Resources
- Identity Use Cases & Scenarios.
- FIDIS Deliverables.
- Identity of Identity.
- Interoperability.
- Profiling.
- Forensic Implications.
- HighTechID.
- Privacy and legal-social content.
- D13.1: Identity and impact of privacy enhancing technologie.
- D13.1 Addendum: Identity and impact of privacy enhancing technologies.
- D13.3: Study on ID number policies.
- D13.6 Privacy modelling and identity.
- D13.7: Workshop Privacy.
- D14.1: Workshop on Privacy in Business Processes.
- D14.2: Study on Privacy in Business Processes by Identity Management.
- D14.3: Study on the Suitability of Trusted Computing to support Privacy in Business Processes.
- D14.4: Workshop on “From Data Economy to Secure.
- D16.3: Towards requirements for privacy-friendly identity management in eGovernment.
- Mobility and Identity.
- Other.
- IDIS Journal.
- FIDIS Interactive.
- Press & Events.
- In-House Journal.
- Booklets
- Identity in a Networked World.
- Identity R/Evolution.
Information filtering systems aim at countering information overload by extracting information that is relevant for a given user out of a large amount of information available via an information provider, based on user profiles containing, for each given user, personal data such as preferences, and/ or rated items. Information filtering systems may be applied in order to generate recommendations of items that are probably relevant for a given user, in order to predict the relevance of specific items for a given user, or in order to determine users with similar interests. Typical examples are the recommender system used by amazon.com which suggests potentially relevant books or other multimedia, based on the respective user’s preferences and shopping history. Another example is the recommender system used by last.fm which provides personalised song play lists. While in these well known cases the information provider and the provider of the recommender system functionality are realized by a single entity, information filtering systems in most cases constitute a multi-stage business process in which personal data has to be delegated from the information provider to the recommender system provider (cf. Figure 5). The information d in this case is the result data, e.g. a set of recommendations.
Figure Actors and their roles in an information filtering system, if a user profile is going to be delegated in order to offer personalised information, namely recommendations.
In information filtering, the problem introduced above arise because the information provider as well as the recommender system provider may use or delegate the user profile without the consent of the user and thus violate the agreed privacy policy. Users are therefore often reluctant to use recommender systems especially in the context of sensitive information domains, where the user profile contains e.g. financial or health-related personal data.
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