Abstract: Disclosed herein are systems and methods for dynamic job performance in secure multiparty computation (SMPC). The method may comprise receiving an SMPC query that indicates a processing job to be performed on a data input. The method may split the data input to generate a plurality of partial data inputs, based on parameters and the query type of the SMPC query. The method may generate a plurality of jobs to perform on the plurality of partial data inputs and determine a combined result of the processing job. The method may adjust the amount of worker processes in a worker pool based on at least one of: required computation, time of day, date, financial costs, power consumption, and available network bandwidth.

Abstract: Disclosed herein are systems and methods for preserving data integrity when integrating secure multiparty computation (SMPC) and blockchain technology. In one exemplary aspect, a method may split, via a data publisher, data into a plurality of data secret shares using an SMPC protocol, wherein each secret share of the plurality of data secret shares is assigned to an SMPC compute node of a plurality of SMPC compute nodes and wherein the plurality of SMPC compute nodes may be members of a blockchain network. In some aspects, the method may determine parameters of a message authentication code (MAC) condition based on the data, may generate secret shares of the MAC condition parameters, and may include a plurality of MAC secret shares with the plurality of data secret shares.

Abstract: Disclosed herein are systems and methods for synchronizing anonymized linked data across multiple queues for SMPC. The systems and methods guarantee that data is kept private from a plurality of nodes, yet can still be synced within a local queue, across the plurality of local queues. In conventional SMPC frameworks, specialised data known as offline data is required to perform key operations, such as multiplication or comparisons. The generation of this offline data is computationally intensive, and thus adds significant overhead to any secure function. The disclosed system and methods aid in the operation of generating and storing offline data before it is required. Furthermore, the disclosed system and methods can help start functions across multi-parties, preventing concurrency issues, and align secure input data to prevent corruption.

Abstract: Disclosed herein are systems and method for performing secure computing while maintaining data confidentiality. In one exemplary aspect, a method receives, via an application, both data and a request to perform a secure operation on the data, wherein the secure operation is to be performed using a secure compute engine on a cloud platform such that the data is not viewable to a provider of the cloud platform. The method applies transformations to the data so that the data is not viewable to the provider. The method transmits the transformed data to the secure compute engine on the cloud platform to perform the secure operation on the transformed data, receives a result of the secure operation from the secure compute engine, and transmits the result to the application.

Abstract: A system and method is provided for selecting a backup plan based on sampled metadata. An exemplary method includes sampling metadata of files on a computer during a predetermined time interval and predicting file usage dynamics for a time interval subsequent to the predetermined time interval, based on a combination of the derivative features and the attributes of the metadata, wherein the predicted file usage dynamics indicate how the files will be utilized on the computer during the time interval. The method includes identifying on the computer at least one newly stored file, selecting a backup plan for the at least one newly stored file based on the file usage dynamics, and backing up the at least one newly stored file to a storage server according to the selected backup plan.

Abstract: A system and method is provided for selecting a backup plan based on sampled metadata. An exemplary method includes sampling metadata of files on a computer during a predetermined time interval, wherein the files are backed up to a storage server in accordance with a first backup plan. The method includes predicting file usage dynamics for a time interval subsequent to the predetermined time interval, based on a combination of the derivative features and the attributes of the metadata, and comparing the predicted file usage dynamics for the time interval with file usage dynamics of the predetermined time interval. In response to determining that a difference between the predicted file usage dynamics and the file usage dynamics of the predetermined time interval exceeds a threshold difference, the method includes selecting a second backup plan for the files, and backing up the files to the storage server accordingly.

Abstract: A system and method is provided for data classification to control file backup operations. An exemplary method includes sampling metadata of one or more files on a computer at a predetermined time interval, determining whether the metadata has changed over time after a predetermined amount of metadata is collected, deriving a model of the change of the metadata over time when the metadata has changed over time, wherein the model of change is a snapshot of dynamically sampled attributes of the one or more files and reflects a state of the computer, performing an analysis of the model of change by determining features of the one or more files comprising at least file usage dynamics of the computer and a user associated with the one or more files based on the model; and selecting a backup plan for the one or more files according to the file usage dynamics.

