Abstract An information system is composed of two types of entities, subjects like users and objects like databases from the security viewpoint. A subject issues an operation to an object to manipulate data in the object. An object is protected from malicious accesses of unauthorized subjects in the AC (Access Control) models. Nevertheless, if data in an object $$o_j$$ o j is stored in another object $$o_k$$ o k , a subject $$s_i$$ s i which is allowed to read $$o_k$$ o k can read the data of $$o_j$$ o j in $$o_k$$ o k even if $$s_i$$ s i is not granted an access right of $$o_j$$ o j , i.e. illegal information flow from $$o_j$$ o j to $$s_i$$ s i occurs. In our previous studies, the O-IFC (Object-based Information Flow Control) is proposed where operations occurring illegal information flow are prohibited. Here, a unit of data exchanged among entities is an object. Even if some operations do not occur illegal information flow, the operations are prohibited in the O-IFC. In order to reduce the operations unnecessarily prohibited, a novel C-IFC (Content-based IFC) is proposed where a finer unit of data than an object is considered to be exchanged among entities. In this article, an object is composed of natural language sentences. It is critical to decide whether or not a sentence $$st_j$$ s t j in $$o_j$$ o j is the same as another sentence $$st_k$$ s t k in $$o_k$$ o k . In this article, the sentence classifier is used which is generated by using the large language model based on the neural networks. If $$st_j$$ s t j and

In this paper a novel password-reminder scheme will be presented, which can facilitate security education processes. The goal was to create a CAPTCHA-like image with hidden meaning, that only user would be able to decode. With selected visual pattern, being presented each time during logging in, the user is asked to associate a password consisting of two or more words and a personal number.

Abstract CAPTCHA ( Completely Automated Public Turing test to tell Computers and Humans Apart ) has long been used to keep automated bots from misusing web services by leveraging human-artificial intelligence (HAI) interactions to distinguish whether the user is a human or a computer program. Various CAPTCHA schemes have been proposed over the years, principally to increase usability and security against emerging bots and hackers performing malicious operations. However, automated attacks have effectively cracked all common conventional schemes, and the majority of present CAPTCHA methods are also vulnerable to human-assisted relay attacks. Invisible reCAPTCHA and some approaches have not yet been cracked. However, with the introduction of fourth-generation bots accurately mimicking human behavior, a secure CAPTCHA would be hardly designed without additional special devices. Almost all cognitive-based CAPTCHAs with sensor support have not yet been compromised by automated attacks. However, they are still compromised to human-assisted relay attacks due to having a limited number of challenges and can be only solved using trusted devices. Obviously, cognitive-based CAPTCHA schemes have an advantage over other schemes in the race against security attacks. In this study, as a strong starting point for creating future secure and usable CAPTCHA schemes, we have offered an overview analysis of HAI between computer users and computers under the security aspects of open problems, difficulties, and opportunities of current CAPTCHA schemes.

One of the most important issues in modern educational processes is application of computer tools supporting learning activities. In our research we propose to use cognitive information systems,which allow to support teaching thanks to the computer visual data understanding. Such systems can facilitate learning activities by improving perception abilities for students while acquiring new knowledge, or competence.

Summary Chatbots are artificial communication systems becoming increasingly popular and not all their security questions are clearly solved. People use chatbots for assistance in shopping, bank communication, meal delivery, healthcare, cars, and many other actions. However, it brings an additional security risk and creates serious security challenges which have to be handled. Understanding the underlying problems requires defining the crucial steps in the techniques used to design chatbots related to security. There are many factors increasing security threats and vulnerabilities. All of them are comprehensively studied, and security practices to decrease security weaknesses are presented. Modern chatbots are no longer rule‐based models, but they employ modern natural language and machine learning techniques. Such techniques learn from a conversation, which can contain personal information. The paper discusses circumstances under which such data can be used and how chatbots treat them. Many chatbots operate on a social/messaging platform, which has their terms and conditions about data. The paper aims to present a comprehensive study of security aspects in communication with chatbots. The article could open a discussion and highlight the problems of data storage and usage obtained from the communication user—chatbot and propose some standards to protect the user.

