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: 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: 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 performing antimalware scanning of data files that is data-centric rather than device-centric, In the example, a plurality of computing devices are connected via a network. An originating device creates or first receives data, and scans the data for malware, After scanning the data, the originating device creates and attaches to the data a metadata record including the results of the malware scan, The originating device may also scan the data for malware contextually-relevant to a second device.

Abstract: A technique includes providing real-time collective and collaborative navigation for one or more users to navigate to a destination. Each computing device associated with a member includes navigation objects that is pooled in a server and updated on each client device of users as one or more users navigate to a destination. A set of navigation objects may be created and distributed to the members within the group for the session, whereby the navigation objects are data structures that may be managed by a server. Based on a contextual trigger, the navigation object may be created or modified and used to provide a context to the navigation session. Navigation objects may continuously analyze user context and user situations to detect anomalies for one or more members in the group.

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: 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: A computer-implemented method for measuring the “greenness” of an IT environment is provided. The method comprises extracting asset data from a plurality of IT assets located on a network and extracting one or more greenness attributes from each of the plurality of IT assets. Key performance indicators are calculated, using the one or more greenness attributes, for each of the plurality of IT assets, and the key performance indicators are presented to a user in a graphical user interface. According to more particular embodiments, the greenness attributes comprise numerical values representing one or more of power requirements, power consumption, and resource utilization.

Abstract: A computer-implemented method for measuring the “greenness” of an IT environment is provided. The method comprises extracting asset data from a plurality of IT assets located on a network and extracting one or more greenness attributes from each of the plurality of IT assets. Key performance indicators are calculated, using the one or more greenness attributes, for each of the plurality of IT assets, and the key performance indicators are presented to a user in a graphical user interface. According to more particular embodiments, the greenness attributes comprise numerical values representing one or more of power requirements, power consumption, and resource utilization.