Acronis International Gmbh

Agency for Science, Technology and Research

Sanjeev Solanki

Serguei M Beloussov

Stanislav Protasov

Mark A Will

Kailash Sivanesan

Eugene Aseev

Tow Chong Chong

Evgeny A Aseev

Xuewu Xu

Sivanesan Kailash Prabhu

Aarthi Kannan

Xiaolu Hou

Sergey Bykov

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.

System and method for selecting a backup plan for newly stored files

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.

System and method for selecting a backup plan based on sampled metadata

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.

Systems and methods for dynamic job performance in secure multiparty computation

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.

Systems and methods for preserving data integrity when integrating secure multiparty computation and blockchain technology

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.

Systems and methods for performing secure computing while maintaining data confidentiality

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.

Systems and methods for synchronizing anonymized linked data across multiple queues for secure multiparty computation

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.

System and method for data classification during file backup

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.

Systems and methods for generating tokens using secure multiparty computation engines

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.

User clustering based on metadata analysis

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.

System and method for scheduling and allocating data storage

The present invention discloses a recording medium comprising an improved doped Stoichiometric Lithium Niobate (SLN) crystal for high-speed holographic data storage. The improved doped SLN has an extremely high optical damage resistance of more than 145 kW/cmfor power density of an incident laser beam along the c axis. The Recording time using the present improved doped SLN is advantageously very short and is about 1 second for a single hologram with a saturated diffraction efficiency of 28.7% at a recording laser beam density of 70 W/cm. Reliable retrieval of a signal-image written at as low as 1 milliseconds has been performed in the Z-cut doped SLN. The present recording medium is an improved doped SLN of a Z-cut SLN crystal doped with Iron (Fe) and Terbium (Tb). The Terbium (Tb) content within the fluxed melts for growing the improved doped SLN ranges from 10 ppm to 140 ppm.

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Singapore, Singapore

Sanjeev Solanki