Abstract: Operating upon encrypted data with a particular data scope. A base encryption key is established and associated with the particular data scope, and then stored in a base encryption key store. That base encryption key store might be managed by an application or service that stores base encryption keys for multiple data scopes. A proxy encryption key acts as a kind of proxy for the base encryption key. The proxy encryption key may be used for frequent operations on encrypted data within the particular data scope. Thus, the principles described herein act as a frequency amplifier that allows key-based operations upon the particular data scope to be performed at much higher frequencies than otherwise would be possible by operating directly using the base encryption key.

Abstract: Data may be encrypted using a derived block encryption key for each of at least one append blocks of data. A data operation associated with manipulating particular data associated with a user may be received. The particular data may comprise at least one append block of data. In response to the received data operation, for each append block of data of the at least one append block of data, parameters associated with deriving a block encryption key for a given append block of data of the at least one append block of data may be accessed. The parameters may comprise at least a data encryption key associated with the user and a nonce. A block encryption key may be derived for the given append block of data utilizing the parameters. The given append block of data may be encrypted utilizing the block encryption key.

Abstract: Operating upon encrypted data with a particular data scope. A base encryption key is established and associated with the particular data scope, and then stored in a base encryption key store. That base encryption key store might be managed by an application or service that stores base encryption keys for multiple data scopes. A proxy encryption key acts as a kind of proxy for the base encryption key. The proxy encryption key may be used for frequent operations on encrypted data within the particular data scope.

Abstract: Operating upon encrypted data with a particular data scope. A base encryption key is established and associated with the particular data scope, and then stored in a base encryption key store. That base encryption key store might be managed by an application or service that stores base encryption keys for multiple data scopes. A proxy encryption key acts as a kind of proxy for the base encryption key. The proxy encryption key may be used for frequent operations on encrypted data within the particular data scope. Thus, the principles described herein act as a frequency amplifier that allows key-based operations upon the particular data scope to be performed at much higher frequencies than otherwise would be possible by operating directly using the base encryption key.

Abstract: Data may be encrypted using a derived block encryption key for each of at least one append blocks of data. A data operation associated with manipulating particular data associated with a user may be received. The particular data may comprise at least one append block of data. In response to the received data operation, for each append block of data of the at least one append block of data, parameters associated with deriving a block encryption key for a given append block of data of the at least one append block of data may be accessed. The parameters may comprise at least a data encryption key associated with the user and a nonce. A block encryption key may be derived for the given append block of data utilizing the parameters. The given append block of data may be encrypted utilizing the block encryption key.

Abstract: Operating upon encrypted data with a particular data scope. A base encryption key is established and associated with the particular data scope, and then stored in a base encryption key store. That base encryption key store might be managed by an application or service that stores base encryption keys for multiple data scopes. A proxy encryption key acts as a kind of proxy for the base encryption key. The proxy encryption key may be used for frequent operations on encrypted data within the particular data scope. Thus, the principles described herein act as a frequency amplifier that allows key-based operations upon the particular data scope to be performed at much higher frequencies than otherwise would be possible by operating directly using the base encryption key.

Abstract: Methods, systems, and computer program products are provided for using a claims model to control the execution of an execution unit. The execution unit is executed. An identity principal is identified under which the execution unit is executed. At least one claim is identified that is required for the execution unit. Each identified claim has a claim name and a claim value. One or more claims associated with the identified identity principal is/are requested from a claims provider. The claim(s) is/are received from the claims provider. The received claim(s) is/are compared with the identified claim(s). An action for the identified claim(s) is determined based on a result of the comparison.

Abstract: A computing system may predict a word based on received user input that selects a part of the word (e.g., the first characters, the first root, etc.). Specifically, a program, when run on the computing system, may perform a method including creating a candidate list of words based on received user input. These words may be then organized into a hierarchy, or tree structure, in which each word is associated with a parent and each parent is a partial match for its associated words. The top-tier partial matches may be presented, and user input corresponding to a selected partial match may be received. A set of candidates related to the selected partial match may then be presented for user selection.

Abstract: Described herein are techniques for operating a security server to determine behavioral profiles for entities in a network and to detect attacks or unauthorized traffic in a network based on those behavioral profiles. In one technique, a behavioral profile may be generated based on requests for security operations to be performed that are received at a security server from an entity in a network. The behavioral profile may be generated using learning techniques, including artificial intelligence techniques such as neural networks. When the security server receives from an entity one or more requests for security operations to be performed, the security server may compare properties of the requests to the behavioral profile for the entity and properties of requests commonly sent by the entity. The security server may determine a similarity score indicating how similar the request are to the behavioral profile and to requests commonly received from the entity.

Abstract: Described herein are techniques for operating a security server to determine behavioral profiles for entities in a network and to detect attacks or unauthorized traffic in a network based on those behavioral profiles. In one technique, a behavioral profile may be generated based on requests for security operations to be performed that are received at a security server from an entity in a network. The behavioral profile may be generated using learning techniques, including artificial intelligence techniques such as neural networks. When the security server receives from an entity one or more requests for security operations to be performed, the security server may compare properties of the requests to the behavioral profile for the entity and properties of requests commonly sent by the entity. The security server may determine a similarity score indicating how similar the request are to the behavioral profile and to requests commonly received from the entity.

