Linguistic techniques for cryptographic data sharing algorithms

Summary Increasing populations and rapid large‐scale urbanization has created a demand to increase the quality of life through economic development, social stability, and better quality environments. These issues are addressed in the field of Smart Cities where, through the Internet of Things, efforts are being made to support added‐value services for the administration of the city and for citizens. The continuous exchange of information inevitably produces a huge amount of data, which demands analyses of data using unconventional methods within a Big Data context. How can we properly process these data? How can we properly use these data in order to increase the competitiveness and efficiency of services, and how could they contribute to social development? Services that could be useful in this field include Early Warning Systems. Information management environments, or more generally pervasive data contexts, may be supported by context representation approaches and enhanced through adopting probabilistic approaches such as Context Dimension Tree, Ontology, and Bayesian Network. The aim of this work is to introduce and explain a methodology for merging CDTs and Ontologies, and probabilistic approach based on BNs in order to help expert users handle emergencies and provide suggestions for improving the liveability of cities for their inhabitants.

Section: Discussionmentioning

confidence: 99%

“…In addition, it is capable, through Ontologies and CDT, to interface with other similar systems based on different contexts sharing and exchanging knowledge in order to increasingly improve its performance. 30…”

Section: Discussionmentioning

confidence: 99%

A multilevel graph approach for rainfall forecasting: A preliminary study case on London area

Clarizia

Colace

Santo

et al. 2019

“…The development of IT techniques and a need to collect and store increasingly larger sets of secret data resulted in the need to develop new data protection protocols. This is why the class of data sharing algorithms has been enhanced by new solutions, described in the literature as linguistic and biometric threshold schemes …”

Section: Introductionmentioning

confidence: 99%

“…This means that in linguistic threshold schemes, there is an addition of splitting the secret among protocol participants based on the knowledge they have on the issue concealed. An example of such tasks are linguistic CAPTCHA codes, described in, among others, previous works …”

Section: Introductionmentioning

confidence: 99%

Cognitive security paradigm for cloud computing applications

Ogiela¹,

Ogiela

2019

Self Cite

Summary This article presents new paradigms of confidential data protection. New classes of paradigms are dedicated to enhancing the already‐known cryptographic solutions belonging to the group of data sharing schemes. Data sharing schemes are dedicated to executing data protection tasks by means of splitting data into parts (called shadows) and distributing those shadows among a group of secret trustees. This process is enriched by linguistic and biometric solutions to guarantee protection of the shared data by means of biometric labeling or by means of user verification with the application of meaning interpretation of individual secret parts. The novelty in this solution is in the application of cognitive algorithms to describe correctly the shared secret. Cognitive paradigms guarantee an execution of information concealment protocols and their division at various levels of knowledge held by individual protocol participants. The paradigms of cognitive description of the concealed data have been introduced into a new class of data sharing protocols, ie, into the linguistic‐biometric threshold schemes. A special feature of the proposed solution is its universality, which is a possibility to apply the here‐discussed threshold schemes in the processes of data concealment with varying importance. At the same time, this solution can be applied to execute data protection tasks against unauthorized acquisition or disclosure. This paper will present also the possibilities of application of the paradigms presented here to conceal data dedicated to the data management processes.

“…14,15 Linguistic shadow is one of all shadows in splitting process. This shadow can be named "linguistic shadow," because in this part of secret, only linguistic data are included.…”

Section: Introductionmentioning

confidence: 99%

Visual CAPTCHA application in linguistic cryptography

Ogiela

Takizawa

Ogiela

2017

Self Cite

This paper presents an idea of linguistic cryptography and linguistic techniques for data analysis.The main aspects are using parts of divided information to reconstruct them. Linguistic techniques dedicated to cryptography, as well as secret splitting and sharing, are very useful to secure all information and all parts of them. Very important aspects of cryptographic algorithms are played by techniques of data division as well as data splitting and information sharing algorithms. Those methods allow the distributed data within a different selected group of secret trustees. In this paper, authors described algorithms of using those methods for linguistic data sharing techniques. In those methods, CAPTCHA analysis plays an important role. All secret parts of information can be written by using any parts of the selected CAPTCHA procedures. KEYWORDS linguistic cryptography, secret sharing, visual CAPTCHA | INTRODUCTIONMany different cryptographic solutions to secure data create many different algorithms dedicated to division of information into groups of secret trustees. One of those methods is data division of algorithms by using splitting techniques. In the class of data splitting techniques, we can find many other algorithms as well as data splitting algorithms and data sharing techniques. Those solutions were described in publications. 1-4 Also, in literature, new techniques for securing data sets were described by using linguistic methods. Linguistic algorithms are dedicated for message sharing. 5-7 This solution has been described in publications, 4,8 which presents linguistic algorithms supporting the semantic meaning analysis 5 of data security aspects. 9,10 Linguistic techniques have been proposed for semantic analysis for different data sets, especially for image data and ratio data. Linguistic techniques were dedicated to semantic description, cognitive analysis, and data understanding, in all classes of analysed data. The linguistic description of analysed data sets was very useful for data understanding. The main aspects of securing data by using linguistic techniques proposed used semantic algorithms to describe data sets for divided data. The idea of this solution has been described in the publications. [11][12][13] In this paper, the authors propose an idea of applied linguistic methods and semantic algorithms for data sharing by used CAPTCHA protocols.The main idea of linguistic cryptographic techniques consists in generating an additional part of divided data/secret, as shadow which includes semantic information. This shadow can be named "linguistic shadow," because in this part of secret, only linguistic data are included. This example was described in previous studies. 14,15 Linguistic shadow is one of all shadows in splitting process. This part of splitting secret includes the most important semantic information about analysed and divided data. In this part of secret, we can include only information about meaning of analysed data, importance of analysed situation, causes of the analysed procedur...