In advanced data processing systems, one of the most important paradigms for distributed data analysis is innovative and transformative computing approaches. Such solutions allow not only data analytics tasks to be facilitated, but also intelligent and secure information analysis oriented especially for applications of new technologies and computational intelligence techniques. Nowadays there is a great demand to efficiently store and analyze a huge amount of information, originated from distributed sources or sensors, as well as an expectation to manage such information in a secure manner for applications in ubiquitous and mobile computing. The possibility of creation and development of such computation technologies will be connected with the introduction of new transformative computing procedures dedicated to security, big data analysis and cloud computing technologies. These subjects, as well as others, connected with innovative computational models for transformative computing technologies, data security, security protocols, and distributed data analysis will form the subject of this Transformative 2020 special issue. The main topics of transformative computing in security, big data analysis and cloud computing applications presented at Transformative 2020 are primarily oriented at new computational approaches for big data and cloud security, transformative computing applications, personalized cryptography and biometric security, ambient intelligence, innovative security and privacy protocols, security of cognitive information systems, cryptography and secret data management, computational intelligence in data and services management, security and privacy for mobile and distributed systems, visual and cognitive CAPTCHA, cognitive approaches for big data analytics, advanced cognitive steganography systems, behavioral features in data analysis and security solutions, and transformative approaches for big data analytics. This special issue features 12 papers, which present high quality scientific research, and interesting cutting-edge topics. The first paper entitled “Customizing intelligent recommendation study with multiple advisors based on hierarchy structured fuzzy-analytic hierarchy process” by Park et al.1 proposes a new system that integrates a multi advisory function. The proposed solution starts from problem definition and continues to define the required solving task. Such technology supports customized information on defining problems and enables the definition of the requirements to characterize user features. The second paper “Time-based legality of information flow in the capability-based access control model for the Internet of Things” by Nakamura et al.2 introduces a new idea of time based legality of information flow. In the capability-based access control (CBAC) model, each subject has a capability token with which users can effectively manage a device. The authors propose a time-based operation interruption protocol (TBOI) to prevent illegal information—at any given time and at a later date. The third paper entitled “Transformative and Cognitive Approaches to Information Retrieval and Security Procedures” by Ogiela3 describes cognitive approaches in data security and a transformative computing paradigm dedicated to creation of human-centered security protocols. Such transformative computing applications focus on data exploration in distributed systems. The fourth paper “A Machine Learning-Based Memory Forensics Methodology for TOR Browser Artifacts” by Pizzolante et al.4 presents a bottom-up formal investigation model for the memory forensics of the Tor Browser. This methodology was developed based on a bottom-up logical approach for collecting information from different abstraction levels. The fifth paper entitled “Healthcare Fraud Detection Using Primitive Sub Peer Group Analysis” by Settipalli and Gangadharan5 presents new algorithms for identifying suspicious behaviors in health insurance. In this paper, a primitive sub peer group analysis (PSPGA) based on peer group analysis (PGA) and pattern interpretation and analysis is proposed for identifying user behaviors. The PSPGA recognizes drifts and classifies them as correct or fraudulent. The sixth paper “New Cognitive Sharing Algorithms for Cloud Service Management” by Ogiela and Ogiela6 presents new algorithms based on the meaning description dedicated to data analysis and security processes. The authors describe the possibility of applying such algorithms, depending on the structure of the target system, especially in cloud computing. This paper also discusses examples of two-stage secret protection algorithms—the simple data protection and the information set with semantics. The seventh paper entitled “Protocol Fuzzing to Find Security Vulnerabilities of RabbitMQ” by Kwon et al.7 shows a new fuzzy protocol for systems and service communications. A message broker named RabbitMQ is developed to find unknown vulnerabilities inherent in software. Their simulations demonstrate that the proposed algorithm is able to solve tasks by using the RabbitMQ especially in data security processes. The eighth paper “A deep learning-based indoor-positioning approach using received strength signal indication and carrying mode information” by Lin et al.8 describes a new indoor positioning scheme—learning-based indoor positioning system (LEIPS) which is used for identification of smartphone users by using inertial sensors and deep learning algorithms. Their experimental results demonstrate that the LEIPS has reached 96% of positioning accuracy. The ninth paper entitled “Optimizing Resource Scheduling Based on Extended Particle Swarm Optimization in Fog Computing Environments” by Narayana et al.9 introduces the extended particle swarm optimization (EPSO) algorithm which was developed with additional gradient method for optimizing scheduling tasks in cloud-fog environments. It also improves the efficiency of data analysis and minimizes the time of their implementation. The tenth paper “Population Data Mobility Retrieval at Territory of Czechia in Pandemic Covid-19 Period” by Platos et al.10 addresses the selection and collection steps of data analysis on mobile phones at the Czech Republic during the Covid pandemic. A data collection architecture is then proposed for spatial temporal mobility analysis. The analysis precision including the pandemic and non-pandemic periods is also shown. The eleventh paper entitled “Design and Analysis of Efficient Neural Intrusion Detection for Wireless Sensor Networks” by Batiha and Krömer11 analyses the acceleration of a neural intrusion detection model. The model was developed to detect intrusion/malicious behaviors for wireless sensor networks. The authors present their computational experiments with classification accuracy and training efficiency on different devices. In the last paper “Improved Publicly Verifiable Auditing Protocol for Cloud Storage” by Zhang et al.,12 the authors describe an outsourcing data protocol for cloud systems which is one of the new full integrity cloud auditing protocols. In addition to identifying its weaknesses, authors also analyze this protocol and present its security issues. This special issue introduces new solutions of transformative computing paradigms on different important topics. A part of them focuses on security and system protection. Some solve urgent network problems. All these techniques are oriented from information flow processes and machine learning in transformative computing, cognitive and semantic description of data management and security, fuzzy protocols and deep learning positioning, intrusion detection, fog-cloud applications, and big data collection and analysis processes. The wide range and impacts of the presented papers indicate an extraordinary variety of transformative computing paradigms, applications, and approaches. The authors also raise important open topics and their solutions. Following that, authors also show the possibility of further development on the discussed aspects. We would like to specially thank the Editors in Chief—Professor Geoffrey Fox, who first gave the opportunity of publishing this issue, and Professor David W. Walker who led this work at all times to the end. We are especially grateful for the opportunity to present this Special Issue and for their great kindness and help, as well as the unique opportunity to present new and interesting scientific works in Concurrency and Computation: Practice and Experience. We would also like to thank all the authors who have submitted their papers to this Special Issue. We congratulate all authors whose works have been accepted and positively evaluated. These works bring a great contribution to the development of the computer science, show new directions for research, as well as an innovative view of the previously developed experience and science.

