Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for secure distributed backup for personal device and cloud data. An example method disclosed herein includes, in response to a request for a file from a client device, obtaining a map corresponding to the file from a remote device, and requesting, by executing an instruction with a processor, a first file chunk from a first cloud storage location based on the map. The example method further includes requesting, by executing an instruction with the processor, a second file chunk from a second cloud storage location based on the map, and generating, by executing an instruction with the processor, the file by combining the first file chunk and the second file chunk. The example method also includes causing transmission of the file to the client device.

Abstract: According to one example, a system and method are disclosed for malware and grayware remediation. For example, the system is operable to identify applications that have some legitimate behavior but that also exhibit some undesirable behavior. A remediation engine is provided to detect malware behavior in otherwise useful applications, and allow the useful parts of the application to run while blocking the malware behavior. In an example method of “healing,” this may involve modifying the application binary to remove undesirable behavior. In an example method of “personalization,” this may involve inserting control hooks through the operating system to prevent certain subroutines from taking effect.

Abstract: Disclosed examples include receiving an indication that a file is designated as a favorite file from a user device; retrieving the file from a remote location; storing the file as a first favorite file on a favorites storage device; re-retrieving the file from the remote location; and updating the first favorite file with the re-retrieved file.

Abstract: Distributed favorite files are stored in a favorite storage device. An agent dynamically monitors usage statistics for a plurality of files, wherein the plurality of files are located on a plurality of devices, designates a first file of the plurality of files as a favorite file based on usage statistics for the first file, and in response to designating the first file as a favorite file, stores a copy of the first file on a favorites storage device.

Abstract: The present disclosure relates to a system and method for providing a secure context exchange cloud service which enables context information to be shared among devices, and providing a safety application which utilizes the context information being shared. In one example, the context exchange cloud service provides secure exchange of the context information, which in turn enables a safety application to enhance personal safety.

Abstract: In one aspect, a computer-implemented method useful for migrating hundreds of Terabytes to Petabytes of data to a cloud-computing environment with a data transfer appliance includes the step of providing a data transfer appliance. The data transfer appliance includes an operating system, one or more computing processing units (CPU's), a memory, and a data storage system. The computer-implemented method includes the step of implementing data capture from a data storage system to the data transfer appliance. The computer-implemented method includes the step of storing the dedupe form of the data in the data transfer appliance by; providing a capture utility, wherein the capture utility comprises a data traversal engine and a data read engine.

Abstract: Securely distributing a backup file includes identifying a file to be stored, generating a plurality of file chunks from the file, transmitting from a first device, each of the plurality of file chunks to one of a plurality of storage locations, and generating, at the first device, a map that identifies the storage location for each chunk. Retrieving a distributed backup file includes receiving a request to retrieve a file, obtaining a map corresponding to the file, wherein the map identifies a storage location for each of a plurality of file chunks of the file, retrieving the plurality of file chunks from the identified locations, and generating the requested file by combining the plurality of file chunks.

Abstract: Code of a particular application is analyzed against a semantic model of a software development kit of a particular platform. The semantic model associates a plurality of application behaviors with respective application programming interface (API) calls of the particular platform. A set of behaviors of the particular application is identified based on the analysis of the code and a particular one of the set of behaviors is identified as an undesired behavior. The particular application can be automatically modified to remediate the undesired behavior. The particular application can be assigned to one of a plurality of device modes, and access to the particular application on a user device can be based on which of the plurality of device modes is active on the user device.

Abstract: Code of a particular application is analyzed against a semantic model of a software development kit of a particular platform. The semantic model associates a plurality of application behaviors with respective application programming interface (API) calls of the particular platform. A set of behaviors of the particular application is identified based on the analysis of the code and a particular one of the set of behaviors is identified as an undesired behavior. The particular application can be automatically modified to remediate the undesired behavior. The particular application can be assigned to one of a plurality of device modes, and access to the particular application on a user device can be based on which of the plurality of device modes is active on the user device.

Abstract: System calls to a kernel of a mobile computing device are monitored. A particular system call is intercepted relating to input/output (I/O) functionality of the mobile computing device. A data loss prevention (DLP) policy is identified that is applicable to the particular system call. An action is performed on the particular system call based at least in part on the DLP policy.

