In recent years, reversible data hiding in encrypted domain (RDH-ED) has garnered considerable interest among researchers, resulting in the development of high-performance methods based on various carriers. However, the challenge of enhancing the data embedding capacity while ensuring reversibility becomes increasingly pronounced when the carrier is a three-dimensional (3D) model. In this paper, a high capacity RDH-ED method based on dynamic prediction and virtual connection for 3D models is proposed. Unlike existing methods that partition the vertices in the model into embeddable and prediction sets, where each vertex can only serve one function, the proposed dynamic prediction mechanism constructs a data embedding order set by leveraging the connectivity relationships between vertices. This allows each vertex within the set to both embed data and provide predictions, significantly increasing the proportion of embeddable vertices. Moreover, the proposed method is the first work to consider independent vertices within the model and integrates a novel virtual connection approach with the dynamic prediction process, enabling all independent vertices to participate in data embedding and prediction, thereby further enhancing the data embedding capacity. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art methods in terms of data embedding capacity while ensuring reversibility.

Recently, as one of the most popular covert communication technologies, generative steganography has received ever-increasing attention due to its promising performance against sophisticated steganalysis tools. However, it is quite difficult for the existing generative steganographic approaches to find a good tradeoff between hiding capacity and extraction accuracy, mainly due to the small capacity of their hiding spaces. To overcome this shortcoming, a Progressive Generative Steganography (PGS) network architecture is proposed to hide a secret message during the progressive image generation process to realize secure covert communication. Specifically, we first propose a robust Secret-to-Noise (S2N) mapping method to encode the secret message as a set of noise maps. Then, guided by these noise maps, a set of corresponding images ranging from low resolution to high resolution are progressively generated by the Single Generative Adversarial Networks (SINGAN). Consequently, a large-sized secret message can be hidden in the finally generated high-resolution image, since a set of high-capacity hiding spaces can be provided by the process of progressive image generation. Moreover, to improve the quality of image generation and the accuracy of secret message extraction, a Dense Secret-Feature Connection (DSFC) strategy is designed and integrated into the proposed PGS network architecture. Extensive experiments demonstrate that the proposed PGS outperforms the existing approaches in the aspects of both hiding capacity and message extraction, while maintaining promising anti-detectability and imperceptibility for covert communication.

Federated learning (FL) has attracted widespread attention because it supports the joint training of models by multiple participants without moving private dataset. However, there are still many security issues in FL that deserve discussion. In this paper, we consider three major issues: 1) how to ensure that the training process can be publicly audited by any third party; 2) how to avoid the influence of malicious participants on training; 3) how to ensure that private gradients and models are not leaked to third parties. Many solutions have been proposed to address these issues, while solving the above three problems simultaneously is seldom considered. In this paper, we propose a publicly auditable and privacy-preserving federated learning scheme that is resistant to malicious participants uploading gradients with wrong directions and enables anyone to audit and verify the correctness of the training process. In particular, we design a robust aggregation algorithm capable of detecting gradients with wrong directions from malicious participants. Then, we design a random vector generation algorithm and combine it with zero sharing and blockchain technologies to make the joint training process publicly auditable, meaning anyone can verify the correctness of the training. Finally, we conduct a series of experiments, and the experimental results show that the model generated by the protocol is comparable in accuracy to the original FL approach while keeping security advantages.

Federated learning (FL), as an effective method to solve the problem of “data island”, has become one of the hot and widespread concern topics in recent years. However, with the using of FL technology in the practical applications, an increasing number of FL tasks make the training management be more complex and the trade-off of multi-task becomes difficult. To overcome this weakness, this work proposes a privacy-preserving FL framework with multi-tasks using partitioned blockchain, which can run several different FL tasks by multiple requesters. First, a temporary committee is formed for an FL task to facilitating visualization, organization and management of security aggregation. Second, the proposed framework combines Paillier homomorphic encryption with Pearson correlation coefficient to protect users' privacy and ensure the accuracy of global model. Finally, a new blockchain-based reward method is presented to inspire participants to share their valuable data. The experimental results show that the global model accuracy of our proposed framework is able to reach 98.43%. Obviously, the proposed framework is more suitable for practical application environment, especially in industrial application field.

