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: Representative sample pages from websites accessible to Internet users are manually selected and classified into pre-defined categories based on page content to create a training set as an input to a classifier. An automated analysis is performed to identify a list of catchwords comprising the most frequently referenced words, tags, and/or links from the classified samples in each category in the training set. A data mining tool generates unique sets of distinctive catchwords and/or distinctive combinations of catchwords that have a high probability of appearing only in a single one of the pre-defined content categories. The classifier utilizes the sets of distinctive catchwords/combinations to classify new pages into one or more of the pre-defined content categories.