Resources
- Identity Use Cases & Scenarios.
- FIDIS Deliverables.
- Identity of Identity.
- Interoperability.
- Profiling.
- Forensic Implications.
- HighTechID.
- D3.1: Overview on IMS.
- D3.2: A study on PKI and biometrics.
- D3.3: Study on Mobile Identity Management.
- D3.5: Workshop on ID-Documents.
- D3.6: Study on ID Documents.
- D3.7: A Structured Collection on RFID Literature.
- D3.8: Study on protocols with respect to identity and identification – an insight on network protocols and privacy-aware communication.
- D3.9: Study on the Impact of Trusted Computing on Identity and Identity Management.
- D3.10: Biometrics in identity management.
- D3.11: Report on the Maintenance of the IMS Database.
- D3.15: Report on the Maintenance of the ISM Database.
- D3.17: Identity Management Systems – recent developments.
- D12.1: Integrated Workshop on Emerging AmI Technologies.
- D12.2: Study on Emerging AmI Technologies.
- D12.3: A Holistic Privacy Framework for RFID Applications.
- D12.4: Integrated Workshop on Emerging AmI.
- D12.5: Use cases and scenarios of emerging technologies.
- D12.6: A Study on ICT Implants.
- D12.7: Identity-related Crime in Europe – Big Problem or Big Hype?.
- D12.10: Normality Mining: Results from a Tracking Study.
- Privacy and legal-social content.
- Mobility and Identity.
- Other.
- IDIS Journal.
- FIDIS Interactive.
- Press & Events.
- In-House Journal.
- Booklets
- Identity in a Networked World.
- Identity R/Evolution.
D3.10: Biometrics in identity management
Biometric raw data potentially in many cases contain additional information about the person they belong to. In many cases, this additional information is health related. In this context also the term “indirect medical implications” is being used. In the context of the Data Protection Directive 95/46/EC this kind of additional information is considered highly sensitive. Many implementations of biometrics use templates as biometric reference data instead of biometric raw data. It is obvious that in these cases additional information is reduced compared to the use of raw data, but mostly no systematic research has been carried out so far with respect to remaining additional information in templates. In some cases it can be concluded from the method used for feature extraction that additional information might still be present in certain types of templates. The following table lists commonly used biometric methods, additional information known to be found in the raw data and additional information likely to be still included in templates.
Table 8: Additional information in biometric raw data and templates
For many biometrics it is currently not clear whether biometric templates include additional information or not. Based on the used method in some cases, it is very likely that health related additional information potentially might still be in the template. Examples for this are face geometry (face asymmetry potentially indicates certain diseases of the nervous system) and hand geometry measurement (certain geometry pattern indicate Marfan syndrome, gout or arthritis).
In this context future research is necessary. In cases where no additional information is contained in templates, the use of templates could have – together with an appropriate system design – a positive influence on the proportionality for the use of this biometrics for certain areas of applications.
Unobserved and non interactive authentication
Certain biometrics, such as behavioural biometrics and face geometry, allow for the collection and processing of biometric raw data without active participation of the user. In most cases, it is possible to use hidden sensors that cannot easily be observed by the persons that are going to be authenticated using the biometric system. The described types of biometrics thus support unobserved and non-interactive authentication (by identification or verification using biometrics). Depending on the area of use, this type of authentication can have especially negative consequences for the data subject in cases where the authentication fails due to technical reasons (e.g. False Rejection Rate (FRR)). In many cases, failure rates of biometric systems can be expected to increase in cases where the data subject is unaware of the authentication procedure and therefore does not co-operate. As testing of biometric systems supporting non-interactive authentication typically is done with volunteers supporting the testing, no research data seems to be available with respect to the impact of lack of co-operation on failure rates. This type of use of biometrics may be in a biometric Type III mixed model and Type V tracking model. As no informed consent by the data subject is possible in this case, this type of authentication should be limited to areas of application which are strictly regulated by law.
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