Anonymising data

Name:

Date:

Class:

Quick Questions

Shade in the A or B circle for each question.

1. Which is a good way to anonymise a class survey?

A Remove the Name column before sharing

B Keep the Name column but make the text smaller

2. Which one replaces a student's name with an ID number?

A Mia scored 8 out of 10

B Student 12 scored 8 out of 10

3. Anonymised data means...

A People in the data cannot be recognised

B The data has been deleted completely

4. Which way of showing ages is better for keeping people anonymous?

A Age groups like 10-11 and 12-13

B Exact birthdays like 4 May 2013

5. A table has no names, but it lists 'Year 6 student who lives at 5 Wattle St'. What is the problem?

A There is no problem because the names are gone

B The address is a hidden identifier that could show who it is

Match the Pairs

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

Left column		Right column
1Student ID: S01		AA grouped age that hides the exact age so a person is harder to identify
2Age group: 10-11		BAn ID number used in place of a student's name so the person cannot be recognised
3Anonymise a table by removing the column that shows who each person is		CCheck: street, birthday, class
4Check for hidden identifiers that could still show who someone is, like a rare detail		DThe ID still contains the person's name, so Mia can still be recognised. Use a random ID like S01 instead.
5Name column removed, but Street: 5 Oak Rd is still in the table		EA hidden identifier is left in. The address can still show who the person is, so the data is not anonymous.
6Name: Mia Lee changed to ID: Mia1		FDelete column: Name

Definitions

anonymise

Fill in the Blanks

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

_________(survey_data)

_____________(survey_data, "Name")

_______________(survey_data, "Name")

if age __ 10 and age <= 11: age_group = "10-11"

___ column in survey_data: check_hidden_identifiers(column)

Spot the Error

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

Question 1

A data = load("survey.csv")

B remove_column(data, "Name")

C add_ids(data)

D keep_column(data, "Address")

E save(data, "safe.csv")

Question 2

A ADD "Name" TO table

B FOR EACH row IN table

C SET row.id TO next_number()

Question 3

A if age == 11:

B group = "10-12"

C else:

D group = "other"

Question 4

A names = ["Mia", "Leo", "Zoe"]

B ids = []

C for i in range(len(names)):

D ids.append(names[i])

E print(ids)

Question 5

A hidden = ["Phone", "Email"]

B for col in hidden:

C remove(data, hidden)

D print("Checked")

Answer Key

Anonymising data

Section A: Quick Questions

A — Remove the Name column before sharing

B — Student 12 scored 8 out of 10

A — People in the data cannot be recognised

A — Age groups like 10-11 and 12-13

B — The address is a hidden identifier that could show who it is

Section B: Match the Pairs

Student ID: S01 → An ID number used in place of a student's name so the person cannot be recognised

Age group: 10-11 → A grouped age that hides the exact age so a person is harder to identify

Anonymise a table by removing the column that shows who each person is → Delete column: Name

Check for hidden identifiers that could still show who someone is, like a rare detail → Check: street, birthday, class

Name column removed, but Street: 5 Oak Rd is still in the table → A hidden identifier is left in. The address can still show who the person is, so the data is not anonymous.

Name: Mia Lee changed to ID: Mia1 → The ID still contains the person's name, so Mia can still be recognised. Use a random ID like S01 instead.

Section C: Definitions

anonymise — To remove or replace names and other identifying details in data, like using ID numbers, so people cannot be recognised.

Section D: Fill in the Blanks

anonymise(survey_data)

remove_column(survey_data, "Name")

replace_with_id(survey_data, "Name")

if age >= 10 and age <= 11:
    age_group = "10-11"

for column in survey_data:
    check_hidden_identifiers(column)

Section E: Spot the Error

D — An address can identify a person, so it must be removed, not kept. The line should be remove_column(data, "Address").

A — To anonymise data you take names out, not add them. The line should be REMOVE "Name" FROM table.

A — Grouping ages means using a range, like 10 to 12. Checking one exact age does not group people, so they could still be recognised. It should be if age >= 10 and age <= 12:

D — This adds each person's name to the ID list, so the names are still there. It should add an ID number: ids.append(i + 1).

C — The loop should remove one column at a time using the loop variable col. It should be remove(data, col).