Abstract: Described herein are various principles that may be used together or separately to implement a search engine to retrieve and use metadata information in performing a search. In one embodiment described herein, a search engine accepts input from a user that describes a search the user desires to be performed. The search engine may then examine the input to determine whether the input relates to an artifact and to what artifact the input relates. The search engine may then retrieve metadata information from a source related to the determined artifact and use the metadata information in performing the search requested by the user.

Abstract: Signals to be processed are categorized based on signal characteristics such as physical aspects, context, conditions under which the signals were generated and source, and/or based on other variables. Categorized sets of signals are processed, and an accuracy for each set calculated. Weights are then applied to accuracy values for the sets, and the weighted values summed. In some cases, certain sums are then weighted and further summed.

Abstract: A licensing device is used for storing one or more licenses for licensable content such as music, video, e-books, software applications, online memberships, and the like. The licensing device communicates with a user's computing devices enabling licensable content to be downloaded and/or activated on a particular computing device. The communication can take many forms such as wireless, wired, or optical. Downloading and/or activation of the content upon confirmation of a valid license may be automatic or partially based on user input.

Abstract: A computing system may predict a word based on received user input that selects a part of the word (e.g., the first characters, the first root, etc.). Specifically, a program, when run on the computing system, may perform a method including creating a candidate list of words based on received user input. These words may be then organized into a hierarchy, or tree structure, in which each word is associated with a parent and each parent is a partial match for its associated words. The top-tier partial matches may be presented, and user input corresponding to a selected partial match may be received. A set of candidates related to the selected partial match may then be presented for user selection.

Abstract: A processing device may display a representation of a system resource. The system resource may include a menu, a folder, a filename, a shortcut, a textbox, or other resource. The user may make a gesture to indicate a desire to create a personalized identifier for the system resource. The processing device may display a personalized identifier writing area for inputting personalized identifier information. Personalized identifier information may be input via text, speech, digital ink, or other methods. The processing device may permit the user to configure types of information to be stored with personalized identifiers and how the personalized identifier information may be indexed for searching. In one embodiment, the input personalized identifier information may be translated to a second language and indexed for searching. Further, the processing device may permit the user to configure whether or how to display a personalized identifier for a system resource.

Abstract: New neural networks for handwriting recognition may be build from existing neural networks. An existing neural network pre-trained for a starting language is chosen based on a desired target language. The neural network is modified so that it may be used to recognize characters of the target language, and the modified neural network is used in a handwriting recognizer for the target language. Modification includes copying one or more of the primary outputs of the existing neural network. An appropriate starting language may be chosen based on the desired target language. In addition, a “super network” may be provided that is a relatively large neural network configured to recognize characters from a number of different languages. One may customize a handwriting recognizer using such a super network by programming a mask to block outputs from the super network that are not necessary for the language desired to be recognized.

Abstract: New neural networks for handwriting recognition may be build from existing neural networks. An existing neural network pre-trained for a starting language is chosen based on a desired target language. The neural network is modified so that it may be used to recognize characters of the target language, and the modified neural network is used in a handwriting recognizer for the target language. Modification includes copying one or more of the primary outputs of the existing neural network. An appropriate starting language may be chosen based on the desired target language. In addition, a “super network” may be provided that is a relatively large neural network configured to recognize characters from a number of different languages. One may customize a handwriting recognizer using such a super network by programming a mask to block outputs from the super network that are not necessary for the language desired to be recognized.

Abstract: Systems and methods are provided for selecting parameter values and combinations of parameter values to use in connection with testing. A graphical user interface allows a user to graphically illustrate the importance of parameter values by drawing a probability curve for each parameter. The resulting curves are then converted to mathematical functions that are then combined into a combination function. The combination function is then evaluated for different parameter value combinations. The parameter value combinations that cause the combination function to exceed a predetermined probability value are selected for testing.

Abstract: A system manager tests, administers and monitors, and/or diagnoses problems with a distributed system having a plurality of computing machines. Each machine includes an event monitoring agent and the system manager comprises a data collection module (DCM), a data management module (DMM), a data storing module (DSM), and a user interface module (UIM). The DCM receives a request from the DMM describing performance data to be collected from each agent, and based on the request the DCM then collects such performance data from each agent and sends same to the DMM. The DMM stores the performance data at the DSM along with metadata corresponding to the request for current and future usage, including monitoring, analyzing and evaluation. The UIM receives requests from a user and forwards same to the DMM, and formats data from the DSM and represents the formatted data to the user.

Abstract: Signals to be processed are categorized based on signal characteristics such as physical aspects, context, conditions under which the signals were generated and source, and/or based on other variables. Categorized sets of signals are processed, and an accuracy for each set calculated. Weights are then applied to accuracy values for the sets, and the weighted values summed. In some cases, certain sums are then weighted and further summed.