Summary This article will present new classes of confidential data management algorithms for the protection and safeguarding of classified data. These algorithms will constitute a solution dedicated to supporting the process of safeguarding and protecting information with various meanings and in different forms. A special type of data is services, understood as information subject to the processes of management and protection. These processes will be discussed on the example of selected solutions dedicated to the concealment of information with secret character. A novelty presented in this article is the possibility to apply algorithms based on the meaning interpretation of the analyzed data in order to secure the data properly; there is also a possibility of executing the proposed solutions regardless of the structure, in which these solutions are to be implemented. An effective execution of data protection tasks will be discussed on the example of two‐stage secret protection algorithms, that is, the protection of the basic data set and the information set with meanings generated on the basis of the semantic secret analysis. Standard secret division approaches are not dependent on semantic content of shared information. In this article, we will be presented such novel approach, which allow to divide data depending on its semantic meaning or content. The problem of semantic meaning evaluation will be solved using cognitive information systems, allowing to extract meaning hidden in the splitted data.

Summary This article presents new methods of securing data with particular emphasis on their applications in Cloud Computing. Methods of hiding data dedicated to processes of its security will be illustrated by cryptographic techniques, particularly data splitting protocols. Cryptographic information sharing algorithms are a particularly important type of techniques allowing confidential or secret data to be secured. These algorithms have been enhanced with new classes of solutions, including semantic protocols of secret sharing. The innovation of this solution consists in the presentation of smart data securing algorithms that use techniques of the semantic sharing of secret information. The information subjected to the process of splitting and hiding can take various forms, including that of services. Because of this aspect, namely, the various forms of the secret, and the use of universal information hiding techniques, a particularly important aspect of the solutions presented here is the ability to use them in various areas of data security. One of such areas comprises Cloud Computing, which forms the leading subject of this publication. In this article, the authors describe intelligent cryptography techniques dedicated to security services data. The most important achievement is the introduction of a new class of data encryption protocols, such as semantic threshold schemes.

Summary 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.

This paper presents new application of eye tracking devices oriented for creation of secure authentication protocols with visual CAPTCHA codes. Many classes of visual CAPTCHA can require specific information, knowledge or even high perception abilities for proper selection of visual parts during authentication procedure. In such procedures we can use contactless interfaces like eye tracking devices for indicating selected CAPTCHA parts. Application of such technologies allow to trace the points of attention, which users can focus on proper elements in required order, necessary for successful authentication.