Abstract: System calls to a kernel of a mobile computing device are monitored. A particular system call is intercepted relating to input/output (I/O) functionality of the mobile computing device. A data loss prevention (DLP) policy is identified that is applicable to the particular system call. An action is performed on the particular system call based at least in part on the DLP policy.

Abstract: To provide a more seamless experience across multiple devices, task streaming systems and methods allow a user to create “task-contexts” and manage metadata of files stored across multiple data storage devices and user preferences associated with capabilities of the multiple devices for operating on the file. Furthermore, the task streaming systems and methods are provided to allow task-contexts to be shared from one device to another device. A task-context specifies one or more files and one or more operations to be performed on the one or more files. By providing a task-context from one device to the other device, a user can accomplish a task with a particular file and seamlessly transition between devices with minimal disruption and effort.

Abstract: Code of a particular application is analyzed against a semantic model of a software development kit of a particular platform. The semantic model associates a plurality of application behaviors with respective application programming interface (API) calls of the particular platform. A set of behaviors of the particular application is identified based on the analysis of the code and a particular one of the set of behaviors is identified as an undesired behavior. The particular application can be automatically modified to remediate the undesired behavior. The particular application can be assigned to one of a plurality of device modes, and access to the particular application on a user device can be based on which of the plurality of device modes is active on the user device.

Abstract: System calls to a kernel of a mobile computing device are monitored. A particular system call is intercepted relating to input/output (I/O) functionality of the mobile computing device. A data loss prevention (DLP) policy is identified that is applicable to the particular system call. An action is performed on the particular system call based at least in part on the DLP policy.

Abstract: A mobile device including: a position locator; a user data engine; and a reputation engine client configured to: receive a location from the position locator; operate the user data engine to provide a user profile, intent, and context data for a user, the context data including dynamic factors about the user, and the profile including relative factors about the user that are relatively static with respect to the context data from the user data engine; and determine a reputation for the location, wherein the reputation is based at least in part on a combination of the user profile, intent, and context.

Abstract: In an example, one or more computing devices operate to provide a context-aware reputation of a place, such as in relation to a human user. Context may include the user's identity and purpose, as well as environmental factors such as time of day, weather, and political drivers. The device may communicate with a server to receive globalized safety intelligence. When the user enters a zone, the device may determine a context-sensitive reputation, such as “Green,” “Yellow,” or “Red.” Depending on the reputation, the device may then take an appropriate action, such as warning the user or providing additional information.

Abstract: Particular embodiments described herein provide for system that can be configured to deliver a notification to a user based on the user's preference for each device that receives the notification. The user's preference is based on how the user interacted with similar notifications in the past and the system can change how it will deliver similar notifications to the user in the future based on how the user interacts with the notification.

Abstract: A safety event is determined as affecting a user based at least in part context data collected at a user device associated with the user. In some aspects, context data is detected from sensors on the client device, the context data describing a present context of the user. A deviation of the present context from a historical context is determined to be beyond a threshold. Determining that the deviation is beyond the threshold can be determined to correspond to a safety event potentially jeopardizing safety of the user. In some aspects, an action can be launched in response to determining the safety event.

Abstract: A system for controlling accesses to network enabled devices includes a network interface over which a hub communicates with network enabled devices, a processor, and a multilayer access control layer. The access control layer includes instructions that, when executed by the processor, cause the processor to detect, at the hub, a request representing an attempt by an application executing on a remote host device to access a network enabled device communicatively coupled to the hub, characterize the request according to a user of the remote host device, the application making the attempt, and the network enabled device, and determine whether to allow or deny the request based upon the characterization and a plurality of rules. The rules may include definitions of access rights, with respect to the network enabled device, for users, applications, commands or queries made by applications, remote host devices, and network domains.

Abstract: In an example, there is disclosed a computing apparatus, including a user notification interface; a context interface; and one or more logic elements forming a contextual privacy engine operable to: receive a notification; receive a context via the context interface; apply the context to the notification via a notification rule; and take an action via the user notification interface based at least in part on the applying. The contextual privacy engine may also be operable to mathematically incorporate user feedback into the notification rule. There is also described a method of providing a contextual privacy engine, and one or more computer-readable storage mediums having stored thereon executable instructions for providing a contextual privacy engine.