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IAPP Exam AIGP Topic 7 Question 19 Discussion

Actual exam question for IAPP's AIGP exam
Question #: 19
Topic #: 7
[All AIGP Questions]

A company initially intended to use a large data set containing personal information to train an Al model. After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model.

This is an example of applying which privacy-enhancing technique (PET)?

Show Suggested Answer Hide Answer
Suggested Answer: A

Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset. Reference: AIGP Body of Knowledge, privacy-enhancing techniques section.


Contribute your Thoughts:

Gilma
2 months ago
Haha, I bet the data scientists had a field day with 'obfuscating' all that personal info. Sounds like they were really 'anonymizing' the heck out of that data!
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Lai
5 days ago
Federated learning might be useful for training models without sharing sensitive data.
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Elvera
13 days ago
Differential privacy could have been another option to consider for enhancing privacy.
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Rosann
15 days ago
I think they could have also used pseudonymization to protect the personal information.
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Frederica
27 days ago
Yeah, they definitely went all out with anonymizing the data.
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Judy
2 months ago
I'm not sure, but I think it could also be B) Pseudonymization because the data was obfuscated.
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Jamika
2 months ago
I agree with Lorriane, anonymization is the best choice to protect personal data.
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Huey
2 months ago
Hmm, I'm not so sure. This sounds more like differential privacy to me, where they added noise to the data to preserve privacy while still deriving value.
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Laticia
26 days ago
C) Differential privacy.
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Mattie
1 months ago
B) Pseudonymization.
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Melodie
1 months ago
A) Anonymization.
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Gwenn
2 months ago
I'd say pseudonymization. Obfuscating the personal data elements but still keeping some linkability sounds like what they did here.
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Dominga
1 months ago
C) Differential privacy.
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Stephaine
1 months ago
B) Pseudonymization.
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Lamar
1 months ago
A) Anonymization.
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Lorriane
2 months ago
I think the answer is A) Anonymization.
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Goldie
2 months ago
Definitely anonymization. Removing all personal information from the data set before training the model is the textbook definition of anonymization.
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Amber
2 months ago
Definitely anonymization.
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Celeste
2 months ago
C) Differential privacy.
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Amie
2 months ago
B) Pseudonymization.
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Trina
2 months ago
A) Anonymization.
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