Method and system for reconstructing original distributions from randomized numeric data
January 19, 2020. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.Patent ReferencesData mining method and system for generating a decision tree classifier for data records based on a minimum description length (MDL) and presorting of records Method and system for generating a decision-tree classifier independent of system memory size Method and system for generating a decision-tree classifier in parallel in a multi-processor system Method for performing targeted marketing over a large computer network Method for refining the initial conditions for clustering with applications to small and large database clustering Method and system for generating a decision-tree classifier in parallel in a multi-processor system Parallel classification for data mining in a shared-memory multiprocessor system Decision tree classifier with integrated building and pruning phases Generating a model for raw variables from a model for cooked variables Method and system for building a decision-tree classifier from privacy-preserving data Patent #: 6546389 InventorsApplicationNo. 487642 filed on 01/19/2000US Classes:707/6, Pattern matching access707/10, Distributed or remote access707/101Manipulating data structure (e.g., compression, compaction, compilation)ExaminersPrimary: Corrielus, Jean B.Attorney, Agent or FirmInternational ClassG06F 017/30AbstractA system and method for mining data while preserving a user's privacy includes perturbing user-related information at the user's computer and sending the perturbed data to a Web site. At the Web site, perturbed data from many users is aggregated, and from the distribution of the perturbed data, the distribution of the original data is reconstructed, although individual records cannot be reconstructed. Based on the reconstructed distribution, a decision tree classification model or a Naive Bayes classification model is developed, with the model then being provided back to the users, who can use the model on their individual data to generate classifications that are then sent back to the Web site such that the Web site can display a page appropriately configured for the user's classification. Or, the classification model need not be provided to users, but the Web site can use the model to, e.g., send search results and a ranking model to a user, with the ranking model being used at the user computer to rank the search results based on the user's individual classification data.Other References
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