Abstract: In an example embodiment, a submission of confidential data is received from a user via a first computerized user interface. An identification of the user is obtained, and details regarding the submission are stored in a submission table. Then a request to display, to the user, statistical information derived from confidential data from users other than the user, is received from a second computerized user interface. Based on information stored in the submission table, a determination is made that the request to display, to the user, statistical information derived from confidential data from users other than the user should be granted. In response to the determining, the statistical information derived from confidential data from users other than the user is displayed via the second computerized user interface.

Abstract: In an example embodiment, an attribute interference model is trained by a machine learning algorithm to output missing attribute values from a member profile of a social networking service. In an attribute inference phase, an identification of a member of a social networking service is obtained. A member profile corresponding to the member of the social networking service is retrieved using the identification. The member profile is then passed to the attribute inference model to generate one or more missing attribute values for the member profile. A collection flow, defined in a user interface of a computing device, is modified based on the generated one or more missing attribute values, the collection flow defining a sequence of screens for collecting confidential data. The modified collection flow is then presented to the member in the user interface to collect confidential data from the member.

Abstract: In an example embodiment, a submission of confidential data is received from a user. A first service is queried using an identification of the user to obtain a member profile corresponding to the user in a social networking service. One or more primary attribute values are identified from the member profile. The one of the primary attribute values are used to query a second service to obtain a derived attribute value corresponding to the one or more primary attribute values. The confidential data, one or more of the primary attribute values, and the derived attribute value are stored in a first submission table in a confidential information database. Then the one or more of the primary attribute values and the derived attribute value are used to classify the user into one or more slices.

Abstract: In an example embodiment, an attribute interference model is trained by a machine learning algorithm to output missing attribute values from a member profile of a social networking service. In an attribute inference phase, an identification of a member of a social networking service is obtained. A member profile corresponding to the member of the social networking service is retrieved using the identification. The member profile is then passed to the attribute inference model to generate one or more missing attribute values for the member profile. A collection flow, defined in a user interface of a computing device, is modified based on the generated one or more missing attribute values, the collection flow defining a sequence of screens for collecting confidential data. The modified collection flow is then presented to the member in the user interface to collect confidential data from the member.

Abstract: In an embodiment, a submission history table is maintained by tracking an identification of each user making a submission of a confidential data value and a timestamp of when the corresponding submission was made. A first confidential data value submission is received from a user having a first identification. Member usage information for the user having the first identification, are retrieved based on the first identification. The submission history table is referenced to determine a length of time since the user having the first identification last made a submission of confidential data. It is determined that the user having the first identification is not permitted to submit confidential information based on the member usage information and the length of time since the user having the first identification last made a submission of confidential data. In response to the determining, the first confidential data value is discarded.

Abstract: In an example embodiment, an attribute interference model is trained by a machine learning algorithm to output missing attribute values from a member profile of a social networking service. In an attribute inference phase, an identification of a member of a social networking service is obtained. A member profile corresponding to the member of the social networking service is retrieved using the identification. The member profile is then passed to the attribute inference model to generate one or more missing attribute values for the member profile. A collection flow, defined in a user interface of a computing device, is modified based on the generated one or more missing attribute values, the collection flow defining a sequence of screens for collecting confidential data. The modified collection flow is then presented to the member in the user interface to collect confidential data from the member.

Abstract: In an embodiment, a submission history table is maintained by tracking an identification of each user making a submission of a confidential data value and a timestamp of when the corresponding submission was made. A first confidential data value submission is received from a user having a first identification. Member usage information for the user having the first identification, are retrieved based on the first identification. The submission history table is referenced to determine a length of time since the user having the first identification last made a submission of confidential data. It is determined that the user having the first identification is not permitted to submit confidential information based on the member usage information and the length of time since the user having the first identification last made a submission of confidential data. In response to the determining, the first confidential data value is discarded.

Abstract: In an example embodiment, a method for protecting against incorrect confidential data values in a computer system is provided. A machine learning algorithm is used to train a confidential data value quality score based on metrics extracted from member profile and member usage information in a social networking service. The confidential data value quality score model is then used to output an estimated quality score for submitted confidential data values.