← ACARA Digital Technologies Years 7–8

Checking de-identified data still answers the question

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.

Part of: ACARA Digital Technologies Years 7–8 — Digital Footprints and Data Minimisation — Module 50

Understanding Checking de-identified data still answers the question in ACARA Digital Technologies Years 7–8

What it does: The question needs age and sleep. AgeBand and SleepHours remain, so the check passes and the data is still useful. The kept columns answer the question, while the removed columns could identify a person. This is the balance between privacy and usefulness. Scan every column again. Address and phone number can still point to a real person. Personal data can hide in other columns or in extra sheets, so check them all. A fit-for-purpose check covers every sheet. Names on another sheet can still identify people. Generalising can make the data less useful if the bands are too wide. Always recheck both usefulness and privacy.

Common mistakes: Having some columns left does not mean the data can still answer the question. The check must compare the columns the question needs with the columns left: usable = needed all in left. The check has to scan every column for identifying data, not the rows. It should be: for col in sheet.columns:.

Class: _____________________  ·  Name: _____________________  ·  Date: _________

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This module's key terms are also in a printable crossword (Modules 752–770) and word search (Modules 696–770).

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Learning
Duo
You de-identified a survey by deleting the Name column. What should you check next?
Check the remaining columns can still answer the original questionNothing more, deleting Name always makes the data safe
Matches
Deleted the Name column. Done, the data is safe to share!
↓
Removing one column is not enough. The student did not scan the remaining columns, such as Email or Street, which could still identify someone.
Applying
Meanings
Term
fit-for-purpose check
Meaning
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.
Practice
SELECT house, AVG(points) AS avgPoints
Testing
Gaps
SELECT house, (points)
Check Answer
Spots
2left = ["house", "reads"]
3usable = len(left) > 0

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Quick Questions

1. You de-identified a survey by deleting the Name column. What should you check next?
A. Nothing more, deleting Name always makes the data safe
B. Check the remaining columns can still answer the original question ✓
Removing one column is only the start. You still need to check that the data is useful and that no other column can identify someone.
2. Your question is 'Which year level uses the canteen most?' You removed Name and Year Level. Is the data still fit for purpose?
A. No, the Year Level column is needed to answer the question ✓
B. Yes, because the data is now fully private
Compare the question with the remaining columns. Without Year Level you cannot answer it, so keep it (perhaps generalised) instead.
3. After deleting Name, a sheet still has Full Address and Phone Number columns. What does a fit-for-purpose check tell you?
A. Identifying columns remain, so remove or generalise them ✓
B. The data is de-identified because Name is gone
Scan every column again. Address and phone number can still point to a real person.
4. Which is the best way to scan your data after de-identifying it?
A. Only look at the first column on the first sheet
B. Look at every column on every sheet ✓
Personal data can hide in other columns or in extra sheets, so check them all.
5. A workbook has two sheets. You cleaned Sheet1 but forgot Sheet2, which lists student names. Is the data safe to share?
A. No, Sheet2 still holds identifying data ✓
B. Yes, Sheet1 is the main sheet so it is enough
A fit-for-purpose check covers every sheet. Names on another sheet can still identify people.

Match the Pairs

Question: Which year level reads the most? Columns left: YearLevel, BooksRead → The remaining columns can still answer the question, so the data passes the usefulness check.
Final scan: Sheet1 = OK, Sheet2 = Name column found, delete it → Scan every column on every sheet again, and remove any identifying column you find.
After removing data, compare your question with the columns that are left to see if you can still answer it. → Question: Do older students sleep less? Columns left: AgeBand, SleepHours
Balance privacy and usefulness: keep what answers the question and remove anything that can identify a person. → Keep: SleepHours, AgeBand | Remove: Name, Email, Street
Deleted the Name column. Done, the data is safe to share! → Removing one column is not enough. The student did not scan the remaining columns, such as Email or Street, which could still identify someone.
Question: Which house has the most points? Columns left: Age, Hobby → The columns left cannot answer the question because the House and Points columns were deleted. The data is no longer useful.

Key Term

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.

Fill in the Blanks

1. SELECT house, AVG(points) FROM students GROUP BY house;
Hint: Type the function that finds the mean, so the house and points columns still answer the question.
2. FOR EACH column IN sheet IF column identifies a person THEN flag column
Hint: Type the keyword that makes the check visit every column in turn.
3. =AVERAGEIF(B2:B30,"13-14",C2:C30)
Hint: Type the function that averages values only where a condition matches, using the age band column.
4. SELECT * FROM students LIMIT 3;
Hint: Type the symbol that shows every column so you can scan for identifying data.
5. IF canAnswer AND noIdentifiers THEN fitForPurpose ← TRUE
Hint: Type the logical operator that needs both conditions true: useful data and no identifying column.

Spot the Error

Question 1
A needed = ["house", "reads"]
B left = ["house", "reads"]
C usable = len(left) > 0
D print(usable)
Line C — Having some columns left does not mean the data can still answer the question. The check must compare the columns the question needs with the columns left: usable = needed all in left.
Question 2
A for col in sheet.rows:
B if col can identify:
C flag(col)
Line A — The check has to scan every column for identifying data, not the rows. It should be: for col in sheet.columns:
Question 3
A sheets = ["Survey", "Codes"]
B for s in sheets:
C scan_columns(sheets[0])
Line C — This scans only the first sheet every time. Every sheet must be scanned again, so the loop must use the loop variable: scan_columns(s).
Question 4
A question = "Sleep by age band"
B needed = ["age_band"]
C left = ["age_band", "sleep"]
D print(needed all in left)
Line B — The question compares sleep across age bands, so it needs both columns. If sleep is left out of needed, the check would pass even when the sleep column has been deleted.
Question 5
A missing = needed - left
B for col in missing:
C if not safe(col): add_back(col)
Line C — If the data has lost a column the question needs, you can only add it back when it is safe (not identifying). Adding back unsafe columns brings the privacy risk back. It should be: if safe(col).

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