## 5 Results of the Application of Linguistic Threshold Schemes  
The possibilities of applying new classes of cryptographic solutions in the area of data protection, in particular linguistic threshold schemes, are varied. The most important are visual information security and image databases protection. It is also possible to apply such techniques to the strategic development of healthcare institutions and enterprises at a regional and national level, guaranteeing the security of data transmission with information relevant to the patients and institutions and ensuring the security of computer networks and data transmission in IT networks.  
An example of the application of linguistic threshold schemes is guaranteeing the security and efficient management of sensitive, secret or personal data. The processes of the safe management of information that is confidential to a significant extent are conducted by governmental or business bodies, corporations and other entities. Linguistic threshold schemes can also be used in the decision-making processes in healthcare institutions, where in order to make an optimal decision, we need a complex analysis of big data and situations, while the actions taken are of strategic or developmental relevance; as such, access to them is limited.  
Another area of application of linguistic threshold schemes is in data protection and distribution at various levels of their processing, taking into consideration the level of an entity: the superior level in hierarchical structures or external levels to a given structure (where the level of fog and cloud belong).  
Figure 4 presents an example in which we can divide medical visualization towards the generation of a particular number of secret parts. Such generated shares can be distributed over cloud infrastructures, and later reconstructed by the compilation of the required number of visual parts. Depending on the number of compiled shares, we can obtain source data with different quality parameters or simply prevent unauthorised users from revealing source data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Grammar** & **Parsing Complexity** \\ \hline Sequential—regular & O(x)—linear \\ Sequential—context-free & O(x\({}^{k}\))—polynomial \\ Tree grammar, general & O(2\({}^{n}\))—exponential \\ Tree grammar, modified & O(x\({}^{k}\))—polynomial \\ Graph grammar, general & O(2\({}^{n}\))—exponential \\ Graph grammar, modified & O(x\({}^{k}\))—polynomial \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity for different classes of grammars used in linguistic threshold schemes.  
Figure 5 shows an example of generating language shadows (secret parts) for a very simple input sequence.  
Figure 4: Example of medical visual data sharing. In this example, a selected image was divided into five parts (visible as grey bars) in such a manner that, for restoration, at least three parts are required. Depending on the number of selected parts the original image can be restored with different quality levels.  
Figure 5: Linguistic shadow generation for an example input sequence. Different colours indicate consecutive 4-bit blocks, and the corresponding rules in the defined grammar.  
This example shows how, for a short input sequence and using simple string grammar, a new linguistic representation of the secret can be created and the secret components of the shared data can be generated.