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.
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Title: EXECUTIVE SUMMARY |
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Executive Summary
Privacy in Ambient Intelligence assumes users trust in service providers. Personal as well as context data is collected by sensors, cameras and RFID readers, e.g., in the METRO Extra-Future Store. The use of loyalty cards maps collected data to users and transforms context data to personal data. Users are neither able to decide on the access of personal data nor to verify the collection and use of personal data, since they are not aware of every collection. Current privacy-enhancing technologies focus on the collection of personal data but not on the usage of personal data.
The identification of requirements for mechanisms for the enforcement of privacy policies and the verification of their enforcement regarding the collection and processing of personal data is the objective of WP14. Privacy evidences, to be used in case of dispute between users and service providers, are proposed on this workshop as a step towards the enforcement of privacy policies. A precondition for privacy evidences is the logging of service provider activities concerning the collection and use of personal data.
This workshop has shown that such log data has to be authentic, i.e., it must faithfully reflect reality and not allow parallel realities. Since log data consists of personal data, e.g. the IP address of user’s personal device, the log data itself is personal in nature and must therefore be kept confidential.
The requirements for secure logging will be presented by the WP14 deliverable D14.6 “From Regulating Access Control on Personal Data to Transparency by Secure Logging”.
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