Intelligent techniques for secure financial management in cloud computing

Summary Each person has unique bio‐information, and this information rarely would be overlapped to other people. Because of this feature, many researchers have been working on a user's identification. However, the problem is that it is not certain if the feature is exactly matched at any time and in any place for the dynamic signal. It is very hard to match bio‐information whenever it is measured; however, all natural signals have an individual pattern. In this paper, it uses an ECG (electrocardiogram) as bio‐information, and it tries to find each pattern, which will be located within the threshold. With the pattern in the threshold, it detects the user's identification. To analyze the patterns, it analyzes them as measured for 120 seconds. Next, it divides them every 1‐2 seconds to 5 seconds. Then, it could recognize the users' identification with this study, and then, finally, the accuracy is 83.3618%.

Section: Limitationsmentioning

confidence: 99%

Personal identification study for touchable devices with ECG

Pan

Mesicek

2019

“…[13][14][15][16][17] In this chapter, the authors focus on discussing the topic of linguistic schemes for message sharing. The essence of the subject of the semantic analysis of data has been described inter alia in the publications.…”

Section: Linguistic Secret-sharing Schemesmentioning

confidence: 99%

Linguistic techniques for cryptographic data sharing algorithms

Ogiela

2017

Self Cite

This publication presents algorithms for protecting data from unauthorised access to it. A special role among information protection algorithms is played by algorithms of data splitting and sharing which allow the information to be distributed within a selected group of secret trustees. This paper describes protocols of the above type and also suggests a way of using the proposed solutions for linguistic information sharing. Linguistic message-sharing algorithms are described as a class of protocols for protecting data with its possible semantic interpretation. This is because the semantic analysis allows the content-based interpretation of various information sets. The proposed methods are very useful from different points of view. Authors would like to present one possible application which is cognitive service management and data-sharing systems applied for cloud computing.

“…Another class of data sharing schemes are biometric threshold schemes, as proposed in previous works . These algorithms, similarly to the linguistic threshold schemes, have been developed as an extension of classic data concealment schemes.…”

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.