Spotting personal data columns in a dataset

Name:

Date:

Class:

Quick Questions

Shade in the A or B circle for each question.

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)

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'

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

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

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

Match the Pairs

Draw a line connecting each item on the left to its match on the right.

Left column		Right column
1Column heading: email, sample value: ava.n@mail.com		AA personal data column because each value points to one real person
2Column heading: comments, sample value: "I walk home past the park alone"		BColumn heading: phone, sample value: 0412 345 678
3A public dataset can still contain personal data, so check its columns before using it		CA free-text column that can reveal personal details about a person
4List every column, then mark each one essential or not essential for your question		Dname: not essential \| suburb: not essential \| rainfall: essential
5Only the heading 'Name' was checked, so 'Notes' was treated as safe without reading its values		EThe sample values were not read. A column with a harmless heading, like Notes, can still hold personal details.
6Column 'home_address' kept because the dataset came from a public website		FPublic data can still contain personal data. A home address identifies a person, so it should be marked as a personal data column.

Definitions

personal data column

Fill in the Blanks

Complete each line of code by filling in the missing parts.

SELECT _________, lastName FROM students;

SELECT * FROM students _____ 3;

______ yearLevel, house FROM students;

column ← "comments" personal ← ____

IF column = "name" __ column = "email" THEN personal ← TRUE END IF

Spot the Error

Shade in the letter of the line that contains an error.

Question 1

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

B col = "email"

C if col in personal:

D print("Personal data")

Question 2

A def is_personal(col):

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

C col = col.lower()

D return col not in words

Question 3

A heading = "Home address"

B sample = "12 Oak St, Perth"

C personal = False

D print(heading, personal)

Question 4

A column = "Comments"

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

C if "Sam" in column:

D print("Personal data")

Question 5

A source = "public"

B has_email = True

C safe = source == "public"

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

Answer Key

Spotting personal data columns in a dataset

Section A: Quick Questions

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

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

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

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

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

Section B: 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.

Section C: Definitions

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.

Section D: Fill in the Blanks

SELECT firstName, lastName FROM students;

SELECT * FROM students LIMIT 3;

SELECT yearLevel, house FROM students;

column ← "comments"
personal ← TRUE

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

Section E: Spot the Error

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.

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.

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.

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.

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.