← ACARA Digital Technologies Years 7–8

Spotting personal data columns in a dataset

A dataset column, such as name, email, phone, address or comments, that identifies or describes a person. Check each heading and its sample values to spot it.

Part of: ACARA Digital Technologies Years 7–8 — Digital Footprints and Data Minimisation — Module 36

Understanding Spotting personal data columns in a dataset in ACARA Digital Technologies Years 7–8

What it does: Even a dataset anyone can download may include columns like phone numbers, which identify real people. Listing each column and marking it essential or not helps you spot the personal data columns you could remove. Free-text columns can contain names, schools, addresses and other details, so they need checking. Public datasets can still contain personal data. Being public does not make an address stop identifying a person. List the columns first, then check each heading and its sample values before you decide what to mark. An email address is linked to one person. Rainfall readings describe the weather, not a person.

Common mistakes: Rainfall is weather data and does not identify a person. The personal list should hold columns like "name" and "email". With rainfall in the list, the email column is never flagged. The function should return True when the heading is in the list of personal words. Using 'not in' flags the wrong columns. It should be: return col in words.

Class: _____________________ · Name: _____________________ · Date: _________

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This module's key terms are also in a printable crossword (Modules 728–751) and word search (Modules 696–770).

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Learning

Duo

A dataset has the columns FullName, Email, Phone and FavouriteColour. Which column is NOT a personal data column?

Email (it does not identify anyone)FavouriteColour (it does not identify who the person is on its own)

Matches

Only the heading 'Name' was checked, so 'Notes' was treated as safe without reading its values

↓

The sample values were not read. A column with a harmless heading, like Notes, can still hold personal details.

Applying

Meanings

Term

personal data column

Meaning

A dataset column, such as name, email, phone, address or comments, that identifies or describes a person. Check each heading and its sample values to spot it.

Practice

name: personal data

Testing

Gaps

SELECT , lastName FROM students;

Check Answer

Spots

1personal = ["name", "rainfall"]

2col = "email"

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Quick Questions

1. A dataset has the columns FullName, Email, Phone and FavouriteColour. Which column is NOT a personal data column?

A. FavouriteColour (it does not identify who the person is on its own) ✓

B. Email (it does not identify anyone)

Name, email and phone columns point to a real person. A favourite colour alone does not usually identify someone, but always check the sample values too.

2. A column is headed Contact. The sample values are 0412 555 019 and 0433 555 872. What should you do?

A. Treat it as a personal data column because the values are phone numbers ✓

B. Ignore it because the heading does not say 'Phone'

Headings can be vague. Look at the sample values as well as the heading to spot personal data.

3. A column called Comments holds free text like 'Hi, I'm Mia from Year 8 at Lakeside High'. How should it be treated?

A. As a possible personal data column, because people can type identifying details into comments ✓

B. As safe, because comments are only opinions

Free-text columns can contain names, schools, addresses and other details, so they need checking.

4. A dataset downloaded from a public website has a column named HomeAddress. Which statement is correct?

A. It is not personal data because anyone can download it

B. It still contains personal data, even though the dataset is public ✓

Public datasets can still contain personal data. Being public does not make an address stop identifying a person.

5. You are checking a new dataset for personal data. Which step comes first?

A. Delete the columns that look the longest

B. List every column heading, then mark each one as personal data or not ✓

List the columns first, then check each heading and its sample values before you decide what to mark.

Match the Pairs

Column heading: email, sample value: ava.n@mail.com → A personal data column because each value points to one real person

Column heading: comments, sample value: "I walk home past the park alone" → A free-text column that can reveal personal details about a person

A public dataset can still contain personal data, so check its columns before using it → Column heading: phone, sample value: 0412 345 678

List every column, then mark each one essential or not essential for your question → name: not essential | suburb: not essential | rainfall: essential

Only the heading 'Name' was checked, so 'Notes' was treated as safe without reading its values → The sample values were not read. A column with a harmless heading, like Notes, can still hold personal details.

Column 'home_address' kept because the dataset came from a public website → Public data can still contain personal data. A home address identifies a person, so it should be marked as a personal data column.

Key Term

personal data column

A dataset column, such as name, email, phone, address or comments, that identifies or describes a person. Check each heading and its sample values to spot it.

Fill in the Blanks

1. SELECT firstName, lastName FROM students;

Hint: Type the column heading that holds each student's given name, a personal data column.

2. SELECT * FROM students LIMIT 3;

Hint: Type the keyword that shows only the first few rows so you can check sample values.

3. SELECT yearLevel, house FROM students;

Hint: Type the keyword that chooses which columns to show, leaving out the columns that identify people.

4. column ← "comments" personal ← TRUE

Hint: Type the Boolean value that flags a free-text column, because comments can describe or identify a person.

5. IF column = "name" OR column = "email" THEN personal ← TRUE END IF

Hint: Type the logical operator that is true when either heading matches.

Spot the Error

Question 1

A personal = ["name", "rainfall"]

B col = "email"

C if col in personal:

D print("Personal data")

Line A — Rainfall is weather data and does not identify a person. The personal list should hold columns like "name" and "email". With rainfall in the list, the email column is never flagged.

Question 2

A def is_personal(col):

B words = ["name", "email"]

C col = col.lower()

D return col not in words

Line D — The function should return True when the heading is in the list of personal words. Using 'not in' flags the wrong columns. It should be: return col in words.

Question 3

A heading = "Home address"

B sample = "12 Oak St, Perth"

C personal = False

D print(heading, personal)

Line C — A home address column and its sample values point to where a person lives, so it is a personal data column. It should be personal = True.

Question 4

A column = "Comments"

B sample = "Hi, I'm Sam from 8B"

C if "Sam" in column:

D print("Personal data")

Line C — A name hidden in a comment shows up in the sample values, not in the heading. The check should look in sample: if "Sam" in sample.

Question 5

A source = "public"

B has_email = True

C safe = source == "public"

D print("Safe to share:", safe)

Line C — A public dataset can still contain personal data. Safety depends on whether columns like email are present, not on where the data came from. It should be: safe = not has_email.

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