Determining pseudonym values using tweak-based encryption

    公开(公告)号:US11106821B2

    公开(公告)日:2021-08-31

    申请号:US15926425

    申请日:2018-03-20

    Abstract: A technique includes accessing data that represents a plurality of values that are associated with a plurality of ranges. The technique includes determining a pseudonym value for a given value, where the given value is associated with a given range and determining the pseudonym includes encrypting the given value to provide the pseudonym value; controlling the encryption to cause the pseudonym value to be within the given range; and tweaking the encryption based on an attribute that is associated with the given value.

    Applying a degree of transparency
    83.
    发明授权

    公开(公告)号:US11048372B2

    公开(公告)日:2021-06-29

    申请号:US15545820

    申请日:2015-02-12

    Abstract: Implementations disclosed herein relate to a device for applying a degree of transparency. The device may comprise a display engine to display a foreground and background area, wherein the background area is associated with a first application and the foreground area is associated with a second application. The device may further comprise a transparency engine to apply a degree of transparency to the foreground area in an overlap area between the foreground area and the background area. The device may further comprise a trigger engine to determine whether a trigger event associated with the first application has occurred, wherein the trigger event is based on a metric value received by the first application, and in response to determining that the triggering event associated with the first application has occurred, cause the display engine to display the first application in front of the second application.

    Generating a combined entity workflow

    公开(公告)号:US11017342B2

    公开(公告)日:2021-05-25

    申请号:US15753255

    申请日:2015-08-31

    Abstract: Example implementations relate to generating a combined entity workflow. For example, a method for generating a combined entity workflow may include defining, using a workflow engine, a business logic block for each of a plurality of information technology (IT) entity types within an IT system, wherein the business logic block comprises a non-scripted declarative business rule, defining, for each of the plurality of entity types and using the workflow engine, a relationship between the business logic block and a model workflow for the respective entity type, and generating, using the workflow engine, a combined entity workflow for each of the plurality of entity types, by combining the business logic block and the model workflow.

    Database comparison operation to identify an object

    公开(公告)号:US10956493B2

    公开(公告)日:2021-03-23

    申请号:US15570878

    申请日:2015-05-19

    Abstract: Examples disclosed herein relate to a database comparison operation to identify an object. For example, a processor may enroll a set of object templates in a storage based on objects within input content and enroll a target object template in the storage based on a target object in target content. The processor may identify an object within the input content associated with the target object based on a database comparison operation of the stored set of object templates to the stored target object template. The processor may output object recognition information related to the identified object.

    Dynamic function invocation
    86.
    发明授权

    公开(公告)号:US10936592B2

    公开(公告)日:2021-03-02

    申请号:US15565521

    申请日:2015-04-30

    Abstract: According to an example, dynamic function invocation may include ascertaining a query for a database management system (DBMS). Dynamic function invocation may further include implementing a dynamic function as a meta user defined function to invoke a plurality of different coded functions including a coded function that is to be invoked by the query to perform an operation related to the DBMS, and executing the dynamic function by the query to load and invoke the coded function to perform the operation related to the DBMS.

    MOBILE DEVICE LOCATOR
    88.
    发明申请

    公开(公告)号:US20210011111A1

    公开(公告)日:2021-01-14

    申请号:US17030673

    申请日:2020-09-24

    Abstract: Examples herein involve estimating a first position of a mobile device based on first communication signals, assigning a first set of particles to a number of respective first sampling locations within a threshold distance of the first position, adjusting the assignment of the first set of particles to second sampling locations based on movement of the mobile device, and estimating a second position of the mobile device based on the second sampling locations.

    MACHINE LEARNING-BASED NETWORK DEVICE PROFILING

    公开(公告)号:US20200382527A1

    公开(公告)日:2020-12-03

    申请号:US16428422

    申请日:2019-05-31

    Abstract: A method includes applying, by a computer, supervised machine learning to classify a network device that is associated with a security event occurring in a computer system based on data representing features of the network device. The security event is associated with a potential security threat to the computer system, and the classification of the network device by the supervised machine learning is associated with a confidence. The technique includes, in response to the confidence being below a threshold, applying an active machine learning classifier to the data to learn a classification for the data and using the classification learned by the active machine learning classifier to adapt the supervised machine learning to recognize the classification.

    MAPPING BETWEEN RAW ANOMALY SCORES AND TRANSFORMED ANOMALY SCORES

    公开(公告)号:US20200379863A1

    公开(公告)日:2020-12-03

    申请号:US16426850

    申请日:2019-05-30

    Abstract: In some examples, a system is to, given an anomaly score threshold over which at least one anomalous point is to be observed in a test set of points with a specified probability, determine, using raw anomaly scores for a training set of points, a first mapping between raw anomaly scores in a first range and first transformed anomaly scores using a first transformation technique. The system is to determine, using the raw anomaly scores for the training set of points, a second mapping between raw anomaly scores in a second range greater than the first range and second transformed anomaly scores using a second transformation technique different from the first transformation technique. The system is to use the first mapping and the second mapping to detect an anomaly in a computing environment based on the test set of points.

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