Information Hiding with Data Diffusion Using Convolutional Encoding for Super-Encryption

The unification of data encryption with information hiding methods continues to receive significant attention because of the importance of protecting encrypted information by making it covert. This is because one of the principal limitations in any cryptographic system is that encrypted data flags the potential importance of the data (i.e. the plaintext information that has been encrypted) possibly leading to the launch of an attack which may or may not be successful. Information hiding overcomes this limitation by making the data (which may be the plaintext or the encrypted plaintext) imperceptible, the security of the hidden information being compromised if and only if its existence is detected. We consider two functions f1(r) and f2(r) for r ∈ R n , n = 1, 2, 3, ... and the problem of ‘Diffusing’ these functions together, applying a process we call ‘Stochastic Diffusion’ to the diffused field and then hiding the output of this process into one of the two functions. The coupling of these two processes using a form of conditioning that generates a well-posed inverse solution yields a super-encrypted field that is dataconsistent. After presenting the basic encryption method and (encrypted) information hiding model coupled with a mathematical analysis (within the context of ‘convolutional encoding’), we provide a case study which is concerned with the implementation of the approach for full-colour 24-bit digital images. The ideas considered yields the foundations for a number of wide-ranging applications that include covert signal and image information interchange, data authentication, copyright protection and digital rights management. In this context, we also provide prototype software using m-code and Python for readers to use, improve upon and develop further for applications of interest

Information Hiding Using Convolutional Encoding

We consider two functions f1(r) and f2(r), for r 2 Rn and the problem of ‘Diffusing’ these functions together, followed by the application of an encryption process we call ‘Stochastic Diffusion’ and then hiding the output of this process in to one or other of the same functions. The coupling of these two processes (i.e., data diffusion and stochastic diffusion) is considered using a form of conditioning that generates a wellposed and data consistent inverse solution for the purpose of decrypting the output. After presenting the basic encryption method and (encrypted) information hiding model, coupled with a mathematical analysis (within the context of ‘convolutional encoding’), we provide a case study which is concerned with the implementation of the approach for full-colour 24-bit digital images. The ideas considered yields the foundations for a number of wide-ranging applications that include covert signal and image information interchange, data authentication, copyright protection and digital rights management, for example

A practical guide to digitizing a collection using Open Source Software: A Southern African perspective

The article provides an overview of the practical implementation of adigital library using open source software. Southern Africa has notfully embraced or incorporated open source software into their informationmanagement operations. This lack of adaptation is attributed to a number of reasons amongst which are lack of general awareness and the absence of appropriately trained librarians to take advantage of such technological sources. The article gives guidelines and recommendations on what to consider when planning to digitize a collection. The following issues will be looked at: digital rights management, institutional repositories, Metadata Encoding and Transmission Standard (METS is an XML Schema designed for the purpose of creating XML document instances that express the hierarchicalstructure of digital library objects) and its applications, the open archives initiatives, and open source software for digital libraries. The article also focuses on the practical steps in using open source software for digitizing. Open source software has been chosen because of its free availability

Socio-economic implications of the KwaZulu-Natal sardine run for local indigenous communities

The economic and social effects of the annual sardine run on the indigenous community on the south coast of KwaZulu-Natal, South Africa, were assessed using data gathered from questionnaires and personal interviews with 329 members of the community. Their knowledge, perceptions and attitudes about the sardine run, as well as their level of involvement in, and the financial benefits accrued from it, were also assessed. Although around two-thirds of those interviewed were aware of the sardine run and just over half participated in it, only some 17% benefited financially from it. However, despite this low level of participation, the financial benefit to the community could amount to R17–18 million, and as much as R34–54 million if a multiplier effect of 2–3 is applied. There was a high level (over 70%) of willingness to learn  more about the event, and to become more involved in training exercises that would allow local people to take advantage of opportunities arising from the sardine run. It is recommended that management strategies and development plans should be implemented towards assisting the indigenous communities to become more involved in the sardine run.Keywords: ecotourism, indigenous community, KwaZulu-Natal, marketing, sardine run, socio-economic, South Coast, sustainable tourismAfrican Journal of Marine Science 2010, 32(2): 399–40

Information Hiding with Data Diffusion Using Convolutional Encoding for Super-Encryption

The unification of data encryption with information hiding methods continues to receive significant attention because of the importance of protecting encrypted information by making it covert. This is because one of the principal limitations in any cryptographic system is that encrypted data flags the potential importance of the data (i.e. the plaintext information that has been encrypted) possibly leading to the launch of an attack which may or may not be successful. Information hiding overcomes this limitation by making the data (which may be the plaintext or the encrypted plaintext) imperceptible, the security of the hidden information being compromised if and only if its existence is detected. We consider two functions f1(r) and f2(r) for r ∈ R n , n = 1, 2, 3, ... and the problem of ‘Diffusing’ these functions together, applying a process we call ‘Stochastic Diffusion’ to the diffused field and then hiding the output of this process into one of the two functions. The coupling of these two processes using a form of conditioning that generates a well-posed inverse solution yields a super-encrypted field that is dataconsistent. After presenting the basic encryption method and (encrypted) information hiding model coupled with a mathematical analysis (within the context of ‘convolutional encoding’), we provide a case study which is concerned with the implementation of the approach for full-colour 24-bit digital images. The ideas considered yields the foundations for a number of wide-ranging applications that include covert signal and image information interchange, data authentication, copyright protection and digital rights management. In this context, we also provide prototype software using m-code and Python for readers to use, improve upon and develop further for applications of interest