Feedback Statistics on Anonymous Service Usage

Service providers and advertisers recently intensify their efforts to personalize services and product messages to fit the targeted person's profile and thus increase the user's attention. Without the user's explicit consent however, privacy protection laws and regulations justifiably hinder access to personal information. The providers are often constraint to identify potential target groups of their interest by evaluation of anonymous usage statistics or proactively provided user feedback. This paper presents a means to automatically gather and associate service feedback data with parts of the user's identity information. This allows a classification of users into groups with the same identity characteristics, yet maintaining the privacy protection compliant to applicable laws. The key point of this solution is the introduction of pseudonyms, which get linked to the usage statistics and feedback data collected by the service provider. An information query/response system provides the association between feedback and identity data over these pseudonyms, and enables the subsequent retrieval of statistical identity information about a group of users. However, identifying information remains undisclosed and the user's privacy protected. We implemented a prototype which proves the applicability and potential of this approach.