Abstract: A system and method for objective quantification and mitigation of privacy-risk of a dataset is disclosed.

Abstract: The system and method provide for automatically extracting latent structures and relationships in datasets of any size, to enable effective management of that data. The system and method produce hierarchies of fields with relationships of a particular type and strength. The hierarchies of fields with relationships of a particular type and strength can be used to optimize the data storage profile of a company by separating large datasets into multiple smaller datasets, reducing operational costs, perform enhanced analysis of the data through a greater understanding of its internal logical structure, reduce privacy risk within the dataset by removing risky fields and substituting them in analytical processing with less risky fields that carry a similar amount of statistical information, to identify inference risks in the data to enable more effective data protection decision-making, and can be used to separate out wide datasets into multiple narrower/smaller datasets, thus aiding more efficient analytics.

Abstract: A system and method for estimating privacy risk measures are described. The system and method include an input-output (IO) interface for receiving at least one input dataset, on at least one of which a measurement of the risk is to be performed, and a configuration file governing the specifications of the dataset(s), risk scoring and risk mitigation settings, a processor for determining the size of at least one dataset, based on the size of at least one dataset being larger than a threshold, a sampling engine for estimating the privacy risk by computing a statistically representative sample size and subsampling at least one dataset to a sample size, and a scoring engine to perform column level measurements on the subsampled dataset and row level measurements on at least one dataset. Further included, based on the column level and row level measurements, the IO interface outputs a privacy risk.

Abstract: A system and method for objective quantification and mitigation of privacy-risk of a dataset is disclosed.

Abstract: A system and method for objective quantification and mitigation of privacy-risk of a dataset is disclosed.

Abstract: A system and method for guiding privacy-enhancing transformations are described. The system and method include a recommendation engine configured to identify sets of transformations to mitigate a privacy risk below a user specified threshold specified in-terms of privacy-risk score for a given input dataset while keeping the utility of the dataset above the user-specified utility threshold specified in-terms of utility score. A simulation engine configured to simulate the identified set of transformations from the recommendation engine on the dataset to determine the optimal application of the plurality of transformations for maximizing the utility of the dataset, and output device to provide the optimized dataset with the privacy risk score and utility score.

Abstract: A system and method for objective quantification and mitigation of privacy-risk of a dataset is disclosed.

Abstract: A system and method of harnessing knowledge and expertise to improve machine learning is disclosed. The system and method include capturing the data to input, preparing the captured data, enhancing the prepared data, modeling and learning the process associated with the enhanced data, reviewing the result of the learning and modeling to produce an output, visualizing the reviewed output, and input and recommendations from recommendations engine that make recommendations of techniques and configurations to use.

Abstract: A system and method for providing the ability to use k-anonymous groups to analyze disparate data sets via the use of either individual to segment or segment to segment matching using modelling or querying approaches are disclosed. The system and method include creating a common representation across all consumer and producer data sets, training one or more models or defining one or more queries optimized to recognize the behavior of the specified subjects within the generated common representation, evaluating those models or executing those queries on the common representation of the producer data set(s) to identify likely candidates for the specified input data subjects in each producer data set, the performing of actions over the identified subjects for each producer data set, and output the analytics result.

Abstract: A system and method for objective quantification and mitigation of privacy-risk of a dataset is disclosed.

Abstract: A system and method for generating one or more synthetic datasets with privacy and utility controls are disclosed.

Abstract: A system has a primary server storing search data sets (“staks”) and a linked social network server.

Abstract: A searching system may include a primary server with interfaces for communication with databases or with underlying search engines. The primary server may be adapted to receive a search query from a client under instructions from a user; and retrieve search results and transmit the results as a response to the client. The primary server may be adapted to store search data sets of relevant results for queries by users who are member of the set. The primary server may be adapted to select a search data set of which the user for the query is a member, and to use said search data set in providing the response to the query. The primary server may be adapted to download data concerning the selected search data set to the user. The primary server may be adapted to automatically update the search data set according to the results selected by the user.

Abstract: A searching system may include a primary server with interfaces for communication with databases or with underlying search engines. The primary server may be adapted to receive a search query from a client under instructions from a user; and retrieve search results and transmit the results as a response to the client. The primary server may be adapted to store search data sets of relevant results for queries by users who are member of the set. The primary server may be adapted to select a search data set of which the user for the query is a member, and to use said search data set in providing the response to the query. The primary server may be adapted to download data concerning the selected search data set to the user. The primary server may be adapted to automatically update the search data set according to the results selected by the user.

Abstract: A system has a primary server storing search data sets (“staks”) and a linked social network server. Interaction with the server is via a client and a Web site. There is a software agent code executing on the client. The software agent component provides full integration with underlying search engines so that users can continue to search in the normal way, using their favourite search engines, while benefiting from management of search staks, automatic stak selection, and result promotion. The system allows users to manage their staks and providing a range of social networking style services to help users make contact with other like-minded searchers. In addition, it allows users to search for relevant staks. The software agent component provides browser-based access to allow users to manage and share their searchers direct from their browser, as well as providing the benefits of search promotions as they search normally.