A.0 INTRODUCTION

The present invention relates to information management data processing modules and computer software system and method enabling protection of confidential information, identification of such information, identification of select content, classification for the same, and security system for the same. Businesses and enterprises use information in many different types of forms and format. Structured information is typically data found in database or spreadsheet formats or stored in self-defining formats wherein the information is managed by a software application. A further example of self-defining format is XML documents in format schematics. Some information is “semi-structured” wherein the information has certain defined fields such as email and the structured data fields from, to, cc, bcc and subject, but the body of the email message is unstructured. Unstructured information includes documents generated by end users with the assistance of applications program such as Microsoft Office, word processing programs, image processing programs (Adobe), files downloaded from the Internet, audio and video files etc. Enterprises focus their attention on mission critical applications and data relative thereto which is often located in structured databases or other structured data formats. However, a large majority of information is contained in unstructured or semi-structured formats. There are significant risks involved in handling and locating confidential information in these information files as well as identifying and handling intellectual property data in those files.

The content of these information files are important from a legal standpoint, a data security standpoint, and a government regulation or compliance standpoint. Although the content of the information files may be searched and indexed, the resulting information must then be classified. The enterprise must establish policies which dictate how the information files are handled in light of legal, security and compliance risk factors. Further, enterprise policies regarding information or document retention and immutability (the degree and extent that a particular document or information file may be changed) must be established and enforced.

Simply indexing the content of an information file is not adequate. Semantic analysis, key word tagging and classification categorization (taxonomic analysis) should be conducted. Information tools and computer programs and methods should enable the users (an enterprise) to analyze, categorize and actively manage unstructured information in the enterprise. Theft or accidental loss of customers' information or employees' private information is no longer acceptable. Laws mandate disclosure of such data loses. Further, the public is acutely aware of identity theft and privacy issues. Also, theft or misuse of intellectual property and other confidential business information is a mission critical aspect of many enterprises. To complicate matters, businesses and enterprises do not operate closed informational systems but often times invite partners, customers, vendors, contractors and other third parties to participate in the enterprise informational system. Therefore, most enterprises operate an open ecosystem informational structure. Employees of the enterprise in addition to its partners, customers, vendors and others participate in the production of information and the use/and consumption of information.

In the past, most commentators believed that a breach of confidentiality and misuse of data was a result of an outsider “hacker” getting access to enterprise information by penetrating a firewall or other security system. However, there is a greater risk to the enterprise that rogue insider will take the information and misuse that information. Further, trusted employees constantly copy files onto laptops, PDAs (personal data assistance), USB thumb drives (flash memory), CDs and other removable media in order to fulfill the tasks assigned by the enterprise. This portable data can be easily misplaced or stolen. Further, the ease of electronic transfer of documents to others exposes the information contained therein to mis-directed addresses, as well as unauthorized theft and misuse.

Therefore, enterprises and organizations must manage the access to the information and use of the information and monitor such access and use and audit (log) these activities. A system of information rights management should concern the following aspects: (1) control who can open, print or edit a document or information file; (2) prevent or limit distribution of the information file; (3) enable the enterprise to change permissions on an information file per enterprise policy or changed conditions; and (4) maintain an audit trail of such activity.

The information infrastructure tools also must accommodate the changeable value of information over the life cycle of the information file. Although the information and content of the file does not change, the value of the information changes due to contextual events and external environmental conditions. External events which change the value of information and information file can be summarized as follows: (a) creation and modification and use of the information in the information file as it is processed by the enterprise; (b) completion of the project or event associated with the information file; (c) government regulations imposed on the information; (d) legal actions associated with the content of the information file and financial audits related to such information; (e) distributing the information in the information file to individuals with different access or security clearances or rights; (f) distribution of the information, both physically and electronically, to others who are not in the enterprise; (g) the types and operation of storage devices and the media which retain the information files; (h) the discovery of new uses for the information content in the file; (i) new software applications which replace obsolete applications; (k) personnel changes within the enterprise and associated changes and permissions to access, control and modify the information file; (l) expansion and contraction of the enterprise itself (organizational changes) and the changeable nature of business relationships with partners, customers and suppliers.

