Privacy-preserving information hiding and its applications

The phenomenal advances in cloud computing technology have raised concerns about data privacy. Aided by the modern cryptographic techniques such as homomorphic encryption, it has become possible to carry out computations in the encrypted domain and process data without compromising information privacy. In this thesis, we study various classes of privacy-preserving information hiding schemes and their real-world applications for cyber security, cloud computing, Internet of things, etc. Data breach is recognised as one of the most dreadful cyber security threats in which private data is copied, transmitted, viewed, stolen or used by unauthorised parties. Although encryption can obfuscate private information against unauthorised viewing, it may not stop data from illegitimate exportation. Privacy-preserving Information hiding can serve as a potential solution to this issue in such a manner that a permission code is embedded into the encrypted data and can be detected when transmissions occur. Digital watermarking is a technique that has been used for a wide range of intriguing applications such as data authentication and ownership identification. However, some of the algorithms are proprietary intellectual properties and thus the availability to the general public is rather limited. A possible solution is to outsource the task of watermarking to an authorised cloud service provider, that has legitimate right to execute the algorithms as well as high computational capacity. Privacypreserving Information hiding is well suited to this scenario since it is operated in the encrypted domain and hence prevents private data from being collected by the cloud. Internet of things is a promising technology to healthcare industry. A common framework consists of wearable equipments for monitoring the health status of an individual, a local gateway device for aggregating the data, and a cloud server for storing and analysing the data. However, there are risks that an adversary may attempt to eavesdrop the wireless communication, attack the gateway device or even access to the cloud server. Hence, it is desirable to produce and encrypt the data simultaneously and incorporate secret sharing schemes to realise access control. Privacy-preserving secret sharing is a novel research for fulfilling this function. In summary, this thesis presents novel schemes and algorithms, including: • two privacy-preserving reversible information hiding schemes based upon symmetric cryptography using arithmetic of quadratic residues and lexicographic permutations, respectively. • two privacy-preserving reversible information hiding schemes based upon asymmetric cryptography using multiplicative and additive privacy homomorphisms, respectively. • four predictive models for assisting the removal of distortions inflicted by information hiding based respectively upon projection theorem, image gradient, total variation denoising, and Bayesian inference. • three privacy-preserving secret sharing algorithms with different levels of generality

Using Design Science to Build a Watermark System for Cloud Rightful Ownership Protection

Cloud computing opportunities have presented service options for users that are both economical and flexible to use requirements. However, the risk analysis for the user identifies vulnerabilities for intellectual property ownership and vulnerabilities for the identification of rightful property owners when cloud services are used. It is common for image owners to embed watermarks and other security mechanisms into their property so that the rightful ownership may be identified. In this paper we present a design that overcomes many of the current limitations in cloud watermarking uses; and propose a schema that places responsibility on the cloud provider to have a robust information protection program. Such a design solution lays out an information security architecture that enhances utility for cloud services and gives better options for users to securely place properties in the cloud. The Design Science methodology is used to build the artefact and answer the research question: How can rightful ownership be protected in the Cloud

A Survey on Multimedia Content Protection Mechanisms

Cloud computing has emerged to influence multimedia content providers like Disney to render their multimedia services. When content providers use the public cloud, there are chances to have pirated copies further leading to a loss in revenues. At the same time, technological advancements regarding content recording and hosting made it easy to duplicate genuine multimedia objects. This problem has increased with increased usage of a cloud platform for rendering multimedia content to users across the globe. Therefore it is essential to have mechanisms to detect video copy, discover copyright infringement of multimedia content and protect the interests of genuine content providers. It is a challenging and computationally expensive problem to be addressed considering the exponential growth of multimedia content over the internet. In this paper, we surveyed multimedia-content protection mechanisms which throw light on different kinds of multimedia, multimedia content modification methods, and techniques to protect intellectual property from abuse and copyright infringement. It also focuses on challenges involved in protecting multimedia content and the research gaps in the area of cloud-based multimedia content protection

A buyer-seller watermarking protocol for digital secondary market

In the digital right management value chain, digital watermarking technology plays a very important role in digital product’s security, especially on its usage tracking and copyrights infringement authentication. However, watermark procedures can only effectively support copyright protection processes if they are applied as part of an appropriate watermark protocol. In this regard, a number of watermark protocols have been proposed in the literature and have been shown to facilitate the use of digital watermarking technology as copyright protection. One example of such protocols is the anonymous buyer-seller watermarking protocol. Although there are a number of protocols that have been proposed in the literature and provide suitable solutions, they are mainly designed as a watermarking protocol for the first-hand market and are unsuitable for second-hand transactions. As the complexity of online transaction increases, so does the size of the digital second-hand market. In this paper, we present a new buyer-seller watermark protocol that addresses the needs of customer’s rights problem in the digital secondary market. The proposed protocol consists of five sub-protocols that cover the registration process, watermarking process for the first, second and third-hand transactions as well as the identification & arbitration processes. This paper provides analysis that compares the proposed protocols with existing state-of-the-arts and shows that it has met not only all the buyer’s and seller’s requirements in the traditional sense but also accommodates the same requirements in the secondary market

A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions

With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses generative large AI algorithms to assist or replace humans in creating massive, high-quality, and human-like content at a faster pace and lower cost, based on user-provided prompts. Despite the recent significant progress in AIGC, security, privacy, ethical, and legal challenges still need to be addressed. This paper presents an in-depth survey of working principles, security and privacy threats, state-of-the-art solutions, and future challenges of the AIGC paradigm. Specifically, we first explore the enabling technologies, general architecture of AIGC, and discuss its working modes and key characteristics. Then, we investigate the taxonomy of security and privacy threats to AIGC and highlight the ethical and societal implications of GPT and AIGC technologies. Furthermore, we review the state-of-the-art AIGC watermarking approaches for regulatable AIGC paradigms regarding the AIGC model and its produced content. Finally, we identify future challenges and open research directions related to AIGC.Comment: 20 pages, 6 figures, 4 table

Gamified Digital Forensics Course Modules for Undergraduates

Cyber security and forensics are among the most critical areas of national importance with a rising demand for knowledgeable professionals. In response to the increasing need for advanced studies in forensics, we propose game-based modules using the game-based learning approach that enable first-year students to learn basic digital forensics concepts without pre-requisite knowledge. This paper focuses on the design and development of an interactive game framework and the educational digital forensics modules that will be plugged into the game framework in a real computing environment. In contrast to the traditional teaching approaches, this modular approach will use game-based learning and visualization techniques to engage students to learn abstract concepts and to explore forensics investigation technologies and procedures through interactive games. The general design of the game framework can be replicated and adapted by other science education programs

Privacy Intelligence: A Survey on Image Sharing on Online Social Networks

Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs when sharing images on OSNs. However, OSN image sharing itself is relatively complicated, and systems currently in place to manage privacy in practice are labor-intensive yet fail to provide personalized, accurate and flexible privacy protection. As a result, an more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a systematic survey of 'privacy intelligence' solutions that target modern privacy issues related to OSN image sharing. Specifically, we present a high-level analysis framework based on the entire lifecycle of OSN image sharing to address the various privacy issues and solutions facing this interdisciplinary field. The framework is divided into three main stages: local management, online management and social experience. At each stage, we identify typical sharing-related user behaviors, the privacy issues generated by those behaviors, and review representative intelligent solutions. The resulting analysis describes an intelligent privacy-enhancing chain for closed-loop privacy management. We also discuss the challenges and future directions existing at each stage, as well as in publicly available datasets.Comment: 32 pages, 9 figures. Under revie