ACARA Digital Technologies Years 7–8 — Digital Footprints and Data Minimisation Hangman

The 56 key terms

1personal data

Information that identifies or relates to a specific person, like their name, birthday, address, photos or location. It needs protecting because it is stored in online accounts.

2digital footprint

The trail of data you leave online from what you do and post, like posts, comments, searches and photos, including data other people share about you.

3active and passive footprints

Active = data you choose to share, like posting a photo. Passive = data collected about you without you typing it, like cookies, location tracking or browsing history.

4online account

A profile on a website or app that stores the personal data you give it, like your name and age. The company may share that data, so only give what is needed.

5cookie

A small file a website saves in your browser to remember you. Tracking cookies record what you do across different sites, often to show you targeted ads.

6metadata

Hidden details stored inside a photo or file, like the time, device and GPS location. It can add to your digital footprint when you share it, so check and remove it first.

7App permissions

Settings where each app asks to use your location, camera, microphone or contacts. You can allow or deny each one, or allow only while using the app, and review them any time.

8digital permanence

Anything you post online can be copied, saved or shared by others, so it may never fully disappear, even if you delete it. Think before you post.

9digital reputation

The impression other people form of you from the data in your digital footprint, such as posts, photos and comments that show up when someone searches your name.

10Privacy

The right to keep your personal information and data safe from others without permission.

11privacy setting

A switch in an app or account that controls who can see your information, like making it private or friends only.

12Manage your footprint

Take action to control what is online about you by searching your own name, deleting old posts and unused accounts, and asking others to remove content.

13server log

A record a web server makes for every visit, such as the IP address, device type, time and pages viewed, even when the visitor types nothing in.

14Form responses sheet

A spreadsheet linked to an online form. Each submitted response becomes a new row, each question is a column, and a timestamp column records when it was sent, giving data ready to use.

15Collect email addresses

A form setting that automatically records every respondent's email address, so answers can be linked to a person. Turn it off to keep a survey anonymous.

16sensor data

Measurements, such as heart rate or steps, that a device like a wearable records automatically at regular intervals. It is quantitative data and can reveal a person's health trends.

17third party

A company other than the one you gave your data to, such as an advertiser or analytics service, that may receive your data. Privacy policies often list them.

18privacy policy

A document that says what data a service collects, why it collects it, who it shares it with, how long it keeps it and how you can opt out.

19purpose and user

Every digital system is made to do a job (its purpose) for certain people (its users). Example: a school library system helps students and teachers borrow books.

20data audit

A table that lists each piece of data a system collects, how it is collected (typed in or automatic) and what it is used for. It shows the system's footprint.

21essential data

Data a system cannot do its purpose without. If the system could still work with it left out, the data is optional and does not need to be collected.

22The system needs this because

A justification names the data item, the system's purpose and gives a because reason why the item is needed or could be removed. Test it by removing the item and checking the system still works.

23data minimisation

Collecting only the data a form really needs. Remove extra questions, mark fields optional and use ranges, like an age range instead of a birth date.

24Required

A switch on a form question. When on, people must answer it. Switch it off for questions you do not really need, so people can skip them and share less personal data.

26data breach

When data a system holds is stolen or leaked. Data you never collect cannot be breached, so the more you keep, the bigger the risk, including identity theft and misuse.

27data retention

Keeping data only as long as its purpose needs it, then deleting it, including old accounts, finished project data and backup copies.

28consent

Asking permission before collecting someone's personal data and explaining how it will be used. The person can say no. For young people, a parent or guardian may also need to agree.

29sensitive information

A special kind of personal information, like health, ethnic origin, religion or biometric data, that the Privacy Act protects more strictly because misuse can cause serious harm.

30Australian Privacy Principles (APPs)

13 legal principles in the Privacy Act 1988 that set rules for how organisations collect, use, store, give access to and correct personal information. Use them as a checklist.

31APP 3

Australian Privacy Principle 3: only collect personal information that is reasonably necessary for your functions, and collect it lawfully and fairly. Take extra care with sensitive information.

32APP 2

Australian Privacy Principle 2: people can choose to stay anonymous or use a pseudonym with an organisation, unless that is impractical or the law requires them to identify themselves.

33CSV file

A plain text file that stores table data with commas between the values. A spreadsheet can open it and show the data in rows and columns.

34data table

A grid that organises data. Each column has one heading, and each row holds one record, like one student or one pet. There are no blank rows.

35filter

A filter shows only the rows that match a value you choose and hides the other rows. The hidden rows are not deleted. Clear the filter to see them all again.

36personal 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.

37direct and indirect identifiers

A direct identifier, like a name or email, points to one person alone. An indirect identifier, like suburb, age or school, only identifies someone when combined with other details.

38Make a copy

Duplicate the original file with Make a copy or Save As, then de-identify the copy. The original stays unchanged, so you never lose your data.

39Delete column

Right-click a column header and choose Delete to remove the whole column and all its data. Undo with Ctrl+Z if it was a mistake, and check formulas that used the column.

40hidden column

A hidden column is only out of sight. Its data stays in the file and is shared with it, and anyone can unhide it. Delete the column to really remove the data.

41anonymise

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

42participant code

A code such as P01 used instead of a name. Names and indirect identifiers like school or suburb are removed, and the key list linking codes to people is stored separately.

43=LEFT(A2,3)

Text functions split text or join it. LEFT, RIGHT and MID take part of a value; CONCAT or & joins parts into one. This formula returns the first 3 characters of cell A2.

44=IF(test, value_if_true, value_if_false)

Checks a condition, like B2>=50, and shows one result if it is true and another if it is false. Text results go in quotes, and you can copy it down a column.

45="P"&TEXT(ROW()-1,"00")

Builds a unique ID code like P01 for each row. Copy it down, paste as values only so the codes stay, then delete the name column.

46generalising

Replacing an exact value with a broader group, like a birth date with an age band, so the data is less identifying but still useful.

47=IF(B2<13,"Under 13",IF(B2<16,"13-15","16+"))

A nested IF formula that replaces an exact age with an age band, so the sheet shows a group like 13-15 instead of the exact number. This keeps less personal data.

48=LEFT(C2,2)

Keeps only the first 2 characters of the postcode in cell C2, so an exact location becomes a wider region. This helps protect privacy.

49re-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.

50fit-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.

51fake test data

Invented but realistic values made by a data generator, so you can build and test a solution without collecting any real person's data.

52primary key

A field whose value is different for every record, such as a student ID or an AutoNumber, so each record can be identified. Names make a poor key because two people can share one.

53SELECT Name, Score FROM Students

A query that lists the fields to show. Only Name and Score appear, in that order, so other columns are left out. SELECT * shows every field.

54UPDATE table SET field = value WHERE condition;

UPDATE changes field values and DELETE FROM removes records. Always add WHERE so only the chosen records change. Without it, every record in the table is changed or deleted.

55privacy by design

Building privacy into a solution from the start: collect minimal data, use anonymous IDs, make settings private by default and plan how data will be deleted.

56privacy check

A review of a data project that asks if you have consent, only collect what you need, hide names, and store the data safely, before you share it.

57finding

A clear statement that names the system, the data collected, whether it is essential and a change to make, joined with 'because' to give the reason.