Abstract: A data privacy system automatically determines quasi-identifiers in a database containing individuals' records. The data privacy system applies a machine learning model to the database, the model configured to classify each record in the database and output a measure of its confidence in its classification. The data privacy system determines, based on the measure of confidence, how important each attribute is to the model's classification. The data privacy system iteratively applies a machine learning model on a modified database that includes the highest ranked attributes to identify the quasi-identifiers in the records in the database. The data privacy system can use identified quasi-identifiers to determine if the database is susceptible to a membership inference attack, and in response to such a determination, can perform one or more data privacy operations on the database to reduce this risk.

Abstract: A data privacy system automatically determines quasi-identifiers in a database containing individuals' records. The data privacy system applies a machine learning model to the database, the model configured to classify each record in the database and output a measure of its confidence in its classification. The data privacy system determines, based on the measure of confidence, how important each attribute is to the model's classification. The data privacy system iteratively applies a machine learning model on a modified database that includes the highest ranked attributes to identify the quasi-identifiers in the records in the database. The data privacy system can use identified quasi-identifiers to determine if the database is susceptible to a membership inference attack, and in response to such a determination, can perform one or more data privacy operations on the database to reduce this risk.

Abstract: Unicode data can be protected in a distributed tokenization environment. Data to be tokenized can be accessed or received by a security server, which instantiates a number of tokenization pipelines for parallel tokenization of the data. Unicode token tables are accessed by the security server, and each tokenization pipeline uses the accessed token tables to tokenization a portion of the data. Each tokenization pipeline performs a set of encoding or tokenization operations in parallel and based at least in part on a value received from another tokenization pipeline. The outputs of the tokenization pipelines are combined, producing tokenized data, which can be provided to a remote computing system for storage or processing.

Abstract: Unicode data can be protected in a distributed tokenization environment. Data to be tokenized can be accessed or received by a security server, which instantiates a number of tokenization pipelines for parallel tokenization of the data. Unicode token tables are accessed by the security server, and each tokenization pipeline uses the accessed token tables to tokenization a portion of the data. Each tokenization pipeline performs a set of encoding or tokenization operations in parallel and based at least in part on a value received from another tokenization pipeline. The outputs of the tokenization pipelines are combined, producing tokenized data, which can be provided to a remote computing system for storage or processing.

Abstract: A gateway device includes a network interface connected to data sources, and computer instructions, that when executed cause a processor to access data portions from the data sources. The processor accesses classification rules, which are configured to classify a data portion of the plurality of data portions as sensitive data in response to the data portion satisfying the rule. Each rule is associated with a significance factor representative of an accuracy of the classification rule. The processor applies each of the set of classification rules to a data portion to obtain an output of whether the data is sensitive data. The output are weighed by significance factors to produce a set of weighted outputs. The processor determines if the data portion is sensitive data by aggregating the set of weighted outputs, and presents the determination in a user interface. Security operations may also be performed on the data portion.

Abstract: Data in a database can be protected, for instance by tokenizing the entries of the database using one or more token tables. To enable searching data within the database without first detokenizing the tokenized database entries, bigrams of each data entry can also be tokenized and stored in association with the tokenized data entry. When a query term is received, the query term can be parsed into bigrams, and each bigram can be tokenized. The tokenized query bigrams can be used to query the database, and tokenized database entries corresponding to tokenized bigrams that match the tokenized query bigrams can be identified and returned as search results.

Abstract: Data in a database can be protected, for instance by tokenizing the entries of the database using one or more token tables. To enable searching data within the database without first detokenizing the tokenized database entries, bigrams of each data entry can also be tokenized and stored in association with the tokenized data entry. When a query term is received, the query term can be parsed into bigrams, and each bigram can be tokenized. The tokenized query bigrams can be used to query the database, and tokenized database entries corresponding to tokenized bigrams that match the tokenized query bigrams can be identified and returned as search results.

Abstract: Data can be protected in a centralized tokenization environment. A security value is received by a central server from a client device. The central server accesses a token table corresponding to the client device and generates a reshuffled static token table from the accessed token table based on the received security value. When the client device subsequently provides data to be protected to the central server, the central server tokenizes the provided data using the reshuffled static token table and stores the tokenized data in a multi-tenant database. By reshuffling token tables using security values unique to client devices, the central server can protect and store data for each of multiple tenants such that if the data of one tenant is compromised, the data of each other tenant is not compromised.

Abstract: Unicode data can be protected in a distributed tokenization environment. Data to be tokenized can be accessed or received by a security server, which instantiates a number of tokenization pipelines for parallel tokenization of the data. Unicode token tables are accessed by the security server, and each tokenization pipeline uses the accessed token tables to tokenization a portion of the data. Each tokenization pipeline performs a set of encoding or tokenization operations in parallel and based at least in part on a value received from another tokenization pipeline. The outputs of the tokenization pipelines are combined, producing tokenized data, which can be provided to a remote computing system for storage or processing.

Abstract: Unicode data can be protected in a distributed tokenization environment. Data to be tokenized can be accessed or received by a security server, which instantiates a number of tokenization pipelines for parallel tokenization of the data. Unicode token tables are accessed by the security server, and each tokenization pipeline uses the accessed token tables to tokenization a portion of the data. Each tokenization pipeline performs a set of encoding or tokenization operations in parallel and based at least in part on a value received from another tokenization pipeline. The outputs of the tokenization pipelines are combined, producing tokenized data, which can be provided to a remote computing system for storage or processing.

