable convolution maximizes the residual information by utilizing its channel correlation. The adversarial mechanism makes the generator extract more content features to mislead the discriminator, thus separating more steganographic features. We conducted experiments on BOSSBase1.01 and BOWS2 to detect various adaptive steganography algorithms. The experimental results demonstrate that our method extracts the steganographic features effectively. The separable convolution increases the signal-to-noise ratio, maximizes the channel correlation of residuals, and improves efficiency. The adversarial mechanism can separate more steganographic features, effectively improving the performance. Compared with the traditional steganalysis methods based on deep learning, our method shows obvious improvements in both detection performance and training efficiency.

In opportunistic networks, nodes forward messages according to routing utility deduced from encounter information. When nodes simply communicate with real IDs, private information such as encounter time and location will be leaked. The private information can be preserved by anonymizing node real IDs, but ID anonymity will also prevent nodes from collecting real ID-based encounter information to compute routing utility. To address this problem, a privacy-preserving exchange-based routing protocol (PPERP) for opportunistic networks is proposed in this paper. In PPERP, nodes are classified into delivery nodes and nondelivery nodes. Two encountering nondelivery nodes exchange approximately equivalent anonymous encounter records. Then, when the exchanged encounter records are sent to a delivery node, it will compute the routing utilities to nondelivery nodes without knowing their real IDs. In PPERP, a node can directly compute its routing utility without relying on any trusted third party, so PPERP is suitable for opportunistic networks in which nodes usually are sparsely distributed and encounter opportunistically. Bilinear mapping technique is adopted to ensure the confidentiality, integrity, and nonrepudiation of encounter records. The security analysis and performance evaluation show that the proposed protocol is secure, efficient, and practical.

With the interaction of geographic data and social data, the inference attack has been mounting up, calling for new technologies for privacy protection. Although there are many tangible contributions of spatial-temporal cloaking technologies, traditional technologies are not enough to resist privacy intrusion. Malicious attackers still steal user-sensitive information by analyzing the relationship between location and query semantics. Reacting to many interesting issues, oblivious transfer (OT) protocols are introduced to guarantee location privacy. To our knowledge, OT is a cryptographic primitive between two parties and can be used as a building block for any arbitrary multiparty computation protocol. Armed with previous privacy-preserving technologies, for example, OT, in this work, we first develop a novel region queries framework that can provide robust privacy for location-dependent queries. We then design an OT-assist privacy-aware protocol (or OTPA) for location-based service with rigorous security analysis. In short, the common query of the client in our solution can be divided into two parts, the region query and the content query , to achieve location -anonymity, location -diversity, and query -diversity, which ensure the privacy of two parties (i.e., client and server). Lastly, we instantiate our OTPA protocol, and experiments show that the proposed OTPA protocol is reasonable and effective.