Personalized cryptography in cognitive management

Ogiela, Lidia; Takizawa, Makoto

doi:10.1007/s00500-017-2546-2

Cited by 6 publications

(11 citation statements)

References 22 publications

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“…The number of subsets is equal to the number of characteristic features that are possible to specify for the concealed set. 4. In every subset, there are executed independent data sharing protocols based on (m, n)-threshold schemes.…”

Section: The D Data Set With a Number D Of Items Is Split Into Separamentioning

confidence: 99%

“…and at specific levels of hierarchy. 4. In every subset, there are executed independent data sharing protocols based on (m, n)-threshold schemes.…”

Section: The Number Of Subsets Is Equal To the Number Of Characteristmentioning

confidence: 99%

“…Publications describe various examples of data securitization protocols. [1][2][3][4][5][6][7][8] The algorithms of data splitting and sharing 9 are a special example of protocols used for the processes of data securitization. 7,8,[10][11][12][13][14] In this group of protocols, apart from the classic information splitting algorithms, there are also new classes of threshold schemes, including both linguistic and biometric threshold schemes.…”

Section: Introductionmentioning

confidence: 99%

“…15 A detailed description of these threshold schemes can be found in other works. [3][4][5][9][10][11][12][13][14][15][16][17] In this work, we shall discuss new classes of threshold schemes based on cognitive processes of concealed data description. The gist of the proposed solution is a possibility to specify some nonrepetitive semantic features of the concealed data and the application of such type of information in the process of its concealment.…”

Section: Introductionmentioning

confidence: 99%

“…The processes of data securitization and their safe management are the subject of various analyses, for example, of safe management of selectively chosen data or entire information sets, specifying the need to conceal data, protocols guaranteeing data security, as well as algorithms implementing data securitization processes. Publications describe various examples of data securitization protocols . The algorithms of data splitting and sharing are a special example of protocols used for the processes of data securitization .…”

Section: Introductionmentioning

confidence: 99%

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New protocols of cognitive data management and sharing in cloud computing

Ogiela

Takizawa

2019

Concurrency and Computation

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Summary In this paper, we shall discuss new classes of cognitive systems dedicated the tasks of dividing and managing shared information not only in simple horizontal structures but also primarily in vertical structures. An example of such a structure is a computer cloud where, from various levels of access to information, one can execute the management process, both with respect to the entire information resources, as well as to individual pieces of data, including shared information. A special type of information is a service, which, similar to various types of data, is subject to the concealment process. In this work, we shall present universal algorithms for shared data management, such that can be used in concealment protocols both for data and services. Due to the above, this paper will treat the term “service” as a special type of data, constituting the basis of their sharing and management processes. Moreover, we shall determine the impact of the proposed algorithms on the enhancement of data/service management processes in Cloud Computing as well as the impact of the service management and data sharing processes in Could Computing on decision‐taking processes in systems supporting the management processes.

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Section: The D Data Set With a Number D Of Items Is Split Into Separamentioning

confidence: 99%

“…and at specific levels of hierarchy. 4. In every subset, there are executed independent data sharing protocols based on (m, n)-threshold schemes.…”

Section: The Number Of Subsets Is Equal To the Number Of Characteristmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New protocols of cognitive data management and sharing in cloud computing

Ogiela

Takizawa

2019

Concurrency and Computation

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Towards human‐oriented solutions for deep semantic data analysis

Ogiela

Snåšel

2021

Concurrency and Computation

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This paper presents a new data analysis technology based on human-oriented analysis. This analysis covers semantic methods of data description and interpretation referring to marketing preferences of system users. The proposed methods of cognitive marketing -in order to interpret fully all possible preferences which can occur -are a subject to an analysis focusing on their meaning and usefulness at the stage of product evaluation, promotion, but also regarding product distribution and price. All these processes will constitute marketing analysis due to the possibilities to assess some preferences of the analyzed data. The essence of this paper is a possibility to present the methodology of cognitive marketing based on the application of the meaning analysis which reaches the human brain and the attention attractors registered by the brain, which give rise to some interest or, quite the contrary, which remain unnoticed. A new solution is dedicated to deep semantic analysis based on the registration, processing, and analysis of attention attractors and their perception by individual person. Such attractors can be detected on the basis of observation of how attention is focused on some specific features of the examined information groups. The variety of the examined information and data can enable a wide-ranging analysis of the issue discussed here; as a result, can assess the degree to which human attention focuses on the process of meaning interpretation of various cognitive features and observations. K E Y W O R D Sdeep data analysis, human-oriented solutions, semantic description INTRODUCTIONData analysis methods treated as sets of varied information differ with a view to the type of the analyzed sets, but also with a view to the objective of the information obtained as a result of this process. One of approaches to data analysis is the meaning analysis, which is an extension -an enhancement -of the currently commonly used data analysis methods. [1][2][3] The meaning data analysis enables an automatic, computer interpretation of varied data sets with regards to the semantics they have. [4][5][6][7][8] Semantic layers contained in various data sets make it possible to determine their meaning in the process of analysis of a given set/piece of information/situation, and so forth.An important feature of the meaning analysis is the possibility to assess the impact (importance) of the interpreted sets on their future state.Based on the notation of features of the analyzed sets, an appropriate interpretation of all determinants of the current state is made, with a view to their future change resulting from possibilities to alter them in the future. It serves not only to describe appropriately the current (analyzed) state, but primarily its task is to understand the reasons and make forecasts about future changes.The basic feature of the meaning data analysis is an execution of the meaning description of the interpreted sets. This description is executed by means of linguistic data description techniques, applied with t...

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