A Lossless Data-Hiding based IoT Data Authenticity Model in Edge-AI for Connected Living

Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.

Lossless Data Hiding in LWE-Encrypted Domains Based on Key-Switching

This paper proposes a lossless data hiding scheme in learning with errors (LWE)-encrypted domain based on key-switching technique. Lossless data hiding and extraction could be realized by a third party without knowing the private key for decryption. Key-switching-based least-significant-bit (KSLSB) data hiding method has been designed during the lossless data hiding process. The owner of the plaintext first encrypts the plaintext by using LWE encryption and uploads ciphertext to a (trusted or untrusted) third server. Then the server performs KSLSB to obtain a marked ciphertext. To enable the third party to manage ciphertext flexibly and keep the plaintext secret, the embedded data can be extracted from the marked ciphertext without using the private key of LWE encryption in the proposed scheme. Experimental results demonstrate that data hiding would not compromise the security of LWE encryption, and the embedding rate is 1 bit per bit of plaintext without introducing any loss into the directly decrypted result.

Histogram based QR Code Authentication using Medical Images

Medical images require proper attention during the information embedding since the information which is to be embedded should not disturb the image quality. The remedy for the distortion caused by embedding data into medical images can be overcome by using lossless data hiding techniques. QR Code are consisting of relevant medical information that can be retrieved easily. In the present, information security become vital asset at the communication services, thereby concealing of information become a dilemma. For this issue, we employ a method known as Steganography in which the data is concealed in a medium like text, image etc., and appears to be normal, without affecting the quality of the hidden medium. Watermarking enables the protection of data format which is embedded with other data format and provides ownership access to the end user in unrecognizable format. Both steganography and watermarking techniques employ greater security to the information which can be embedded within a data format of any type.