Abstract: Disclosed herein are systems and methods for preserving data integrity when integrating secure multiparty computation (SMPC) and blockchain technology. In one exemplary aspect, a method may split, via a data publisher, data into a plurality of data secret shares using an SMPC protocol, wherein each secret share of the plurality of data secret shares is assigned to an SMPC compute node of a plurality of SMPC compute nodes and wherein the plurality of SMPC compute nodes may be members of a blockchain network. In some aspects, the method may determine parameters of a message authentication code (MAC) condition based on the data, may generate secret shares of the MAC condition parameters, and may include a plurality of MAC secret shares with the plurality of data secret shares.

Abstract: Disclosed herein are systems and methods for generating tokens using SMPC compute engines. In one aspect, a method may hash, by a node, a data input with a salt value. The method may split, by the node, the hashed data input into a plurality of secret shares, wherein each respective secret share of the plurality of secret shares is assigned to a respective SMPC compute engine of a plurality of SMPC compute engines. The respective SMPC compute engines may be configured to collectively hash the respective secret share with a secret salt value, unknown to the plurality of SMPC compute engines. The respective SMPC compute engine may further receive a plurality of hashed secret shares from remaining SMPC compute engines of the plurality of SMPC compute engines, and generate a token, wherein the token is a combination of the hashed respective secret share and the plurality of hashed secret shares.

Abstract: Disclosed herein are systems and methods for dynamic job performance in secure multiparty computation (SMPC). The method may comprise receiving an SMPC query that indicates a processing job to be performed on a data input. The method may split the data input to generate a plurality of partial data inputs, based on parameters and the query type of the SMPC query. The method may generate a plurality of jobs to perform on the plurality of partial data inputs and determine a combined result of the processing job. The method may adjust the amount of worker processes in a worker pool based on at least one of: required computation, time of day, date, financial costs, power consumption, and available network bandwidth.

Abstract: Disclosed herein are systems and methods for generating tokens using SMPC compute engines. In one aspect, a method may hash, by a node, a data input with a salt value. The method may split, by the node, the hashed data input into a plurality of secret shares, wherein each respective secret share of the plurality of secret shares is assigned to a respective SMPC compute engine of a plurality of SMPC compute engines. The respective SMPC compute engines may be configured to collectively hash the respective secret share with a secret salt value, unknown to the plurality of SMPC compute engines. The respective SMPC compute engine may further receive a plurality of hashed secret shares from remaining SMPC compute engines of the plurality of SMPC compute engines, and generate a token, wherein the token is a combination of the hashed respective secret share and the plurality of hashed secret shares.

Abstract: Disclosed herein are systems and methods for synchronizing anonymized linked data across multiple queues for SMPC. The systems and methods guarantee that data is kept private from a plurality of nodes, yet can still be synced within a local queue, across the plurality of local queues. In conventional SMPC frameworks, specialised data known as offline data is required to perform key operations, such as multiplication or comparisons. The generation of this offline data is computationally intensive, and thus adds significant overhead to any secure function. The disclosed system and methods aid in the operation of generating and storing offline data before it is required. Furthermore, the disclosed system and methods can help start functions across multi-parties, preventing concurrency issues, and align secure input data to prevent corruption.

Abstract: Disclosed herein are systems and method for performing secure computing while maintaining data confidentiality. In one exemplary aspect, a method receives, via an application, both data and a request to perform a secure operation on the data, wherein the secure operation is to be performed using a secure compute engine on a cloud platform such that the data is not viewable to a provider of the cloud platform. The method applies transformations to the data so that the data is not viewable to the provider. The method transmits the transformed data to the secure compute engine on the cloud platform to perform the secure operation on the transformed data, receives a result of the secure operation from the secure compute engine, and transmits the result to the application.