In view of the foregoing, it is necessary to have an information classification system or engine which automatically categorizes information in unstructured information files and labels the same and permits the enterprise to establish a policy for that information and implement the policy recommendation with specific actions to ensure that the information is properly handled, distributed, retained, deleted (document retention) and otherwise managed.

OBJECTS OF THE INVENTION

It is an object of the present invention to provide a system operator or CIO with data processing tools to manage and organize data processed by an enterprise.

It is a further object of the present invention to provide tools for securing secret or security sensitive sec-con data in the enterprise computer system and to locate, identify and secure select content SC which may be of interest or importance to the enterprise.

It is an additional object of the present invention to employ a dynamic, adaptive filter to enhance select content (SC) collection, to employ classification systems to organize such SC, and, in some cases, to do so continuously.

It is a further object of the present invention to assist in data processing or manipulation including processes such as coping, data extraction, data archiving, data distribution, data destruction (a document retention process), inter-enterprise and intra-enterprise data distribution, data access request processing, and data distribution denials.

SUMMARY

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. A data input is processed through at least one activated categorical filter to obtain select content, and contextually associated select content and taxonomically associated select content as aggregated select content. The aggregated select content is stored in the corresponding select content data store. A data process from the group of data processes including a copy process, a data extract process, a data archive process, a data distribution process and a data destruction process is associated with the activated categorical filter and the method and system applies the associated data process to a further data input based upon a result of that further data being processed by the activated categorical filter utilizing the aggregated select content data. In effect, the system and process translates the sec-con or SC data and then stores the same in certain locations or secure stores. In this manner, a transformation of the data is achieved. By transposing the sec-con or SC data, higher levels of organization are realized or higher levels of security are implemented.

The enterprise designated filters screen data for enterprise policies such as a level of service policy, customer privacy policy, supplier privacy policy, enterprise human resource privacy policy, financial data handling policy, public company reporting policy, health care regulation policy, technical innovation policy, competitive reporting policy and document or data retention policy.

In another embodiment, the method and system extracts security sensitive content from a data input to obtain extracted security sensitive data for a corresponding security level and remainder data. This extracted security sensitive data is stored in a respective security sensitive extract store. Remainder data is stored in the distributed computer system. Distribution of the data input with respect to said distributed computing system is limited based upon throughput from at least one of the enterprise designated filters. The controlled release of corresponding extracted security sensitive data from the respective extract stores with the associated security clearances for corresponding security levels is permitted by the system. In this manner, security sensitive content is separately stored apart form the select content, which is stored in select content data stores.