In advanced distributed computer systems, one of the most important approaches for big data analysis and Cloud information management is innovative analysis protocols and cognitive system application. Such solutions should facilitate not only to perform the big data analytics tasks but also intelligent and secure data and services management, especially using innovative machine and computational intelligence approaches. Presently, there is a great demand to efficiently store and semantically analyze a great amount of information, originated from different sources, as well as manage such information in secure manner for different application in ubiquitous and mobile computing. Possibility of creation and development of such computation technologies was connected with introduction of new computing paradigms and information systems, which allows to combine cognitive informatics with security areas, as well as big data and cloud computing technologies. These subjects, as well as others, connected with innovative computational models for security protocols and distributed data analysis were form the subject of CogInnov2018 Special Issue. The main topics of this Special Issue include the new computational approaches dedicated to Big data and Cloud security, cognitive information systems with their applications, personalized cryptography and biometrics security, ambient intelligence for Big data analysis, innovative security and privacy protocols, security of Cognitive Information systems, visual cryptography and secret data management, computational intelligence in services management, security and privacy for mobile and distributed systems, visual and cognitive CAPTCHA, advanced steganography systems, behavioral features in security solutions, as well as cognitive approaches for Big data analytics. This Special Issue selected and accepted 9 papers, which presents high quality scientific research and interesting cutting-edge topics. The paper entitled “Packer Identification Method Based on Byte Sequences” by B.H. Jung, S.I. Bae, Ch. Choi, and E.G. Im1 proposes a new packer identification method that uses two types of statistical features. These features are generated using encrypted data and feature from byte frequency distributions. To extract an encrypted data, the authors used on entropy scoring-based method. Experimental results are very optimistic and can identify packers with the accuracy approximately 91.6% on average, when the Random Forest algorithm is used. In the paper “Compression-based Steganography” by B. Carpentieri, A. Castiglione, A. De Santis, F. Palmieri, and R. Pizzolante2 new idea of steganography was proposed. The main aspects of this paper consists compression based steganography. The authors exploited the hierarchical structure of a compressed archive by using new algorithms and protocols to propose their solution. The proposed algorithm can be useful in many everyday situations, for example, alerts and data protection. The paper entitled “Personal Identification Study for Touchable devices with ECG” by H. Ko, S.B. Pan, and L. Mesicek3 describes new points of view for personal identification processes. The authors proposed analysis by ECG signal and the users' signal trend. These trends can be describe by analysis of selected values, as well as ARL, AFL, ART, ADP, calrms, calstd, and stdRR. These items can makes a pattern graph and threshold scope for a user. In the paper “Information flow control in object-based peer-to-peer publish/subscribe systems” by S. Nakamura, T. Enokido, and M. Takizawa4 the P2PPSO (P2P Publish/Subscribe with Object concept) system was presented. These systems can use also TOBS protocols, in which illegal objects are not delivered to the target peer. Evaluation processes show number of event messages carry illegal objects in the TOBS protocol. The paper entitled “A Multilevel Graph Approach for Rainfall Forecasting: A preliminary Study Case on London area” by F. Clarizia, F. Colace, M. De Santo, M. Lombardi, F. Pascale, D. Santaniello, and A. Tuker5 presents innovative approach such as the Multilevel Graph Approach to the hydrological analysis. It is possible by using the proposed methodology at the service of Early Warning Systems. The presented methods with Bayes Networks, Ontologies, and the Context Dimension Tree were successfully used in the prediction of the road accident risks within a specific borough of London. In the paper “Effect of Size of Giant Component for actor node selection in WSANs: A comparison study” by D. Elmazi, M. Cuka, M. Ikeda, and L. Barolli6 two Fuzzy Based Systems FBS1 & FBS2 for actor selection in WSANs were presented. The proposed systems decided whether the person will be selected for the required job/or not, based on information supplied by sensors and condition. The authors evaluated proposed solution by computer simulations. Comparison of FBS1 and FBS2 shows that Size of Giant Component parameter that was used in FBS2 gives better results due to the growth of SGC parameter. The paper entitled “A MapReduce based Modified Grey Wolf Optimizer for QoS-aware Big Service Composition” by B. Bhaskar, Ch. Jatoth, G.R. Gangadharan, and U. Fiore7 presents new efficient QoS-aware Big service composition by applying a MapReduce based on Modified GreyWolf Optimizer (MR-MGWO). It explores more search space dedicated to multidimensional environment. The authors present optimal balance of exploration and exploitation in their solution that enhances the convergence rate and minimizes the computational time. The authors show that the performance of MR-MGWO is superior to other similar approaches for solving Big service composition. In the paper “Cognitive security paradigm for cloud computing applications” by L. Ogiela and M.R. Ogiela8 new paradigms of data security were described. The presented algorithms are dedicated to enhancing the cryptographic data sharing schemes. New proposition was enriched by linguistic and biometric protocols 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 schemes is the application of cognitive algorithms to describe correctly the shared data. The paper entitled “JPEG Steganography with Particle Swarm Optimization Accelerated by AVX” by V. Snasel, P. Kromer, J. Safarik, and J. Platos9 presents new digital steganography aspects. These types of steganography aim at hiding secret data in digital form transmitted over insecure channels. The authors presented the JPEG format in digital protocols often used as cover objects in digital steganography. Optimization methods improved the properties of data with embedded secret. Moreover, it is necessary to introduce additional computational complexity in the processing stage. AVX instructions available in modern CPUs were used to accelerate secret parallel operations of image steganography. This Special Issue has described new aspects of cognitive and innovative computation paradigms dedicated especially for novel big data and cloud computing applications. Cognitive paradigms were oriented by semantic aspects of data description, analysis, interpretation, and security. Innovative paradigms were presented with reference to new possibilities and areas of application of the discussed solutions. A wide range of the presented topics indicates an extraordinary variety of analysis processes and data protection. The issues discussed were mostly open topics, which indicates the possibility of further development of the discussed topics. LIDIA OGIELA Professor Lidia Ogiela, Ph.D. – computer scientist, mathematician, economist. She received Master of Science in mathematics from the Pedagogical University in Krakow, Poland, and Master of Business Administration in management and marketing from AGH University of Science and Technology in Krakow, Poland, both in 2000. In 2005, she was awarded the title of Doctor of Computer Science and Engineering at the Faculty of Electrical, Automatic Control, Computer Science and Electronic Engineering of the AGH University of Science and Technology for her thesis and research on cognitive analysis techniques and its application in intelligent information systems. In 2016, she received habilitation (docent title) at VSB Technical University of Ostrava in Czech Republic. In 2018, she was awarded the second title of Doctor of Computer Science and Engineering at the Hosei University of Tokyo, Japan, for her thesis and research oriented on human centered computing for future generation computer systems. She is an author a more than 190 scientific international publications on information systems, cognitive analysis techniques, biomedical engineering, security techniques, and computational intelligence methods. She is a member of few prestigious international scientific societies such as SIAM – Society for Industrial and Applied Mathematics, IEEE Computer Society, CSS Cognitive Science Society, and SPIE – The International Society for Optical Engineering. Currently, she is at the professor position and works at Institute of Computer Science at Pedagogical University of Krakow, Poland. I would like to specially thank the Editor in Chief Professor Geoffrey Fox for the opportunity to run this Special Issue and for his great kindness and help, as well as unique opportunity to present new and interesting scientific works in Concurrency and Computation: Practice and Experience Journal. I would like to thank all the authors who have submitted their papers to this Special Issue. I congratulate the authors whose works have been accepted and positively evaluated. These works bring a great contribution to the development of the computer science and show new directions of research works as well as an innovative view of the previously developed experience and science.

