IAPP AIGP Exam - 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)?

AAnonymization.

BPseudonymization.

CDifferential privacy.

DFederated learning.

Show Suggested 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.

by Cathern at Nov 13, 2024, 01:13 AM

Limited Time Offer

25%

Off

Get Premium AIGP Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Sabine

3 months ago

Are we sure they really obfuscated everything? Sounds risky.

upvoted 0 times

...

Noah

3 months ago

Anonymization is the right term here, no doubt.

upvoted 0 times

...

Allene

3 months ago

Surprised they didn't just go with differential privacy!

upvoted 0 times

...

Werner

4 months ago

I think it's more like pseudonymization.

upvoted 0 times

...

An

4 months ago

This is definitely anonymization!

upvoted 0 times

...

Margo

4 months ago

I’m confused because I thought differential privacy was also about protecting data, but this seems more straightforward with anonymization.

upvoted 0 times

...

Lovetta

4 months ago

I practiced a question similar to this, and I believe it was about anonymization too. It makes sense given that they obfuscated all personal info.

upvoted 0 times

...

Ashton

4 months ago

I'm not entirely sure, but I remember something about pseudonymization keeping some identifiers. This seems more like full anonymization.

upvoted 0 times

...

Meghan

5 months ago

I think this is about anonymization since they removed all personal data completely.

upvoted 0 times

...

Jennifer

5 months ago

I'm a little confused by the wording here. Federated learning is about distributed training, not anonymization. I'm leaning towards either anonymization or pseudonymization, but I'll have to re-read the question to be sure.

upvoted 0 times

...

Helaine

5 months ago

Okay, I think I've got this. The key detail is that the company "permanently obfuscated all personal data elements." That sounds like they're removing the personal identifiers, which is the definition of anonymization. I'm confident that's the right answer.

upvoted 0 times

...

Mira

5 months ago

Hmm, I'm not totally sure about this one. Anonymization, pseudonymization, and differential privacy all seem like they could be relevant here. I'll have to think it through carefully.

upvoted 0 times

...

Jaime

5 months ago

This one seems pretty straightforward. The question is asking about a privacy-enhancing technique, and the details describe obfuscating personal data, which sounds like anonymization to me.

upvoted 0 times

...

Gilma

1 year 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!

upvoted 0 times

Lai

1 year ago

Federated learning might be useful for training models without sharing sensitive data.

upvoted 0 times

...

Elvera

1 year ago

Differential privacy could have been another option to consider for enhancing privacy.

upvoted 0 times

...

Rosann

1 year ago

I think they could have also used pseudonymization to protect the personal information.

upvoted 0 times

...

Frederica

1 year ago

Yeah, they definitely went all out with anonymizing the data.

upvoted 0 times

...

Judy

1 year ago

I'm not sure, but I think it could also be B) Pseudonymization because the data was obfuscated.

upvoted 0 times

...

Jamika

1 year ago

I agree with Lorriane, anonymization is the best choice to protect personal data.

upvoted 0 times

...

Huey

1 year 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.

upvoted 0 times