Abstract: A gateway device includes a network interface connected to data sources, and computer instructions, that when executed cause a processor to access data portions from the data sources. The processor accesses classification rules, which are configured to classify a data portion of the plurality of data portions as sensitive data in response to the data portion satisfying the rule. Each rule is associated with a significance factor representative of an accuracy of the classification rule. The processor applies each of the set of classification rules to a data portion to obtain an output of whether the data is sensitive data. The output are weighed by significance factors to produce a set of weighted outputs. The processor determines if the data portion is sensitive data by aggregating the set of weighted outputs, and presents the determination in a user interface. Security operations may also be performed on the data portion.

Abstract: A gateway device includes a network interface connected to data sources, and computer instructions, that when executed cause a processor to access data portions from the data sources. The processor accesses classification rules, which are configured to classify a data portion of the plurality of data portions as sensitive data in response to the data portion satisfying the rule. Each rule is associated with a significance factor representative of an accuracy of the classification rule. The processor applies each of the set of classification rules to a data portion to obtain an output of whether the data is sensitive data. The output are weighed by significance factors to produce a set of weighted outputs. The processor determines if the data portion is sensitive data by aggregating the set of weighted outputs, and presents the determination in a user interface. Security operations may also be performed on the data portion.

Abstract: Data can be protected in a centralized tokenization environment. A security value is received by a central server from a client device. The central server accesses a token table corresponding to the client device and generates a reshuffled static token table from the accessed token table based on the received security value. When the client device subsequently provides data to be protected to the central server, the central server tokenizes the provided data using the reshuffled static token table and stores the tokenized data in a multi-tenant database. By reshuffling token tables using security values unique to client devices, the central server can protect and store data for each of multiple tenants such that if the data of one tenant is compromised, the data of each other tenant is not compromised.

Abstract: Data can be protected in a centralized tokenization environment. A security value is received by a central server from a client device. The central server accesses a token table corresponding to the client device and generates a reshuffled static token table from the accessed token table based on the received security value. When the client device subsequently provides data to be protected to the central server, the central server tokenizes the provided data using the reshuffled static token table and stores the tokenized data in a multi-tenant database. By reshuffling token tables using security values unique to client devices, the central server can protect and store data for each of multiple tenants such that if the data of one tenant is compromised, the data of each other tenant is not compromised.

Abstract: Data in a database can be protected, for instance by tokenizing the entries of the database using one or more token tables. To enable searching data within the database without first detokenizing the tokenized database entries, bigrams of each data entry can also be tokenized and stored in association with the tokenized data entry. When a query term is received, the query term can be parsed into bigrams, and each bigram can be tokenized. The tokenized query bigrams can be used to query the database, and tokenized database entries corresponding to tokenized bigrams that match the tokenized query bigrams can be identified and returned as search results.

Abstract: Unicode data can be protected in a distributed tokenization environment. Data to be tokenized can be accessed or received by a security server, which instantiates a number of tokenization pipelines for parallel tokenization of the data. Unicode token tables are accessed by the security server, and each tokenization pipeline uses the accessed token tables to tokenization a portion of the data. Each tokenization pipeline performs a set of encoding or tokenization operations in parallel and based at least in part on a value received from another tokenization pipeline. The outputs of the tokenization pipelines are combined, producing tokenized data, which can be provided to a remote computing system for storage or processing.

Abstract: Data can be protected in a centralized tokenization environment. A security value is received by a central server from a client device. The central server accesses a token table corresponding to the client device and generates a reshuffled static token table from the accessed token table based on the received security value. When the client device subsequently provides data to be protected to the central server, the central server tokenizes the provided data using the reshuffled static token table and stores the tokenized data in a multi-tenant database. By reshuffling token tables using security values unique to client devices, the central server can protect and store data for each of multiple tenants such that if the data of one tenant is compromised, the data of each other tenant is not compromised.

Abstract: A gateway device includes a network interface connected to data sources, and computer instructions, that when executed cause a processor to access data portions from the data sources. The processor accesses classification rules, which are configured to classify a data portion of the plurality of data portions as sensitive data in response to the data portion satisfying the rule. Each rule is associated with a significance factor representative of an accuracy of the classification rule. The processor applies each of the set of classification rules to a data portion to obtain an output of whether the data is sensitive data. The output are weighed by significance factors to produce a set of weighted outputs. The processor determines if the data portion is sensitive data by aggregating the set of weighted outputs, and presents the determination in a user interface. Security operations may also be performed on the data portion.

Abstract: Data can be protected in a centralized tokenization environment. A security value is received by a central server from a client device. The central server accesses a token table corresponding to the client device and generates a reshuffled static token table from the accessed token table based on the received security value. When the client device subsequently provides data to be protected to the central server, the central server tokenizes the provided data using the reshuffled static token table and stores the tokenized data in a multi-tenant database. By reshuffling token tables using security values unique to client devices, the central server can protect and store data for each of multiple tenants such that if the data of one tenant is compromised, the data of each other tenant is not compromised.

Abstract: A gateway device includes a network interface connected to data sources, and computer instructions, that when executed cause a processor to access data portions from the data sources. The processor accesses classification rules, which are configured to classify a data portion of the plurality of data portions as sensitive data in response to the data portion satisfying the rule. Each rule is associated with a significance factor representative of an accuracy of the classification rule. The processor applies each of the set of classification rules to a data portion to obtain an output of whether the data is sensitive data. The output are weighed by significance factors to produce a set of weighted outputs. The processor determines if the data portion is sensitive data by aggregating the set of weighted outputs, and presents the determination in a user interface. Security operations may also be performed on the data portion.