Accenture Global Services Gmbh

Katharina Probst

Rayid Ghani

Andrew E Fano

Yan Liu

Marko Krema

Mark Grechanik

Yaron Rachlin

Chad Michael Cumby

Kevin Michael Conroy

Privacy is defined in the context of a guessing game based on the so-called guessing inequality. The privacy of a sanitized record, i.e., guessing anonymity, is defined by the number of guesses an attacker needs to correctly guess an original record used to generate a sanitized record. Using this definition, optimization problems are formulated that optimize a second anonymization parameter (privacy or data distortion) given constraints on a first anonymization parameter (data distortion or privacy, respectively). Optimization is performed across a spectrum of possible values for at least one noise parameter within a noise model. Noise is then generated based on the noise parameter value(s) and applied to the data, which may comprise real and/or categorical data. Prior to anonymization, the data may have identifiers suppressed, whereas outlier data values in the noise perturbed data may be likewise modified to further ensure privacy.

Data anonymization based on guessing anonymity

A code search tool greatly reduces time, cost, and other resource expenditures associated with implementing a new application. The tool is a search, navigation and visualization tool that accepts high-level processing concepts as inputs to identify, rank, and return the code of relevant existing applications. A software developer may use the relevant applications to rapidly build prototypes, identify requirements, and develop new applications. The tool provides an efficient way to improve the reuse of application logic to realize the high-level processing concepts, and more efficiently deliver proof of concept.

Application search tool for rapid prototyping and development of new applications

General entity retrieval and ranking is described. A first set of documents is retrieved from one or more document repositories based on a query formed according to the topic. The first set of documents is characterized based on its first set of metadata values. One or more candidate entities are identified based on the first set of documents and the original query is thereafter augmented according to a candidate entity. The second set of documents resulting from the augmented query is then characterized in a similar manner. For each candidate entity, the first and second document set characterizations are compared to determine their degree of similarity. Increasingly similar document set characterizations indicates that the candidate entity is increasingly relevant to the original query. Repeating this process for each of the one or more candidate entities can give rise to rankings according to the respective degrees of similarity.

Entity assessment and ranking

One or more classification algorithms are applied to at least one natural language document in order to extract both attributes and values of a given product. Supervised classification algorithms, semi-supervised classification algorithms, unsupervised classification algorithms or combinations of such classification algorithms may be employed for this purpose. The at least one natural language document may be obtained via a public communication network. Two or more attributes (or two or more values) thus identified may be merged to form one or more attribute phrases or value phrases. Once attributes and values have been extracted in this manner, association or linking operations may be performed to establish attribute-value pairs that are descriptive of the product. In a presently preferred embodiment, an (unsupervised) algorithm is used to generate seed attributes and values which can then support a supervised or semi-supervised classification algorithm.

Extraction of attributes and values from natural language documents

A classification algorithm is separated into one or more input-invariant parts and one or more input-dependent classification parts. Classifiable electronic data is obtained via a communication network. Using the classification algorithm, classifications of a plurality of data elements in the classifiable data are identified, where the at least one classification part incorporates user input concerning classification of at least one data element of the plurality of data elements.

Automated classification algorithm comprising at least one input-invariant part

A classification algorithm is separated into one or more input-invariant parts and one or more input-dependent classification parts. The input-invariant parts of the classification algorithm capture the underlying and unchanging relationships between the plurality of data elements being operated upon by the classification algorithm, whereas the one or more classification parts embody the probabilistic labeling of the data elements according to the various classifications. For any given iteration, a user's input is used to modify at least one classification part of the algorithm. Recalculated classification parts (i.e., updated classification results) are determined based on computationally simple combinations of the one or more modified classification parts and the one or more input-invariant parts. Preferably, a graphical user interface is used to solicit user input. In this manner, wait times between user feedback iterations can be dramatically reduced, thereby making application of active learning to classification tasks a practical reality.

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Dyer, IN, United States of America

Katharina Probst