Ji-Hwei Horng

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction while also enhancing security through a secret sharing mechanism. Experimental results demonstrate that the proposed scheme achieves approximately 50% accuracy in detecting steganographic text across two steganalysis networks. Additionally, the generated steganographic text preserves the semantic information of the cover text, evidenced by a BERT score of 0.948. This indicates that the proposed scheme performs well in terms of security.

Crypto-space reversible data hiding has emerged as an effective technique for transmitting secret information over the internet. However, most existing schemes are designed for uncompressed images, while almost all images are processed and transmitted in compressed formats. There is an urgent need to develop methods for compressed images, such as JPEG (Joint Photographic Experts Group). In this paper, we propose a reversible data hiding in encrypted JPEG images, where the bitstreams of AC (alternating current) coefficients and the secret data are mapped to numbers over Galois field. The obtained numbers are then utilized to conduct a polynomial for secret sharing. By reproduction into secret shares, the AC coefficients and the secret data are secured. In addition, a block sorting strategy is used to reduce image distortion under low data payload. Experimental results demonstrate that the proposed scheme outperforms state-of-the-art methods in embedding capacity while preserving the file size and conforming to the JPEG format.

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction, while also enhancing security through a secret sharing mechanism. Experimental results indicate that the generated steganographic text effectively preserves the sentiment and semantic information of the cover text.

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 years, data hiding techniques have emerged as a prominent area of research due to the increasing demand for information security. Existing data hiding techniques typically do not prioritize the verification of the correctness of the extracted data, nor do they attempt to rectify any erroneous information extracted. This paper presents a simple yet effective method by which a recipient can perform error detection and correction on extracted data. Prior to data embedding, a preprocess is applied to secret data. After converting the secret data to a data stream, a data hider categorizes adjacent and identical bits within the data stream into groups. The number of these bits is then recorded as an indicator to extract information and perform error detection and correction. The number of bits in each group is adjusted to ensure that it is an odd number. Moreover, the collected indicator data stream is encoded using (7,4) Hamming code to compact the recorded information. Finally, the concatenation of the encoded indicator and the encoded secret is embedded into the multiple least significant bits (LSBs) of the original image pixels. As for the receiver, error correction can be applied based on the indicator after the extraction of the encoded data. Experimental results demonstrate that this method allows the data receiver to detect and correct errors while maintaining a certain level of embedding capacity and image quality. In terms of image quality, this method exhibits superior performance compared to existing research when the embedded data volume is relatively small.