ACARA Digital Technologies Years 7–8 Word Scramble: Digital Footprints and Data Minimisation (Modules 752–770)
Name:
Date:
Class:
🔤
Word scramble
Unscramble the letters and write each key term on the line.
1
LETDEE LNMUCO
Right-click a column header and choose Delete to remove the whole column and all its data. Undo with Ctrl+Z if it was a mistake, and check formulas that used the column.
2
DDINEH LCONUM
A ______ is only out of sight. Its data stays in the file and is shared with it, and anyone can unhide it. Delete the column to really remove the data.
3
ENIGASGLNEIR
Replacing an exact value with a broader group, like a birth date with an age band, so the data is less identifying but still useful.
4
=2FCT(EL,2)
Keeps only the first 2 characters of the postcode in cell C2, so an exact location becomes a wider region. This helps protect privacy.
5
TE-DIRCETOIINAINF
Working out who a person is by matching a mix of details, like age, suburb and year level, that fits only a very small group, even a group of one. Bigger groups hide people better.
6
SUF-PRI-PEOFOTR ECHCK
After removing or generalising data, check that the columns left can still answer your question and that no column can still identify a person. Scan every column and sheet again.
7
AEFK ETTS AADT
Invented but realistic values made by a data generator, so you can build and test a solution without collecting any real person's data.
8
IFGNIND
A clear statement that names the system, the data collected, whether it is essential and a change to make, joined with 'because' to give the reason.
Answer Key
ACARA Digital Technologies Years 7–8 · Modules 752–770
1LETDEE LNMUCO → Delete column — Right-click a column header and choose Delete to remove the whole column and all its data. Undo with Ctrl+Z if it was a mistake, and check formulas that used the column.
2DDINEH LCONUM → hidden column — A hidden column is only out of sight. Its data stays in the file and is shared with it, and anyone can unhide it. Delete the column to really remove the data.
3ENIGASGLNEIR → generalising — Replacing an exact value with a broader group, like a birth date with an age band, so the data is less identifying but still useful.
4=2FCT(EL,2) → =LEFT(C2,2) — Keeps only the first 2 characters of the postcode in cell C2, so an exact location becomes a wider region. This helps protect privacy.
5TE-DIRCETOIINAINF → re-identification — Working out who a person is by matching a mix of details, like age, suburb and year level, that fits only a very small group, even a group of one. Bigger groups hide people better.
6SUF-PRI-PEOFOTR ECHCK → fit-for-purpose check — After removing or generalising data, check that the columns left can still answer your question and that no column can still identify a person. Scan every column and sheet again.
7AEFK ETTS AADT → fake test data — Invented but realistic values made by a data generator, so you can build and test a solution without collecting any real person's data.
8IFGNIND → finding — A clear statement that names the system, the data collected, whether it is essential and a change to make, joined with 'because' to give the reason.