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.

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.

The aim of the article is to discuss security of IT systems and to identify elements that should be protected against unauthorized access. Software protection is created based on the characteristics of cyberspace. The complexity of IT systems makes security more complicated and unreliable. The development of technology is inseparably connected with the development of threats, which means that the improvement of software solutions has become an extremely important aspect for all users. The article is also intended to indicate what security elements should be considered as both the system user and when creating the security system. This paper presents topic of cognitive security aspects dedicated to management processes.

In the peer-to-peer (P2P) type of topic-based publish/subscribe (P2PPS) model, each peer process (peer) can publish and subscribe event messages which are characterized by topics with no centralized coordinator. A peer pj publishes an event message ej after receiving an event message ei, i.e. ei causally precedes ej. Here, the event message ej may carry information of the event message ei. If a peer pk receiving ej is not allowed to subscribe the topics of ei, the peer pk illegally obtains the information by receiving ej. In our previous studies, the SBS, TBS, and FS-H protocols are proposed to prevent illegal information flow. In addition, if a source peer pi publishes an event message e without giving related topics or with giving unrelated topics, a destination peer pj may misunderstand the meaning of the event message e. Here, malicious information flow occurs. In this paper, we newly propose a TBSM (topic-based synchronization to prevent malicious information flow) protocol. Here, event messages which may cause malicious information flow are banned. In the evaluation, we show the number of event messages banned in the TBSM protocol is larger than the TBS protocol since the number of event messages which are just malicious is larger than just illegal.

