Abstract: A two-stage anonymization process is applied to monitored network traffic in which unique user identifiers, such as the MSISDN (Mobile Station International Subscriber Directory Number), are extracted from the traffic and anonymized to generate an ASI (anonymized subscriber identifier). A strictly random RSI (random subscriber identifier) is generated and used to replace the ASI. The RSI is generated upon a first occurrence of an ASI and stored in a lookup table for utilization upon subsequent ASI occurrences. Use of the strictly random RSI enables various studies and analysis of user behavior to be performed at a heightened level of privacy protection as compared with conventional anonymization schemes that do not utilize strictly random identifiers.

Abstract: A two-stage anonymization process is applied to monitored network traffic in which unique user identifiers, such as the MSISDN (Mobile Station International Subscriber Directory Number), are extracted from the traffic and anonymized to generate an ASI (anonymized subscriber identifier). A strictly random RSI (random subscriber identifier) is generated and used to replace the ASI. The RSI is generated upon a first occurrence of an ASI and stored in a lookup table for utilization upon subsequent ASI occurrences. Use of the strictly random RSI enables various studies and analyses of user behavior to be performed at a heightened level of privacy protection as compared with conventional anonymization schemes that do not utilize strictly random identifiers.

Abstract: A network intelligence solution (NIS) is arranged to tap a stream of IP (Internet Protocol) packets traversing a node in a network that supports a mobile communications service between mobile equipment employed by subscribers in a universe of subscribers to the service and one or more remote servers such as web servers. The NIS performs deep packet inspection to measure Internet usage by the universe of subscribers as well as usage by a subscriber panel that is a representative subset of the universe. A unique network identifier is generated, for example using the MSISDN (Mobile Subscriber Integrated Services Digital Network Number) associated with each subscriber which is anonymized, to enable socio-demographic information collected from the subscriber panel to be correlated to the panel's Internet usage. The correlations can then be extrapolated to make generalizations about socio-demographics of the larger subscriber universe.

Abstract: Domains supported by websites accessible to mobile network users over the Internet are classified into pre-defined categories based on domain content. A network intelligence solution (NIS) taps a stream of IP (Internet Protocol) packets traversing a node in the network between mobile equipment employed by network users and remote web servers. The NIS performs deep packet inspection to aggregate Internet usage so that a distribution of frequency of access by the network users to each of the classified domains may be calculated. Clusters encompassing one or more of the categories are specified based, at least in part, on the access frequency distribution. Each network user is assigned to one or more clusters based at least on observations of the user's frequency of access to the classified domains. Clusters are specified to meet a target homogeneity of access frequency for each encompassed category and further to meet a target heterogeneity across clusters.

Abstract: A network intelligence solution (NIS) is arranged to access a stream of IP (Internet Protocol) packets associated with communications over a mobile communications network between mobile equipment employed by a user and a remote server such as a web server. When the mobile equipment accesses the network, the TAC (Type Allocation Code) portion of the IMEI (International Mobile Equipment Identity) is extracted from the IP stream at the NIS so that information about the mobile equipment such as technical information (e.g., manufacturer, model, operating system, etc.) and market data (e.g., market share, average sales price of the equipment, etc.) can be retrieved from one or more databases. The NIS performs deep packet inspection (DPI) to measure Internet usage by the mobile equipment user with each network access. Relationships between variables and/or observed data in each of the Internet usage data and mobile equipment information may then be identified.

Abstract: A method for collecting, processing and analyzing Internet and e-commerce data accessed by users of messaging devices such, for example, as mobile terminal users includes receiving network access data extracted from packetized traffic of a communication system. A portion of the extracted network access data is encrypted to anonymize the received network access data, obscuring information from which messaging device users' identities might otherwise be determined. The encrypted portion constitutes a unique, anonymized identifier that can be correlated to the messaging device user associated with the traffic. Network access data anonymized in this manner, once received, is processed for analysis. By referencing the identifier, anonymized network access data associated with any messaging device user is distinguishuable from anonymized network access data associated with all other messaging device user—allowing patterns of internet access activity of the users to be tracked and reported anonymously.

Abstract: A method for collecting Internet and e-commerce data accessed via messaging devices such as mobile terminals comprises monitoring packet traffic in a communication system providing communication services to the messaging devices and extracting network data from packets associated with respective device users. The portion of extracted network data from which a user's identity might otherwise be determined is encrypted, creating an anonymized, unique identifier correlated to network access data extracted from any packet traffic applicable to that user. Network access data associated with each user is distinguishable from network access data associated with all other users on the basis of the unique identifier. A third party granted access to the anonymized network access data, associated with identifiably unique but anonymous users of the communication system, may retrieve and store the data in a database for analysis.