METHOD AND SYSTEM FOR GENERATING ANALOGOUS FICTIONAL DATA FROM NON-FICTIONAL DATA

• US 20090307240A1
• Filed: 06/06/2008
• Published: 12/10/2009
• Est. Priority Date: 06/06/2008
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Basile, Ryan M.
• Application Number:
  US12/135,093
• Publication Number:
  US 20090307240A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 16/9014 hash tables
• CPC Class Codes: G06Q 20/383 Anonymous user system

Abstract

A method and system for generating analogous fictional data from non-fictional data, is provided. One implementation involves recording non-fictional data, scoring the non-fictional data in terms of occurrence percentile, obtaining a set of user-configurations that represents a likeness range between non-fictional data and corresponding fictional data, based on the scores and the user-configurations, generating analogous fictional data from the non-fictional data, and comparing hash values for the fictional data with hash values for the non-fictional data to determine matches, and in case of matches, generating analogous fictional data from the non-fictional data based on the scores and incrementally lowered likeness range, whereby entire records of fictional data are generated based on entire records of non-fictional data, wherein the fictional data is consistent with the non-fictional data.

Claims

1. A computer implemented method for generating analogous fictional data from non-fictional data, comprising:

recording non-fictional data;

scoring the non-fictional data in terms of occurrence percentile;

obtaining a set of user-configurations that represents a likeness range between non-fictional data and desired corresponding fictional data;

based on the scores and the user-configurations, generating analogous fictional data from the non-fictional data;

comparing hash values for the fictional data with hash values for the non-fictional data to detect matches between hash values of the fictional data and the non-fictional data; and

upon detecting matches, then generating analogous fictional data from the non-fictional data based on the scores and an incrementally lowered likeness range, whereby entire records of complete analogous fictional data are generated based on entire records of non-fictional data, wherein the fictional data is structurally and relationally consistent and viable with the non-fictional data, such that the generated fictional data is very close to actual non-fictional data, without actually comprising the non-fictional data.