← ACARA Digital Technologies Years 7–8

Re-identification risk from small groups and combined details

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.

Part of: ACARA Digital Technologies Years 7–8 — Digital Footprints and Data Minimisation — Module 49, QCE Digital Solutions Unit 2 — Data and Programming Techniques — Module 124

Understanding Re-identification risk from small groups and combined details in ACARA Digital Technologies Years 7–8

What it does: Each extra condition in the WHERE line narrows the group. Combining indirect identifiers can shrink the matches down to one person. This query leaves out names and points. Showing fewer details means more students share the same values, so groups stay bigger. Removing names is not enough. When a mix of details matches only one person, that person can be re-identified. If each combination matches 5 or more people, nobody stands out alone. This is a good sign for privacy. Removing or widening a detail makes more people match, so groups grow. Adding detailed columns makes groups shrink.

Common mistakes: Small groups are the risky ones, because fewer people share your details. The test should be groupSize < 5, so a group of one is marked high risk. yearLevel and house are columns in the students table, not in products. The query should read FROM students to find groups of one.

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

A dataset has no names, but only one student is aged 13, lives in Elmwood and is in Year 8. Is this student safe from re-identification?

Yes, because there is no name in the dataNo, they are a group of one, so someone could work out who they are

Matches

Only the name column is a risk, so keeping age, suburb and year level is safe.

↓

Indirect identifiers such as age, suburb and year level can be combined to match a very small group, even a group of one, so they are risky too.

Applying

Meanings

Term

re-identification

Meaning

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.

Practice

SELECT yearLevel, house, COUNT(*) AS groupSize

Testing

Gaps

SELECT (*)

Check Answer

Spots

3groupSize ← 1

4IF groupSize > 5 THEN

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Quick Questions

1. A dataset has no names, but only one student is aged 13, lives in Elmwood and is in Year 8. Is this student safe from re-identification?

A. No, they are a group of one, so someone could work out who they are ✓

B. Yes, because there is no name in the data

Removing names is not enough. When a mix of details matches only one person, that person can be re-identified.

2. Which group size hides a person best?

A. A group of 1 student who has unique details

B. A group of 30 students who share the same details ✓

Bigger groups hide people better because many people match the same details, so you cannot tell which one is the person.

3. Age alone is a weak identifier, but age + suburb + year level together can point to one person. Which statement is correct?

A. Combining indirect identifiers makes the matching group smaller ✓

B. Combining details always makes the group bigger

Each extra detail splits the crowd into smaller groups. Put enough details together and the group can shrink to just one person.

4. A school survey shows only one Year 7 student from the suburb of Rosehill. What is the best way to lower the risk?

A. Keep the exact suburb and add the student's birth date

B. Widen the detail, such as showing a region instead of the suburb ✓

Widening or removing a detail makes groups bigger. Adding more details does the opposite and makes the person easier to find.

5. Which change makes groups bigger and re-identification harder?

A. Changing an exact age of 13 into the age band 12-14 ✓

B. Changing an age band of 12-14 into the exact age of 13

An age band puts more people in the same group. An exact age splits people into smaller groups.

Match the Pairs

SELECT COUNT(*) FROM students WHERE yearLevel = 8 AND house = 'Blue'; → Counts how many students match a mix of details. If the answer is 1, that person is a group of one and could be re-identified.

IF(B2<=12,"11-12","13-14") → Puts exact ages into wider age bands so more people share the same value and each group is bigger.

Age, suburb and year level may each look harmless, but together they can match only one student. → SELECT COUNT(*) FROM students WHERE yearLevel = 7 AND house = 'Red' AND points = 120;

Removing a detail from a dataset makes the groups bigger and lowers the re-identification risk. → SELECT yearLevel, house FROM students;

Only the name column is a risk, so keeping age, suburb and year level is safe. → Indirect identifiers such as age, suburb and year level can be combined to match a very small group, even a group of one, so they are risky too.

A group of one student is fine because the dataset has no names. → A group of one means the details match just one person, so that person can be re-identified even without a name.

Key Term

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.

Fill in the Blanks

1. SELECT COUNT(*) FROM students WHERE yearLevel = 7;

Hint: Type the function that counts how many rows (people) are in the group.

2. SELECT COUNT(*) FROM students WHERE yearLevel = 8 AND house = 'Blue';

Hint: Type the keyword that makes both details have to match, which shrinks the group.

3. SELECT yearLevel, house, COUNT(*) FROM students GROUP BY yearLevel, house;

Hint: Type the column name that is combined with year level to make the groups.

4. IF groupSize = 1 THEN widen the detail ENDIF

Hint: Type the group size that points to just one person and is the riskiest.

5. SELECT yearLevel, COUNT(*) FROM students GROUP BY yearLevel;

Hint: Type the one column to group by after dropping the other detail, so groups get bigger.

Spot the Error

Question 1

A age ← 13

B suburb ← "Hill Park"

C groupSize ← 1

D IF groupSize > 5 THEN

E risk ← "high"

Line D — Small groups are the risky ones, because fewer people share your details. The test should be groupSize < 5, so a group of one is marked high risk.

Question 2

A SELECT yearLevel, house, COUNT(*)

B FROM products

C GROUP BY yearLevel, house

D HAVING COUNT(*) = 1;

Line B — yearLevel and house are columns in the students table, not in products. The query should read FROM students to find groups of one.

Question 3

A Age 13: 40 students

B Age 13 + suburb: 60 students

C Age+suburb+year: 1 student

Line B — Each extra detail you combine makes the matching group smaller, not bigger. Age 13 plus suburb must be fewer than 40 students, such as 6.

Question 4

A SELECT * FROM students

B WHERE yearLevel = 8

C AND house = 'Red';

Line A — To check the size of a group you need COUNT(*). SELECT * lists every column about each student instead of counting the matches.

Question 5

A age ← 13

B IF age BETWEEN 12 AND 14

C ageBand ← "12-14"

D SHOW age

Line D — The exact age puts the person back in a tiny group. It should show ageBand, the wider range, so more people share the same detail.

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