Acronis International Gmbh

Mark Shmulevich

Serguei M Beloussov

Stanislav Protasov

Alexander G Tormasov

Victor Lysenko

Stanislav S Protassov

Kirill Korotaev

Nikolay Grebennikov

Andrey Redko

Eugene Aseev

Vitaly Pogosyan

Alexei Sergeev

Vladimir Strogov

Yuri Per

Anatoly Stupak

Vladimir Simonov

Alexander Khalyapin

Igor Barinov

Andrei Neporada

Stanislav Roguine

Maxim V Lyadvinsky

Vyacheslav Levchenko

Sergey Ulasen

Michael Chadwick

Vasily Semyonov

Victor Shulga

Kirill Davydov

Ivan Kukhta

Alexey Tyurumov

Stanislav M Protassov

Nikolay Denischenko

Georgiy Pashkov

Yuriy Tsybrovskyy

Mikhail Kochegarov

Alexey Danilov

Denis Dementev

Disclosed are systems and methods for data archiving using machine learning techniques. The system collects statistical information and event data and processes them using machine learning techniques to classify data and/or predict data access demands. The system receives statistical information related to user access of a plurality of files, which can effectively 'train' the system to archive data that is not needed at a certain moment and extract it at other moments. The system identifies, using a machine learning module, a pattern of access in the plurality of files based on the received statistical information. The system modifies, using the identified pattern of access, a threshold value related to file access, and assigns a set of files from the plurality of files an access classification based on the modified threshold value. The system migrates the set of files between hot and cold data areas based on the assigned access classification.

System and method for data classification using machine learning during archiving

Methods and systems for safeguarding against malware such as ransomware are described. In part, the disclosure relates to systems and methods for restoring user data and other data encrypted by malware or otherwise rendered inaccessible thereby. In one embodiment, the disclosure relates to a method of safeguarding user data. The method includes monitoring a plurality of processes executing on a computing device; detecting when a first process of the plurality of processes attempts to modify one or more parameters of a user data file; determining if first process is a trusted process or an untrusted process using one or more heuristics; and if the first process is determined to be an untrusted process, create a backup version of the user data file, wherein the backup version of the user data file is created with regard to an unchanged version the user data file.

Systems and methods of safeguarding user data

A method and system for verified data restoration in storage systems is described. The method may include calculating a hash sum relating to initial data for storing in an archive and storing the data into the archive and storing the calculated initial hash sum into a blockchain network. The method may further include retrieving the data from the archive and calculating a retrieved hash sum of the retrieved data. The method may also include comparing the initial hash sum from the blockchain network with the retrieved hash sum. The method may additionally include restoring the data from the archive if the initial hash sum is equivalent to the retrieved hash sum.

Trusted data restoration and authentication

A system and method is provided for performing a full data backup of user data with data protection. An example method includes generating a snapshot of the electronic data stored in memory, initiating a transfer of the electronic data to a data storage facility; detecting requests to modify a file of the electronic data, saving an initial version of the file based on the snapshot of the electronic data, continuing the transfer of the data to the data storage facility, and transferring the modified file using the initial version of the file based on the snapshot of the electronic data.

System and method for data protection during full data backup

Disclosed are system and method for verification of data transferred among several data storages. An exemplary method includes: calculating first hash-sums of the data during an initial placement in a data storage; transmitting the first hash-sums to at least one blockchain network; detecting a transfer of the data to a new data storage; calculating second hash-sums of the data after a placement of the data in the new data storage; transmitting the second hash-sums to the at least one blockchain network; comparing the first and second hash-sums of the data; and determining data immutability after the transfer of the data from the data storage to the new data storage based at least on results of the comparing.

System and method for verification of data transferred among several data storages

Methods of optimizing transmission of data from a client to a remote data center are disclosed, as well as systems and computer program products related to the same. An exemplary method comprises: receiving data, at a first intervening data center, transmitted from a client and addressed to a terminal data center, wherein the first intervening data center and the terminal data center are selected from a plurality of connected data centers; and transferring the data, from the first intervening data center through one or more additional intervening data centers selected from the plurality of connected data centers, until the data reaches the terminal data center; wherein each data center in the plurality of data centers independently selects a connected data center to transfer the data to based upon the amount of latency and/or bandwidth available at the connected data center.

System and method for dynamic online backup optimization

A system and method is disclosed for using data blocks to optimize file storage in electronic data storage. An example method includes storing data objects in a storage service that correspond to a main block and multiple secondary blocks. The main block is stored in a first bucket and the secondary blocks are stored in one or more second buckets, with the main block including metadata indicating a unique identifier of the second bucket. The method further includes receiving a request to delete the data file to free storage; marking the main block of the data file for deletion that prevents a reading operation or a writing operation of additional data to the data file; deleting the secondary blocks of the data file; and after the secondary blocks are deleted, deleting the marked main block of the data file.

