Abstract: Systems, methods, and computer program products are configured to apply k-anonymity to an answer to a query sent to a relational database. A query to the relational database is obtained, the relational database containing a plurality of records. A frequency of occurrence of the attributes in the relational database is determined, an anonymization rule set is created based on the frequency of occurrence of the attributes, the anonymization rule set defining which attributes are to be suppressed in the answer to the query, the anonymization rule set is used to generate the answer to the query, wherein the answer to the query has k-anonymity, and a display or other device is controlled based on the answer to the query.

Abstract: Systems and methods obtain a database query, modify the database query into a modified database query incorporating a privacy mechanism configured for randomizing an attribute value of data in an answer to the modified database query, and output the modified database query. In some embodiments, the modified database query is forwarded to a database.

Abstract: Systems and methods for obscuring data from a data source include devices and processes that may objectively measure the information loss for the data source that is caused by applying a privacy policy, and may apply a policy to the data source based on the measured information loss. The systems and methods may measure the information loss for a large data source by taking a representative sample from the data source and applying the policy to the sample in order to quantify the information loss. The quantified information loss can be iteratively used to change the policy in order to meet utility and/or privacy goals, and the system can subsequently apply the changed policy to the data source.

Abstract: Systems and methods for obtaining a SQL query, translating the SQL into a modified SQL query incorporating a privacy mechanism, and outputting the modified SQL query incorporating the privacy mechanism. In some embodiments, the modified SQL query incorporating the privacy mechanism is forwarded to a SQL database.

Abstract: A method for automated data access management can include creating a project that manages data access to data sources by a plurality of users, wherein each user has user attributes indicating data access policies for the data sources. The method can also include performing project equalization for the project, wherein the project equalization determines a set of user attributes shared by the users. Additionally, the method can include modifying the user attributes of each user for the project, wherein the user attributes of each user are modified to conform to the set of user attributes determined by the project equalization, and detecting a query to retrieve data from the data source. The method can include modifying the query to produce a modified query by applying the modified user attributes associated with the project to the query and retrieving the data from the data source based on the modified query.

Abstract: Systems and methods for obscuring data from a data source include devices and processes that may objectively measure the information loss for a dataset that is caused by applying a privacy policy, and may select and apply a policy to the dataset based on the measured information loss. The systems and methods may measure the information loss for a large dataset by taking a representative sample from the dataset and applying the policy to the sample in order to quantify the information loss. The quantified information loss can be iteratively used to change the policy in order to meet utility and/or privacy goals, and the system can subsequently apply the changed policy to the dataset.

Abstract: Systems, methods, and manufactures for enhancing the determination of differential privacy query results. Implementations may include a database; a result cache; a processor; and a media storing instructions that, when executed by the processor, cause the system to perform operations that include obtaining a query of the database; determining that a specificity of the query exceeds a specificity threshold; determining that the result cache lacks any query results of previous queries that are substantially equivalent to the query; partitioning the database into a plurality of groups; determining a query result by executing the query on the plurality of groups; determining a sensitivity of the query and a nominal measurement of the query; determining a noisy result by adding noise to the query result based on the sensitivity and the nominal measurement; storing the noisy result in the result cache; and providing the noisy result in response to the query.