Abstract: Banking and telecommunication network data may be combined to generate credit scores by separating aspects of credit score computation to protect the privacy of consumers and each institution that may collect data. A system of unsupervised, semi-supervised, or adaptive learning may be separated between a bank and a telecommunications network. Telecommunications networks may generate summarized statistics of their users, which may be combined into a formula for calculating an estimated credit score. The factors used for the formula may be transmitted to the bank, which may compare the calculated scores with actual financial behavior. The bank may generate updated factors, which may be returned to the telecommunications network to update the credit scoring formula.

Abstract: A system may generate abstracted graphs from a social relationship graph in response to a query. A query may identify a person for which permission has been obtains to collect their data. The abstracted graphs may include summary statistics for various relationships of the person. The relationships may include other persons, places, things, concepts, brands, or other object that may be present in a social relationship graph, and the relationships may be presented in an abstracted or summarized form. The abstracted form may preserve data that may be useful for the requestor, yet may prevent the requestor from receiving some raw data. When two or more people have given consent, the data relating to the consenting persons may be presented in a non-abstracted manner, while other data may be presented in an abstracted manner.

Abstract: Telecommunications data may be summarized into mathematical statistics that may not correlate with conventional semantic attributes. Such statistics may be difficult to observe without access to the telecommunications data, and therefore may be much less susceptible to social engineering attacks or other privacy-related vulnerabilities. The mathematical statistics may represent first, second, or higher order behavior-related observations relating to subscribers physical movements, engagement of applications and web browsing on a mobile device, as well as usage and billing of a mobile device. The statistics may not correlate to semantic identifiers for subscribers, and therefore may be difficult to observe and therefore identify specific subscribers whose statistical summaries may be known.

Abstract: Web usage behavior may be labeled by topics and used with other telecommunications network observations in various advertising campaigns. Web browsing behavior may be captured to identify domain names visited by subscribers, and the domain names may be classified using keywords or databases of domain topics. Subscriber usage behavior may identify those subscribers having a high affinity for specific topics. Further, affinity may be determined for subscribers having affinity in their baseline behavior patterns as well as those subscribers who may be deviating from their baseline behavior. Tables of users and their affinity may be generated, which may be used to identify potential candidates for various advertising campaigns.

Abstract: Telecommunications data may be summarized into mathematical statistics that may not correlate with conventional semantic attributes. Such statistics may be difficult to observe without access to the telecommunications data, and therefore may be much less susceptible to social engineering attacks or other privacy-related vulnerabilities. The mathematical statistics may represent first, second, or higher order behavior-related observations relating to subscribers physical movements, engagement of applications and web browsing on a mobile device, as well as usage and billing of a mobile device. The statistics may not correlate to semantic identifiers for subscribers, and therefore may be difficult to observe and therefore identify specific subscribers whose statistical summaries may be known.