System and method of using data blocks to optimize file storage

Disclosed are systems and methods for protecting a computer system from ransomware and malware by copying and backing up files using a volume filter. A storage stack of the computer system includes a file protector driver and a volume filter driver. The file protector driver monitors for potentially dangerous actions to the system's files. The volume filter driver tracks any requested changes to files on a block level, and makes backup copies of the modified blocks when the blocks change on a block level of the storage device.

Anti-malware protection using volume filters

Disclosed are methods, systems and computer program products for backing up user data from a social network account. An exemplary general method includes the steps of obtaining access to a user account on a social network, by a social network application; determining, by the social network application, one or more restrictions on external requests for data imposed by the social network; generating, by a backup agent in communication with the social network application, an algorithm for requesting data from the user account based upon the one or more restrictions on external requests for data; requesting user data from the user account, by the social network application, using the algorithm; receiving the user data from the user account, by the social network application; transmitting the received user data from the social network application to the backup agent; and archiving the received user data, by the backup agent.

System and method for backing up social network data

Disclosed are methods and systems for performing data backup which implement data binning using log-structured merge (LSM) trees during deduplication. An exemplary method includes: calculating a reduced hash value (RHV) associated with each of a plurality of data blocks; partitioning the plurality of reduced hash values into groups; selecting a representative hash value for each group; determining whether the representative hash value occurs in a first LSM tree, the first LSM tree stored in a volatile memory; and when the representative hash value occurs in the first LSM tree: loading the RHVs in the representative hash value's group into volatile memory; comparing each of the RHVs to one or more hash values in a second LSM tree to identify a matching hash value; and writing a segment identifier (ID) corresponding to the matching hash value in an archive, which references a data block in a segment store.

Systems and methods for data backup using data binning and deduplication

A system and method is provided for geo-location-based user authentication and data access control. An exemplary method includes receiving a request from a mobile device to access a user account and user data where the request includes current geo-location data that indicates a current geographical location of the mobile device/ An authentication server then determines whether the current geo-location data is within at least one geo zone that indicates approved geographical areas where the mobile device can access the user account and data. If the current geographical location of the mobile device is determined to be within the approved geographical areas, the server will provide the mobile device with access to the requested account and data.

System and method for geo-location-based mobile user authentication

A system and method is provided for supporting integrity of distributed data storage with erasure coding. An exemplary method includes receiving a request to create a backup or archive of electronic data using erasure coding, contacting storage nodes and providing an incentive mechanism to incentivize each of the storage nodes to support the distributed data storage, receiving confirmation from at least some of the storage nodes indicating an agreement to store data, generating K data blocks and M parity blocks from the electronic data using erasure coding, and allocating the K data blocks and the M parity blocks to the storage nodes that have agreed to store data.

System and method for supporting integrity of data storage with erasure coding

Disclosed are systems, methods and computer program products for performing data backup using an unmanned aerial vehicle (UAV). An example method includes in response to detecting a data backup request from a user device, determining a geographic location of the user device and dispatching the UAV to the geographic location; controlling the UAV to obtain user data from the user device; and controlling the UAV to navigate to a data center to back up the obtained user data onto a cloud storage.

System and method for data backup using unmanned aerial vehicle (UAV)

Disclosed are systems, methods and computer program products for data deduplication during a backup using at least two LSM trees. An example method includes calculating, for a first data block, a first hash value associated with the first data block and determining a reduced hash value based on the first hash value. The method includes determining whether the first data block contains data duplicative of an existing data block in a prior backup based on whether the reduced hash value occurs in a first log-structured merge (LSM) tree. If so, the method includes comparing the first hash value to one or more hash values in a second LSM tree to identify a matching hash value, and writing a first segment identifier (ID) corresponding to the matching hash value in an archive, the first segment ID referencing the existing data block in a segment store.

System and method for data deduplication using log-structured merge trees

A system and method is provided for service level agreement (SLA) based data storage and verification. According to one exemplary aspect, a method includes storing in electronic memory SLAs associated with a user of client devices and receiving from one of the user a request to verify data previously stored on a remote storage device. Furthermore, the method includes receiving from the client device, for example, a hash value of the data relating to the file and also receiving from a remote storage computer, a hash value created by the remote storage computer for the data that was previously stored on the remote storage computer. Moreover, a processor compares the received hash value and the hash value created by the remote storage computer and transmits a confirmation signal to the client device verifying integrity of the file if the processor determines that the respective hash values match.

Growing community of inventors

Moscow, Russia

Mark Shmulevich