This paper presents topic of cognitive systems dedicated to management processes. In this paper will be described the cryptographic techniques dedicated to sharing processes with cognitive interpretation stages. Also, safety analysis of these methods will be described. Such algorithms will be presented especially in Cloud Computing context. This solution allow protecting confidential information, and performing secure operation for different kinds of information.

In this paper will be described the new way of using cognitive cryptography in data security applications. Cognitive features and personal information may be used in creation of advanced data security techniques, and communication protocols. Such personally oriented procedures may play important role in advanced secure management applications and authentication tasks. Additionally some perception and behavioural features allow to generate unique sequences, which also may be used in modern cryptographic application. Such possibilities will be described in this paper.

Summary Current visual CAPTCHA designs are incorporating a number of techniques, which should prevent bots or unauthorized users from correct image fragmentation and symbol recognition, but the designs are varying very much in level of safety and robustness and may not be user‐friendly enough for broader use. This paper attempts to evaluate strengths and weaknesses of current solutions, from the security and user‐friendliness point of view. It also presents another approach to visual CAPTCHA generating algorithm that consists of many different styles of letters, combined with a purposeful background. Depicted CAPTCHA was created with the focus centered on strengthening scheme against different image detection techniques, especially character and pattern recognition. Such CAPTCHA can be applied for different security purposes in modern Cloud of Things technologies. In this paper is also presented an idea of cognitive CAPTCHAs which required special knowledge and perceptual skills. Such CAPTCHA may play important role in future security protocols.

Summary 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.

Summary 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.

In the peer-to-peer type of topic-based publish/subscribe (P2PPS) model, each peer (process) can publish and receive event messages with no centralized coordinator. A peer is allowed to publish and subscribe an event message with topics which are in the publication and subscription topics of the peer. Some information may flow from a peer to another peer if the peer publishes an event message to the other peer. We have to prevent illegal information flow to occur by publications and notifications of event messages. In our previous studies, the subscription-based synchronization (SBS), subscription initialization SBS (SI-SBS), topic-based synchronization (TBS), and SI-TBS protocols are proposed. In the SBS and SI-SBS protocols, it is checked whether or not illegal information flow to occur in terms of subscription and publication rights granted to each peer. However, even some legal notifications are banned while no illegal event message is notified. In the TBS and SI-TBS protocols, only topics which each peer manipulates are considered. In this paper, we evaluate the protocols in terms of the number of notifications banned.

In this paper will be described a new way of using cognitive approaches in personalized cryptography algorithms. Personal information may be used in creation advanced security protocols, as well as personal keys for cryptosystems, and information encoding procedures. Such protocols may play important role in advanced secure management applications. Additionally some behavioral features allow to generate unique sequences, which also may be used in modern cryptographic application. Such possibilities will be described in this paper.

This paper presents topic of data sharing algorithms used to management processes of strategic data. In this solution will be describe the cryptographic techniques dedicated to splitting and sharing processes. Also, safety analysis of these methods will be discussed. The safety analysis and standardization methods of data sharing algorithms for management processes are new solution and very important in aspects of data security, especially in management application. Data splitting and sharing protocols are dedicated for secure data in different distribution processes. Such algorithms allow protecting confidential information, and performing secure operation for different kinds of data.

In this paper will be presented a new computational model connected with application of cognitive cryptography for cryptographic areas. This new cryptographic paradigm is introduced with relation to advanced information systems, focused on semantic data evaluation for cryptographic applications, as well as procedures using cognitive models of data processing. Such systems are mainly designed to use the semantic meaning of encrypted information to select the most effective method of encoding or securing. In this paper will be presented some possible applications of such techniques, especially for information sharing and management application. Cryptographic solutions inspired by biological models will be also described for such applications.

This paper will present new possibilities of information sharing with the use of biometric techniques. Various biometric techniques used in personal identification processes will constitute the basis for new data classification solutions. The process of data classification will be possible owing to the use of cryptographic techniques of sharing information, enriched by elements of biometric processes of personal identification. This type of solutions will serve to support the processes of managing strategic information.