Abstract: A systems and methods of managing user data using clustering patterns based on metadata analysis. The described technique includes receiving file metadata from multiple user devices, where the file metadata is associated with data stored on the plurality of user devices associated with a plurality of users. The technique generates a user metadata fingerprint based on a plurality of user metadata record attributes contained in the file metadata, and determines clustering of the plurality of users based on the generated user metadata fingerprint. The data for the plurality of users may be stored based on the determined clustering.

Abstract: A system and method is disclosed for scheduling and allocating data storage. An example method comprises generating a scheduling problem based at least on states of each of the plurality of storage nodes, a received plurality of storage tasks and received constraints, wherein the scheduling problem is a constraint satisfaction problem, selecting one or more approaches to solving the scheduling problem based on metadata associated with the storage tasks and constraints, solving the scheduling problem to generate a scheduling solution based on the one or more approaches, determining whether the given constraints are satisfied by the scheduling solution, executing, by the processor, the scheduling solution by assigning storage of data to each of the plurality of storage nodes when the constraints are satisfied by the scheduling solution and determining another scheduling solution based on the one or more approaches when the constraints are not satisfied by the scheduling solution.

Abstract: A system and method is disclosed for scheduling and allocating data storage. An example method comprises generating a scheduling problem based at least on states of each of the plurality of storage nodes, a received plurality of storage tasks and received constraints, wherein the scheduling problem is a constraint satisfaction problem, selecting one or more approaches to solving the scheduling problem based on metadata associated with the storage tasks and constraints, solving the scheduling problem to generate a scheduling solution based on the one or more approaches, determining whether the given constraints are satisfied by the scheduling solution, executing, by the processor, the scheduling solution by assigning storage of data to each of the plurality of storage nodes when the constraints are satisfied by the scheduling solution and determining another scheduling solution based on the one or more approaches when the constraints are not satisfied by the scheduling solution.

Abstract: A system and method is provided for data classification to control file backup operations. An exemplary method includes sampling metadata of one or more files on a computer at a predetermined time interval, determining whether the metadata has changed over time after a predetermined amount of metadata is collected, deriving a model of the change of the metadata over time when the metadata has changed over time, wherein the model of change is a snapshot of dynamically sampled attributes of the one or more files and reflects a state of the computer, performing an analysis of the model of change by determining features of the one or more files comprising at least file usage dynamics of the computer and a user associated with the one or more files based on the model; and selecting a backup plan for the one or more files according to the file usage dynamics.

Abstract: A systems and methods of managing user data using clustering patterns based on metadata analysis. The described technique includes receiving file metadata from multiple user devices, where the file metadata is associated with data stored on the plurality of user devices associated with a plurality of users. The technique generates a user metadata fingerprint based on a plurality of user metadata record attributes contained in the file metadata, and determines clustering of the plurality of users based on the generated user metadata fingerprint. The data for the plurality of users may be stored based on the determined clustering.

Abstract: A system and method is provided for data classification to control file backup operations. An exemplary method includes analyzing electronic data to identify properties and parameters of the electronic data and comparing the properties and parameters with predetermined rules that indicate storage levels based on properties and parameters. Furthermore, the method includes identifying one of the storage levels based on the comparison of the properties and parameters of the electronic data with the plurality of rules, and performing a data backup of the electronic data based on the identified storage level.

Abstract: Methods and an apparatus for color holographic display are provided. The method for color holographic display includes modifying a plurality of color component light beams by passing light from each of a plurality of color component light sources through a corresponding one of a plurality of color sub-hologram masks to generate a corresponding plurality of modified color component light beams. The plurality of modified color component light beams are color multiplexed to generate a multiplexed masked color light beam. The multiplexed masked color light beam is shined onto modulated computer generated holographic information corresponding to the object in order to reconstruct the object by diffracting each portion of the multiplexed masked color light beam to a location and at an intensity determined in response to a corresponding portion of the computer generated holographic information to create the object as a color holographic display.