Abstract: System and method secures critical data on distributed ledger (DL) or blockchain (Bchn) or linked memory cell (LCM) data construct. Each cell in DL/Bchn/LCM has cellular immutability and cellular connectivity characteristics. System randomly stores critical data in the DL/Bchn/LCM based with initial state function and stores nonsensical data in other cells based upon nonsensical initial state function which is inferior to the critical data function. The DL/Bchn/LCM has a stem, with stem cells, forking cells and branches with branch cells created by forking initial state function (sometimes triggered by cyber attacks, sometimes by new growth). Trees of DL/Bchn/LCM are formed as are forests of DL/Bchn/LCM trees. automatic information search operations (AISO) through data collections also grow stems and branches on the DL/Bchn/LCM trees. The AISO generates supplemental (Supple) data based on supple initial state function.

Abstract: Data processing method organizing and processing data in a distributed computing system. By organizing select/secret content with [enterprise and external designated] categorical filters (content, contextual and taxonomic) to create further search terms for data mining both enterprise and external data sources (databases, data collections, data stores). The result being aggregated select/secret content. The aggregated select/secret content is stored in the corresponding select/secret content data store. The aggregated select/secret content is further processed for convergent or divergent characteristics. By identifying unknown data elements, another search gathers supplemental documents. Data element ranges are set by taxonomic filter and said contextual filters. Relevancy factors are identified by relationship between input and supplemental documents. Search is controlled by user selection, continuous search, iterative search (n cycles), search within m search terms, and search time.

Abstract: Data processing method organizing and processing data in a distributed computing system. By organizing select/secret content with [enterprise and external designated] categorical filters (content, contextual and taxonomic) to create further search terms for data mining both enterprise and external data sources (databases, data collections, data stores). The result being aggregated select/secret content. The aggregated select/secret content is stored in the corresponding select/secret content data store. The aggregated select/secret content is further processed for convergent or divergent characteristics. By identifying unknown data elements, another search gathers supplemental documents. Data element ranges are set by taxonomic filter and said contextual filters. Relevancy factors are identified by relationship between input and supplemental documents. Search is controlled by user selection, continuous search, iterative search (n cycles), search within m search terms, and search time.

Abstract: The method and system identifies and classifies and monetizes intangible asset (I.A.) unstructured digital data in a computer system by ownership characterizations and intellectual property (I.P.) classifications. Enterprise and non-enterprise data collections are searched based upon an expanded project specification (of descriptive functions, characteristics and objectives). Primary search terms from the project are expanded by content, contextual and taxonomic analysis to generate a plurality of search string parameters employed to search the data collections. The search engine returns data and documents, ownership characteristics are ascertained by comparison of the returns and I.P. classifications are made based upon the returns. This process also handles unstructured enterprise specific data not otherwise located in the data collections or unstructured data from other I.A. programs such as H.R. performance modules, supply chain modules or CRM modules.

Abstract: Method of creating data stores by either processing data throughput or a method for processing data throughput or data mining in a distributed computing system works. The system and method operates on sensitive content (e.g., trade secrets) or select content (e.g., critical content) and applies configurable filters (expanding, contracting, hierarchical/orthogonal classifier filters) to separate and store sensitive/select data in designated, distributed data stores or to supplement data collections. Sensitive/select data stores may have security clearance requirements.

Abstract: Method of processing and organizing (sometimes sanitizing) data throughput in an information infrastructure of a distributed computing system with configurable filters. Data stores are created with the data throughput. Filters operate on data to locate, extract, separate and store sensitive content (e.g., trade secrets) or select content (e.g., critical content) in designated, distributed data stores. Configurable filters expand sensitive/select data throughput, contract throughput, inferencing throughput, and hierarchical/orthogonal classifier filters reorganize the sensitive/select data, all to segment and store sensitive/select data in designated stores. Data output enables (a) sanitation; (b) data storage organization; (c) classification; (d) application of data processing (copying, distribution, archiving, etc.).

