When choosing data sources to be used within a big data architecture, which of the following data attributes MUST be considered to ensure data is not aggregated?

The correct answer is B. Granularity. Granularity refers to the level of detail captured in a dataset. In big data environments, a key privacy risk is the aggregation problem: combining multiple datasets with fine-grained (highly granular) attributes can re-identify individuals even when each source dataset appears a

Privacy Architecture

Question

Options

AAccuracy
BGranularity
CConsistency
DReliability

How the community answered

(39 responses)

A
3% (1)
B
90% (35)
C
8% (3)

Explanation

Granularity refers to the level of detail captured in a dataset. In big data environments, a key privacy risk is the aggregation problem: combining multiple datasets with fine-grained (highly granular) attributes can re-identify individuals even when each source dataset appears anonymous on its own. By carefully controlling and limiting the granularity of data selected from each source, practitioners prevent the mosaic effect where detailed individual-level data points aggregate into a personally identifiable profile. Options A (accuracy), C (consistency), and D (reliability) are data quality attributes important for analytics correctness, but they do not directly govern whether combining datasets will create a privacy-violating aggregation of personal information.

Topics

#Data attributes#Granularity#Big data architecture#Data quality

Community Discussion

No community discussion yet for this question.

Full CDPSE Practice