Snap Inc.

Dachuan Huang

Subhash Sankuratripati

Vasyl Pihur

Leah Fortier

Antonio Marcedone

Ruogu Zeng

Frederick Liu

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.

Identifying personally identifiable information within an unstructured data store

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.

Distributed machine learning for improved privacy

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Santa Monica, CA, United States of America

Dachuan Huang