Abstract: Method of organizing and storing data in an information infrastructure by either processing data throughput or a method for processing data throughput or data mining in a distributed computing system works using a tagging routine. The system and method operates on sensitive content (e.g., trade secrets) or select content (e.g., critical content) and applies configurable filters (expanding, contracting, hierarchical/orthogonal classifier filters) to separate and store sensitive/select data in designated, distributed data stores or to supplement data collections. Sensitive/select data stores may have security clearance requirements. Data output is controlled to provide one or more of storage in predetermined data stores, data mining, coping, transfer to other storage sites, supplemental data searches, and presentation subject to data inquiry.

Abstract: Method of creating an information infrastructure for processing data throughput or a method for processing data throughput in a distributed computing system works on sensitive content (e.g., trade secrets) or select content (e.g., critical content) and generates or reduces sensitive/select data with configurable filters (expanding, contracting, hierarchical/orthogonal classifier filters) to separate and store sensitive/select data in designated, distributed data stores. Sensitive/select data stores may have security clearance requirements.

Abstract: Distributed computer system processes data having select content (SC) represented by one or more predetermined words, characters, etc. The system has a plurality of SC data stores in a server cloud for respective security designated (Sec-D) data and granular data, each with respective access controls thereat. The data stores are operatively coupled on a network. An identification module identifies SC and granular data stores for the respective data in the server cloud. A processor activates data stores in the server cloud thereby permitting access to the SC data and granular stores based upon an application of one or more access controls thereat. The processor has a reconstruction module operating as a data process employing the respective access controls to combine one or more of the Sec-D data and granular data.

Abstract: The method, program and information processing system secures data, and particularly security sensitive words, characters or data objects in the data, in a computer system with territorial, geographic and triggering event protocols. The method and system determines device location within or without a predetermined region and then extracts security data from the file, text, data object or whatever. The extracted data is separated from the remainder data and stored either on media in a local drive or remotely, typically via wireless network, to a remote store. Encryption is used to further enhance security levels. Extraction may be automatic, when the portable device is beyond a predetermined territory, or triggered by an event, such a “save document” or a time-out routine. Reconstruction of the data is permitted only with security clearance and within certain geographic territories. An information processing system for securing data is also described.

Abstract: Method of creating data stores by either processing data throughput or a method for processing data throughput or data mining in a distributed computing system works. The system and method operates on sensitive content (e.g., trade secrets) or select content (e.g., critical content) and applies configurable filters (expanding, contracting, hierarchical/orthogonal classifier filters) to separate and store sensitive/select data in designated, distributed data stores or to supplement data collections. Sensitive/select data stores may have security clearance requirements.

Abstract: Method of creating an information infrastructure for processing data throughput or a method for processing data throughput in a distributed computing system works on sensitive content (e.g., trade secrets) or select content (e.g., critical content) and generates or reduces sensitive/select data with configurable filters (expanding, contracting, hierarchical/orthogonal classifier filters) to separate and store sensitive/select data in designated, distributed data stores. Sensitive/select data stores may have security clearance requirements.

Abstract: Method of processing and organizing (sometimes sanitizing) data throughput in an information infrastructure of a distributed computing system with configurable filters. Data stores are created with the data throughput. Filters operate on data to locate, extract, separate and store sensitive content (e.g., trade secrets) or select content (e.g., critical content) in designated, distributed data stores. Configurable filters expand sensitive/select data throughput, contract throughput, inferencing throughput, and hierarchical/orthogonal classifier filters reorganize the sensitive/select data, all to segment and store sensitive/select data in designated stores. Data output enables (a) sanitation; (b) data storage organization; (c) classification; (d) application of data processing (copying, distribution, archiving, etc.).

Abstract: Method and system of organizing and processing data in a distributed computing system having designated, distributed data stores for sensitive content (e.g., trade secrets) or select content (e.g., critical content). Sensitive/select data is extracted via configurable filters and stored in the designated data stores, sometimes subject to security controls, with limiting distribution functions and controlled release of the sensitive/select data. Distribution is limited due to designated, configurable filters.

