Difference between revisions of "Assured Identity"
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==Full Title or Meme== | ==Full Title or Meme== | ||
| − | An [[Assured Identity]] is an [[Identifier]] together with the Identity [[Assurance]] Level that the identifier represents a real world entity. | + | An [[Assured Identity]] is an [[Identifier]] together with the Identity [[Assurance]] Level that the identifier represents a real-world [[Entity]]. |
| + | |||
| + | ==Context== | ||
| + | This page is about the strength of the binding between an [[Identifier]] and a real-world [[Entity]]. As a part of the evaluation of the strengh of the binding, the quality of the documentation of the real-world entity might need to be validated. | ||
| + | |||
| + | ==Examples== | ||
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| + | ===DISA=== | ||
| + | * [https://disa.mil/-/media/Files/DISA/Fact-Sheets/Fact-Sheet-Assured-Identity_Aug-2017_RELEASABLE.ashx Assured Identity - DISA driving anonymity out of its networks] | ||
| + | DISA is pursuing the development of assured identity to advance how federal agencies identify and authenticate people and | ||
| + | devices to provide a more secure computing environment in these key areas: | ||
| + | Hardware | ||
| + | Attestation | ||
| + | Protection of the derived credential from theft | ||
| + | • Leaning on commercially available hardware-based secure elements to protect credential/associated private key on | ||
| + | mobile devices similar to how DISA uses the Common Access Control (CAC) card. | ||
| + | Mobile CMFA | ||
| + | Mobile devices host a plethora of sensors and resources to collect, process, analyze and react to data of the surrounding | ||
| + | environment. | ||
| + | • Prototyping reliability and usability of biometric and contextual factors to continuously access, supplement, and | ||
| + | strengthen the authentication process. Some factors being considered are: fingerprint, iris, face, voice, trusted | ||
| + | location, proximity of other devices, and connected Wi-Fi networks. | ||
| + | Desktop CMFA | ||
| + | Common office environment relies on possessing the CAC to represent an individual’s identity and has proven reliable | ||
| + | and more secure than traditional username/password. | ||
| + | • DISA is piloting a solution than can prevent, detect and respond to misuse of user’s credentials. Pattern-based | ||
| + | building of user profiles with machine learning through a software ag | ||
==References== | ==References== | ||
[[Category: Assurance]] | [[Category: Assurance]] | ||
| − | [[ | + | [[Category: Identity]] |
Revision as of 19:30, 16 February 2022
Full Title or Meme
An Assured Identity is an Identifier together with the Identity Assurance Level that the identifier represents a real-world Entity.
Context
This page is about the strength of the binding between an Identifier and a real-world Entity. As a part of the evaluation of the strengh of the binding, the quality of the documentation of the real-world entity might need to be validated.
Examples
DISA
DISA is pursuing the development of assured identity to advance how federal agencies identify and authenticate people and devices to provide a more secure computing environment in these key areas: Hardware Attestation Protection of the derived credential from theft • Leaning on commercially available hardware-based secure elements to protect credential/associated private key on mobile devices similar to how DISA uses the Common Access Control (CAC) card. Mobile CMFA Mobile devices host a plethora of sensors and resources to collect, process, analyze and react to data of the surrounding environment. • Prototyping reliability and usability of biometric and contextual factors to continuously access, supplement, and strengthen the authentication process. Some factors being considered are: fingerprint, iris, face, voice, trusted location, proximity of other devices, and connected Wi-Fi networks. Desktop CMFA Common office environment relies on possessing the CAC to represent an individual’s identity and has proven reliable and more secure than traditional username/password. • DISA is piloting a solution than can prevent, detect and respond to misuse of user’s credentials. Pattern-based building of user profiles with machine learning through a software ag
