Abstract: Methods and systems for identifying personally identifiable information (PII) are disclosed. In some aspects, frequency maps of fields storing known PII information are generated. The frequency maps may count occurrences of unique bigrams in the PII fields. A field of interest may then be analyzed to generate a second frequency map. Correlations between the first frequency maps and the second frequency map may be generated. If one of the correlations meets certain criterion, the disclosed embodiments may determine that the field of interest does or does not include PII. Access control for the field of interest may then be based on whether the field includes PII. In some aspects, a storage location of data included in the field of interest may be based on whether the field includes PII.

Abstract: Methods, computer readable media, devices, and systems provide for distributed machine learning. In one aspect, a method of training a model is disclosed. The method includes receiving, by a client device, from one or more servers, an intermediate model, training, by the client device, the intermediate model based on private data, and transmitting, by the client device, to the one or more servers, the trained intermediate model.

Abstract: Methods and systems for identifying personally identifiable information (PII) are disclosed. In some aspects, frequency maps of fields storing known PII information are generated. The frequency maps may count occurrences of unique bigrams in the PII fields. A field of interest may then be analyzed to generate a second frequency map. Correlations between the first frequency maps and the second frequency map may be generated. If one of the correlations meets certain criterion, the disclosed embodiments may determine that the field of interest does or does not include PII. Access control for the field of interest may then be based on whether the field includes PII. In some aspects, a storage location of data included in the field of interest may be based on whether the field includes PII.

Abstract: Methods, computer readable media, devices, and systems provide for distributed machine learning. In one aspect, a method of training a model is disclosed. The method includes receiving, by a client device, from one or more servers, an intermediate model, training, by the client device, the intermediate model based on private data, and transmitting, by the client device, to the one or more servers, the trained intermediate model.

Abstract: Methods, computer readable media, devices, and systems provide for distributed machine learning. In one aspect, a method of training a model is disclosed. The method includes receiving, by a client device, from one or more servers, an intermediate model, training, by the client device, the intermediate model based on private data, and transmitting, by the client device, to the one or more servers, the trained intermediate model.

Abstract: Methods and systems for identifying personally identifiable information (PII) are disclosed. In some aspects, frequency maps of fields storing known PII information are generated. The frequency maps may count occurrences of unique bigrams in the PII fields. A field of interest may then be analyzed to generate a second frequency map. Correlations between the first frequency maps and the second frequency map may be generated. If one of the correlations meets certain criterion, the disclosed embodiments may determine that the field of interest does or does not include PII. Access control for the field of interest may then be based on whether the field includes PII. In some aspects, a storage location of data included in the field of interest may be based on whether the field includes PII.

Abstract: Methods and systems for identifying personally identifiable information (PII) are disclosed. In some aspects, frequency maps of fields storing known PII information are generated. The frequency maps may count occurrences of unique bigrams in the PII fields. A field of interest may then be analyzed to generate a second frequency map. Correlations between the first frequency maps and the second frequency map may be generated. If one of the correlations meets certain criterion, the disclosed embodiments may determine that the field of interest does or does not include PII. Access control for the field of interest may then be based on whether the field includes PII. In some aspects, a storage location of data included in the field of interest may be based on whether the field includes PII.

Abstract: In one embodiment, a method includes determining a first set of similarity metrics for a first storage environment, the first set of similarity metrics being based on predetermined parameters related to the first storage environment, at least one of the predetermined parameters being relating to a redundancy of connections and devices in the first storage environment. In addition, the method includes obtaining a second set of similarity metrics for a second storage environment. Also, the method includes determining that the second storage environment is similar to the first storage environment by calculating a similarity measurement score between the sets of similarity metrics for the first and second storage environments. Moreover, the method includes re-aligning the first storage environment according to one or more identified aspects of the second storage environment that improve performance of the first storage environment.

Abstract: In one embodiment, a method includes determining a first set of similarity metrics for a first storage environment, the first set of similarity metrics being based on predetermined parameters related to the first storage environment, at least one of the predetermined parameters being relating to a redundancy of connections and devices in the first storage environment. In addition, the method includes obtaining a second set of similarity metrics for a second storage environment. Also, the method includes determining that the second storage environment is similar to the first storage environment by calculating a similarity measurement score between the sets of similarity metrics for the first and second storage environments. Moreover, the method includes re-aligning the first storage environment according to one or more identified aspects of the second storage environment that improve performance of the first storage environment.

Abstract: In one embodiment, a method includes determining a first set of similarity metrics for a first storage environment, obtaining one or more second sets of similarity metrics from second storage environments that correspond with the first storage environment, calculating a similarity measurement score between the first storage environment and each of the second storage environments, and determining a set of similar storage environments from amongst the second storage environments based on the similarity measurement scores. Other methods, systems, and computer program products are presented according to more embodiments.

Abstract: In one embodiment, a method includes determining a first set of similarity metrics for a first storage environment, obtaining one or more second sets of similarity metrics from second storage environments that correspond with the first storage environment, calculating a similarity measurement score between the first storage environment and each of the second storage environments, and determining a set of similar storage environments from amongst the second storage environments based on the similarity measurement scores. Other methods, systems, and computer program products are presented according to more embodiments.