Abstract: Method of organizing and storing data in an information infrastructure by either processing data throughput or a method for processing data throughput or data mining in a distributed computing system works using a tagging routine. The system and method operates on sensitive content (e.g., trade secrets) or select content (e.g., critical content) and applies configurable filters (expanding, contracting, hierarchical/orthogonal classifier filters) to separate and store sensitive/select data in designated, distributed data stores or to supplement data collections. Sensitive/select data stores may have security clearance requirements. Data output is controlled to provide one or more of storage in predetermined data stores, data mining, coping, transfer to other storage sites, supplemental data searches, and presentation subject to data inquiry.

Abstract: The present invention is a method of organizing and processing data in a distributed computing system. The invention is also implemented as a computer program on a computer medium and as a distributed computer system. Software modules can be configured as hardware. The method and system organizes select content which is important to an enterprise operating said distributed computing system. The select content is represented by one or more predetermined words, characters, images, data elements or data objects. The computing system has a plurality of select content data stores for respective ones of a plurality of enterprise designated categorical filters which include content-based filters, contextual filters and taxonomic classification filters, all operatively coupled over a communications network.

Abstract: The method for securing data includes establishing a group of security sensitive items, filtering data and extracting and separating the security items from remainder data. The filtered data are separately stored (locally on a PC or on another computer in a LAN or WAN or on the Internet.) A map may be generated. The filter and/or map may be destroyed or stored. The data input, extracted data and remainder data may be deleted from the originating computer. Encryption may be utilized to enhance security (including transfers of data, filter and map). Reconstruction of the data is permitted only in the presence of a predetermined security clearance. A plurality of security clearances may be used to enable a corresponding plurality of partial, reconstructed views of the plaintext (omitting higher security words).

Abstract: A method and system processes data in a distributed computing system to survive an electromagnetic pulse (EMP) attack. The computing system has proximal select content (SC) data stores and geographically distributed distal data stores, all with respective access controls. The data input or put through the computing system is processed to obtain the SC and other associated content. The process then extracts and stores such content in the proximal SC data stores and geographically distributed distal SC data stores. The system further processes data to geographically distribute the data with data processes including: copy, extract, archive, distribute, and a copy-extract-archive and distribute process with a sequential and supplemental data destruction process. In this manner, the data input is distributed or spread out over the geographically distributed distal SC data stores. The system and method permits reconstruction of the processed data only in the presence of a respective access control.

Abstract: Distributed computer system processes data having select content (SC) represented by one or more predetermined words, characters, etc. The system has a plurality of SC data stores in a server cloud for respective security designated (Sec-D) data and scarcity designated (S-D) data, each with respective access controls thereat. The SC data stores are is operatively coupled on a network. An identification module for identifying SC data stores for the Sec-D data and S-D data in the server cloud. A processor activates an SC data stores in the server cloud thereby permitting access to the SC data stores and respective Sec-D data and S-D data based upon an application of one or more of said access controls thereat. The processor has a reconstruction module operating as a data process employing the respective access controls to combine one or more of the Sec-D data and S-D data.

Abstract: The method of quantifying risk, implemented as a computerized program, quantifies the risk of releasing security sensitive words, data objects, characters or icons which may be part of data subject to analysis (target data). Security words, etc. are categorized, pre-existing data for each category is obtained and the categories (and subsumed pre-existing data) are ranked by risk. The target data is compared to the compiled pre-existing data and a risk output is generated. For unknown or undefined words, an indeterminable category is created and is ranked. The method may include inference engines, and contextual routines to add semantic equivalents and opposites to the critical list. Search engines may be employed to add to the list. A differential rank quantifier is assigned to the security words, etc. which has a different rank than the associated category. Frequency analysis, source analysis and stochastic analysis is also used. The risk output is altered.