In today’s digital landscape, the exponential growth of data heightens security risks associated with traditional centralized storage systems. Utilizing blockchain technology, a shift towards decentralized data storage provides a more secure and private alternative. Central to our work is the exploration of symmetry in data management, a concept woven into the fabric of our proposed solution to challenge the inherency in InterPlanetary File System (IPFS) technology. Through the strategic utilization of smart contract-invoked random functions, our blockchain-based solution fragments and securely stores data in order to ensure a symmetrical balance between confidentiality and integrity. Our research endeavors are to contribute a robust, ethically grounded data storage framework fostering advancements in secure data sharing. The implications of this paper are significant in addressing contemporary challenges of data management within the expansive realm of big data.

With the development of Internet technology, information security and data protection have become particularly important. Reversible data hiding is an effective technique for data integrity and privacy protection, and secret image sharing is a distinct research field within reversible data hiding. Due to the ability of sharing secret information between two receivers and the larger embedding capacity compared to the traditional reversible data hiding scheme, dual digital images have also attracted extensive research in the past decade. In this paper, we propose a reversible data hiding scheme based on matrix coding using dual digital images. By modifying the bits in the pixels, we can conceal three bits of the secret message in two pixels. In other words, the embedding rate reaches 1.5 bits per pixel (bpp). The experimental results demonstrate that our method has a significantly larger embedding capacity of 786,432 bits compared to previous similar methods while still maintaining acceptable image quality defined by a peak signal-to-noise ratio (PSNR) greater than 30 dB. The proposed scheme is suitable for applications required to pass a large amount of data but with minor security of image quality to be visually acceptable.

The reversible data hiding scheme in the encrypted domain is a potential solution to the concerns regarding user privacy in cloud applications. The 3D mesh model is an emerging file format and is widely used in engineering modeling, special effects, and video games. However, studies on reversible data hiding in encrypted 3D mesh models are still in the preliminary stage. In this paper, two novel techniques, multi-group partition (MGP) and closest pair prediction (CPP), are proposed to improve performance. The MGP technique adaptively classifies vertices into reference and embeddable vertices, while the CPP technique efficiently predicts embeddable vertices and generates shorter recovery information to vacate more redundancy for additional data embedding. Experimental results indicate that the proposed scheme significantly improves the embedding rate compared to state-of-the-art schemes and can be used in real-time applications.

Secret sharing is a data security technique that divides secret information into multiple parts, embeds these parts into various shares, and distributes these shares to different participants. The original secret information can be retrieved only when the number of shares gathered meets a required threshold. This paper proposes a secret sharing method that can hide data in encrypted images with reversibility and allows content owners to add an additional layer of security before uploading data to the cloud. This method enables the independent extraction of images and data, ensuring that the recovered images and extracted data can serve as validation information for each other. The proposed method not only enhances data security but also guarantees the accuracy of the extracted information.

In recent decades, information security has become increasingly valued, including many aspects of privacy protection, copyright protection, and digital forensics. Therefore, many data hiding schemes have been proposed and applied to various carriers such as text, images, audio, and videos. Vector Quantization (VQ) compression is a well-known method for compressing images. In previous research, most methods related to VQ compressed images have focused on hiding information in index tables, while only a few of the latest studies have explored embedding data in codebooks. We propose a data hiding scheme for VQ codebooks. With our approach, a sender XORs most of the pixel values in a codebook and then applies a threshold to control data embedding. The auxiliary information generated during this process is embedded alongside secret data in the index reordering phase. Upon receiving the stego codebook and the reordered index table, the recipient can extract the data and reconstruct the VQ-compressed image using the reverse process. Experimental results demonstrate that our scheme significantly improves embedding capacity compared to the most recent codebook-based methods. Specifically, we observe an improvement rate of 223.66% in a small codebook of size 64 and an improvement rate of 85.19% in a codebook of size 1024.