With the increasing popularity of Online Social Networks (OSNs), covert communication is rapidly shifting from lossless channels like email to lossy channels, specifically social networks. In response to this trend, robust adaptive steganography has emerged as a powerful technique for concealing information in lossy transport channels. Previous approaches have aimed to address the challenge of JPEG image compression during transmission by utilizing static compression-resistant domains, Syndrome-Trellis Codes (STC), and Error Correction Codes (ECC). However, reliance on a significant number of ECC check codes to ensure robustness could inadvertently affect security. In response to this challenge, we introduce the “Adaptive STC-ECC” strategy, which enhances security by minimizing the number of check codes without compromising robustness. We further improve the robustness by simulating the embedding process and strategically placing the wet point in unstable cover elements. Furthermore, we exploit the residual information between the pre-cover and cover images to adjust the distortion and accurately determine the direction of the dither modulation, thus improving the overall security. Extensive experiments have been conducted to evaluate the performance of our proposed approach, and the results demonstrate its superior robustness and security compared to existing state-of-the-art approaches.

Online dating is an increasingly thriving business which boosts billion-dollar revenues and attracts users in the tens of millions. Despite its popularity, internet dating is not exempt from the concerns about privacy and trust posed by the revelation of potentially sensitive data as well as the exposure to self-reported (and hence potentially distorted) information. The increasing popularity of online dating networks leads to an increase in security concerns and challenges, as well as harmful actions and attacks, such as creating fake accounts, phishing on these networks. To maintain the safety of legitimate online dating users, it is critical to recognize and isolate criminal people as soon as possible. However, researchers concerning malicious user detection in dating social networks are merely a few. To address some key challenges in this space, we propose a trust-aware detection framework to detect malicious users based on different kinds of data from a real dating site. In particular, we develop a user trust model to distinguish between malicious and legitimate users. Furthermore, we propose a novel data-balancing method to improve the recall rate of malicious user detection. Extensive experiments have been conducted over real-world datasets. The results show that the proposed approach yields a precision of up to 59.16% and a recall rate of up to 73%, which is significantly higher than other baseline algorithms.

The development of cloud computing attracts enterprises and individuals to outsource their data, such as images, to the cloud server. However, direct outsourcing causes the extensive concern of privacy leakage, as images often contain rich sensitive information. A straightforward way to protect privacy is to encrypt the images using the standard cryptographic tools before outsourcing. However, in such a way the possible usage of the outsourced images would be strongly limited together with the services provided to users, like the Content-Based Image Retrieval (CBIR). In this article, we propose a secure outsourced CBIR scheme, in which an encryption scheme is designed for the widely used JPEG-format images, and the secure features can be directly extracted from such encrypted images. Specifically, the JPEG images are encrypted by the block permutation, intra-block permutation, polyalphabetic cipher, and stream cipher. Then secure local histograms are extracted from the encrypted DCT blocks and the Bag-Of-Words (BOW) model is further used to organize the encrypted local features to represent the image. The proposed image encryption gets all of the image data protected and the experimental results show that the proposed scheme achieves improved accuracy with a small file size expansion.

Unknown RFID tags appear when tagged items are not scanned before being moved into a warehouse, which can even cause serious security issues. This paper studies the practically important problem of unknown tag detection. Existing solutions either require low-cost tags to perform complex operations or beget a long detection time. To this end, we propose the Collision-Seeking Detection (CSD) protocol, in which the server finds out a collision-seed to make massive known tags hash-collide in the last $N$ slots of a time frame with size $f$ . Thus, all the leading ${f-N}$ pre-empty slots become useful for detection of unknown tags. A challenging issue is that, computation cost for finding the collision-seed is very huge. Hence, we propose a supplementary protocol called Balanced Group Partition (BGP), which divides tag population into $n$ small groups. The group number $n$ is able to trade off between communication cost and computation cost. We also give theoretical analysis to investigate the parameters to ensure the required detection accuracy. The major advantages of our CSD+BGP are two-fold: (i) it only requires tags to perform lightweight operations, which are widely used in classical framed slotted Aloha algorithms. Thus, it is more suitable for low-cost tags; (ii) it is more time-efficient to detect the unknown tags. Simulation results reveal that CSD+BGP can ensure the required detection accuracy, meanwhile achieving $1.7\times $ speedup in the single-reader scenarios and $3.9\times $ speedup in the multi-reader scenarios than the state-of-the-art detection protocol.