The disclosure relates to compressing strings by reducing the number of string characters that are stored. For example, a system may generate a first radix tree for a set of strings and a second radix tree for a reverse of each of the set of strings. The system may merge nodes of the first radix tree and/or second radix tree based on a tuning parameter. The system may identify, based on the first radix tree, beginning portions of at least two strings that match and identify, based on the second radix tree, ending portions of at least two strings that match. The system may use the matching beginning portions, the unique portions, and/or the matching ending portions to generate a pattern that matches the two or more strings. The system may store the two or more strings in association with the generated pattern without their matching beginning and/or ending portions.

Apparatus and method are provided for tying together an URL request to a function in the database which returns media data. The invention provides for apparatus that receives a URL request for BLOB data from a web client and receives dynamic values specific to the web client from the web client. The URL is parsed to determine the function in a database and, if they exist, any parameters. A call is made to the function in the database specified in the URL and, if they exist, with parameters specified in the URL and with the dynamic values from the web client. The result can be streamed to the user.

A method for processing personal data, comprising the steps of: (a) For each reference personal data of a reference personal database, calculating in the encrypted domain a similarity rate of the reference personal data with a candidate personal data; said reference personal database being associated with a first partition into a plurality of first sets of reference personal data, and with a second partition into a plurality of second sets of reference personal data, such that each reference personal data of a reference personal database belongs to a single first set and a single second set; (b) For each first set and each second set, calculating an overall similarity rate of said set based on the similarity rates of the reference personal data of said set; (c) Comparing each overall similarity rate of a first and second set with a first and second predetermined threshold, respectively.

This disclosure relates to personalized and dynamic server-side searching techniques for encrypted data. Current so-called ‘zero-knowledge’ privacy systems (i.e., systems where the server has ‘zero-knowledge’ about the client data that it is storing) utilize servers that hold encrypted data without the decryption keys necessary to decrypt, index, and/or re-encrypt the data. As such, the servers are not able to perform any kind of meaningful server-side search process, as it would require access to the underlying decrypted data. Therefore, such prior art ‘zero-knowledge’ privacy systems provide a limited ability for a user to search through a large dataset of encrypted documents to find critical information. Disclosed herein are communications systems that offer the increased security and privacy of client-side encryption to content owners, while still providing for highly relevant server-side search-based results via the use of content correlation, predictive analysis, and augmented semantic tag clouds for the indexing of encrypted data.

Conventionally, biometric template protection has been achieved to improve matching performance with high levels of security by use of deep convolution neural network models. However, such attempts have prominent security limitations mapping information of images to binary codes is stored in an unprotected form. Given this model and access to the stolen protected templates, the adversary can exploit the False Accept Rate (FAR) of the system. Secondly, once the server system is compromised all the users need to be re-enrolled again. Unlike conventional systems and approaches, present disclosure provides systems and methods that implement encrypted deep neural network(s) for biometric template protection for enrollment and verification wherein the encrypted deep neural network(s) is utilized for mapping feature vectors to a randomly generated binary code and a deep neural network model learnt is encrypted thus achieving security and privacy for data protection.

A method includes computing multiple term distances between pairs of multiple first string terms in a first string and multiple second string terms in a second string, generating a cost matrix based on the term distances, and selecting a set of candidate alignments based on the cost matrix. The method further includes generating multiple alignment scores for the set of candidate alignments, and selecting, from the set of candidate alignments, an alignment between the first string and the second string based on the alignment scores. The method further includes outputting a match identifier based on the alignment.

A method and processing module for performing a particular comparison operation using floating point values. The floating point values are received in a scalar format. The received floating point values are promoted to a vector format, wherein the received floating point values are used as a first component of the vector floating point values. A second component of one or more of the vector floating point values is set to a non-zero, finite value. The particular comparison operation is performed using the vector floating point values to determine a vector result having first and second components. A scalar result of the particular comparison operation is determined, wherein the magnitude of the scalar result is given by the magnitude of the first component of the vector result, and wherein if the first component of the vector result is non-zero then the sign of the scalar result equals the sign of the first component of the vector result, and wherein if the first component of the vector result is zero and if the second component of the vector result is non-zero then the sign of the scalar result equals the sign of the second component of the vector result. The scalar result of the particular comparison operation is outputted.

A method and a processing module for performing a particular comparison operation using floating point values received in one or more input formats, The exponent range of the floating point values is extended. One or more of the following is performed: (a) a floating point value of zero is replaced with a non-zero substitute floating point value whose magnitude is small enough to behave like zero if all other values involved in the particular comparison operation are non-zero finite values in their input format; (b) one or more of the floating point values are shifted by a non-zero amount which is small enough to behave like zero if all other values involved in the particular comparison operation are non-zero finite values in their input format, wherein said non-zero amount is too small to be representable using the one or more input formats but is representable using the extended exponent range; and (c) a floating point value of infinity is replaced with a finite substitute floating point value whose magnitude is large enough to behave like infinity if all other values involved in the particular comparison operation are non-zero finite values in their input format, wherein said finite substitute floating point value has a magnitude that is too large to be representable using the one or more input formats but is representable using the extended exponent range.

A method and a processing module for performing a particular comparison operation using floating point values received in one or more input formats, The exponent range of the floating point values is extended. One or more of the following is performed: (a) a floating point value of zero is replaced with a non-zero substitute floating point value whose magnitude is small enough to behave like zero if all other values involved in the particular comparison operation are non-zero finite values in their input format; (b) one or more of the floating point values are shifted by a non-zero amount which is small enough to behave like zero if all other values involved in the particular comparison operation are non-zero finite values in their input format, wherein said non-zero amount is too small to be representable using the one or more input formats but is representable using the extended exponent range; and (c) a floating point value of infinity is replaced with a finite substitute floating point value whose magnitude is large enough to behave like infinity if all other values involved in the particular comparison operation are non-zero finite values in their input format, wherein said finite substitute floating point value has a magnitude that is too large to be representable using the one or more input formats but is representable using the extended exponent range.

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.