Table of ContentsA.1Creating An Information InfrastructureA.2Sampling and Creation of a Simple Info InfrastructureA.3System Tools and Processing Methods for an Information InfrastructureA.4Information Dispersal - BasicA.5Alternative to Information Dispersal AlgorithmA.6Adding Prior Art Information Dispersal Algorithms to Granular Data DispersionA.7Automatic Dispersal of Data to Remote Storage LocationsA.8Dispersal - Breaking Down the Context of Data Streams while Maintaining Access to theContent of its PartsA.9Granular Tagging - BasicA.10Automated Granular Data Level ManipulationA.11Process for Creation of Automated Granular Tagging and LabelingA.12Process for Creation of Automated Tagged Tear LinesA.13Category Tags as Placeholders in DatabasesA.14Controlled Release - BasicA.15Layered Build-up of Data Streams, Layer upon LayerA.16Granular Data Release from Third Party RepositoryA.17Creation of Modular Data Components from Granular Data StreamsA.18“Rolling” Exposure of Granular Data StreamsA.19Controlled Release of Extracts after VerificationA.20Multi Purpose Usage of Filtering and Extraction ProcessA.21Filtering and Copying “Selected Data”A.22Combined Process of Extracting Part of the “Selected Data” and Copying Parts of the“Selected Data”A.23Data Mining Search Results Basic - Distribution as Multi Level Security VersionsA.24Data Mining - A Search Creating Automatically Key WordsA.25Data Mining - Continuous Data Mining with Query RefinementA.26Automatic Continuous Search - with Divergence and ConvergenceA.27Distributed Data MiningA.28Content Inference Engine - BasicA.29An Inference Engine - Searching for the UnknownsA.30Securing Information Sharing with an Inference EngineA.31Automatic Web Data Inference Crawler RobotsA.32Changeable Data Classifications Based on Content Inference ThreatsA.33Search Operations Creating New Continuously Growing Digital Data StreamsA.34Distributed Searches from Different Storage NodesA.35Storage of Separated Data Pieces in One LocationA.36Storage of Granular Data Streams - BasicA.37Configuration of Granular Data Streams Dispersal to Distributed Storage StoresA.38Granular Data Stream TransportA.39Shuttling Data and Granular Data Streams Between Distributed Storage StoresA.40Replication of Granular Data Streams with Minimal Security RiskA.41Anonymizing Classified and Unclassified Storage StoresA.42Controlled Release of Granular Data Streams - Granular Data Manipulation and ControlA.43Sanitization Process with Inference Filtering - BasicA.44Process for Sanitizing Data on the Network for Privacy Security and SurvivabilityA.45Automated Sanitization of Important DataA.46Sanitization of Non-Important DataA.47Release of Granular Data Streams in With Increasing Levels of Access IdentifiersA.48Release of Granular Data Streams After Obtaining ConsiderationA.49Secured Release of Granular Data Streams into Multiple ScreensA.50Ecosystem Based on “Granular Data Control” - BasicA.51Eco System for Information Superiority and Survivability Based on “Granular Data Control”A.52System and Method for Minimizing Data ExposureA.53Creating “Digital Doors” for Access and Controlled Release in a Data StreamA.54General Aspects - BasicA.55Automatic Creation of a User or Object's Identification ProfileA.56Copying Filtered Granular Data and DispersingA.57Combining Extraction and Copying for Filtering of Filtered Granular DataB.1Basic Operational Theory of Secure or Select Data StorageB.2General OperationB.3System ConfigurationsB.4Input or Initial Processing Considerations (Basic Filter Modules)B.5StorageB.6Extraction and Storage EnhancementsB.7Basic ReconstructionB.8Reconstruction TechniquesC.0Testing the Security SystemC.1The Etiology of InformationC.2Metasearch Engine for Knowledge Discovery: Search Convergence and DivergenceC.3Initial Process - Metasearch Engine and Knowledge ExpanderC.4Simple Metasearch EngineC.5Automatic MetataggingC.6Automatic secondary tagging of a “range”C.7MLS, Multi-level Security-Compliant Search and DistributionsC.8Benefits of the Metasearch - Knowledge ExpanderC.9Information Life Cycle EngineC.10Information Life Cycle SearchesC.11Search With Prioritized ClassificationsC.12Simple FiltersC.13Complex FiltersC.14The Editor As An Information ExpanderC.15Document Object Model (DOM) - Protection and ProcessingC.16Recognizing The Document Object Model (DOM)C.17ContentC.18DOM Process EditorC.19Five Informational AttributesC.20Select Content Introns and ExonsC.21Hierarchical Taxonomic Class ExamplesC.22Knowledge Expander (KE) Basic ProgramC.23Secured Metasearch Engine Based on Granular Data ControlD.1Secure Editor, DOM Editor (General) and DOM Editor Applied as Extractor ModulesD.2Applications of the Secure and SC Data SystemsD.3Automatic FeaturesD.4Multiple Security LevelsD.5Sharing Data with Different Security Levels - Data MiningD.6Multilevel Security through Sanitization with Reconstruction of Sanitized ContentD.7Multiple Independent Levels of Security (MILS)D.8MLS Server and Workstation ComponentsD.9Flexibility of the Present MLS ApproachD.10Multiple Extraction Filters and Application OutlineD.11Multiple Security Features for DataD.12Adaptive Data Security - Event Responsive SystemD.13Email, Web-based and Other Types of ApplicationsD.14Portable Computing Device EnvironmentE.1General System Comments

