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Privacy Talk with Kim Hamilton Duffy, Executive Director of Decentralized Identity Foundation: What…
- Learning Machine started with a focus on educational and workforce credentials, where lifelong learners could access, manage, and decide who they share them with, in contrast to the current models.
- The challenges were still the adoption for relying parties or consumers especially in regards to the self-sovereign identity stack, which is pretty complex.
- People can lead their own educational and workforce journeys, and then be able to find new opportunities by being able to establish trust in these micro credential certifications of competency.
- Academics like MIT were involved in this space, with a focus on public claims or credentials and anchoring hashes rather than storing the full credential.
- They immediately became aware of risks, where public data can be used against them, especially if someone changes their mind, is going into witness protection or for any reasons, do not want to be traceable.
- The way GDPR was characterised, it addressed any form of correlatable data, which led to dvelopment of did methods that are not on-chain.
- Personal data on blockchain is too risky as re-identification can be too easy.
- In their paper Decentralized Identity Foundation mentioned on the blogs and article, privacy identification in the AI period focused on sifting into the data minimization and inclusions of the processes of the credential issuing.
- They discuss how to achieve the equivalent interest in privacy identification in the AI period.
- Kim highlights that decentralized identity types of approaches are becoming more appealing where reusability of KYC credential, within organization to satisfy multiple jurisdiction KYC use cases.
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