Abstract: Presented herein are systems and methods for obfuscating text data elements exchanged with deep learning architectures. A computing system may receive, from a computing device, a first input including a first plurality of data elements to be provided at least in part to a deep learning architecture. The computing system may parse the plurality of data elements of the first input to identify at least one first data element corresponding to an identifier type of a plurality of identifier types. The computing system may generate a second data element corresponding to the identifier type to replace the at least one first data element in the first input. The computing system may generate a first output from the first input by replacing the at least one first data element with the second data element. The computing system may transmit the first output for provision to a deep learning architecture.

Abstract: Presented herein are systems and methods for obfuscating text data elements exchanged with deep learning architectures. A computing system may receive, from a computing device, a first input including a first plurality of data elements to be provided at least in part to a deep learning architecture. The computing system may parse the plurality of data elements of the first input to identify at least one first data element corresponding to an identifier type of a plurality of identifier types. The computing system may generate a second data element corresponding to the identifier type to replace the at least one first data element in the first input. The computing system may generate a first output from the first input by replacing the at least one first data element with the second data element. The computing system may transmit the first output for provision to a deep learning architecture.

Abstract: A method may include generating word string vectors for word strings in a document, obtaining encrypted word string vectors by encrypting the word string vectors, generating a search vector for a search query, obtaining an encrypted search vector by encrypting the search vector, calculating encrypted distances between the encrypted word string vectors and the encrypted search vector, obtaining a decrypted distance by decrypting an encrypted distance, and using the decrypted distance, determining a semantic match between the search query and the document.