Chapter 14 — Privacy foundations | Bachelor's Privacy and data protection

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Chapter 14 — Privacy foundations

Lesson map

Lesson 14 published. Map, example, EX040.

flowchart TD
  N0["Learning outcomes"]
  N1["14.1 Theories"]
  N2["14.2 Principles"]
  N3["14.3 Lawful bases"]
  N4["14.4 Special categories"]
  N5["Synthesis"]
  N0 --> N1 --> N2 --> N3 --> N4 --> N5
  N5 --> EX["Worked example"]
  EX --> Q["Exercise EX040"]

Logical drawing

flowchart LR
  N0["Learning outcomes"]
  N1["14.1 Theories"]
  N2["14.2 Principles"]
  N3["14.3 Lawful bases"]
  N4["14.4 Special categories"]
  N5["Synthesis"]
  N0 --> N1 --> N2 --> N3 --> N4 --> N5 --> Q["EX040"]

[Learning outcomes]
    |
    v
[14.1 Theories]
    |
    v
[14.2 Principles]
    |
    v
[14.3 Lawful bases]
    |
    v
[14.4 Special categories]
    |
    v
[Synthesis]
    |
    v
[Worked example] --> [Exercise EX040]

Learning outcomes

You will (1) explain theories of privacy; (2) apply data protection principles; (3) identify lawful bases for school processing.

14.1 Theories

Elena studies privacy theory. Warren and Brandeis (1890) argued for a right to be let alone. Solove (2006) describes privacy as a family of problems, such as surveillance, aggregation and disclosure. Nissenbaum's contextual integrity holds that information flows should respect the norms of their context. Elena applies contextual integrity to schools: information shared by parents with a teacher about a family situation should not flow to other parents or to app vendors, even if technically possible. Nissenbaum calls this contextual integrity. Norms travel with information.

14.2 Principles

She studies the GDPR principles in Article 5 and finds equivalents in LGPD Article 6, which lists principles such as purpose, adequacy, necessity, transparency, security and prevention. She maps each principle to a school example: purpose limitation means attendance data collected for safeguarding should not be used to rank teachers; minimisation means a lunch app does not need reading levels. The examples make abstract principles concrete for staff training. Purpose limitation prevents attendance data being used for staff performance. Each example comes from trust practice.

14.3 Lawful bases

Elena learns that processing needs a lawful basis under GDPR Article 6. Schools often rely on legal obligation for records required by education law, public task for teaching, and consent for optional activities such as photographs in newsletters. She explains that consent is not always the right basis: if a school needs data to teach, asking for consent suggests that parents could refuse without consequence, which may not be true. Choosing the correct basis makes processing more honest. Public task fits most pupil data.

14.4 Special categories

Health data, religious beliefs and ethnic origin are special categories under Article 9. Schools process them for medical care, dietary needs and equality monitoring. Each needs a condition under Article 9 as well as a basis under Article 6. Elena builds a table of the trust's special category processing with both bases. She finds that equality monitoring data are kept with names when aggregated analysis would suffice, and recommends separating them. Separation reduces exposure without reducing the usefulness of the equality reports. The lead agrees.

Worked example

Elena applies contextual integrity to family information, maps GDPR and LGPD principles to school examples, explains why consent is not always the right basis and finds equality data kept with names.

Synthetic case. The trust's processing is synthetic; Warren and Brandeis (1890), Solove (2006), Nissenbaum (2004), GDPR Articles 5, 6 and 9 and LGPD Article 6 are real.

Errors and limits

Lawful basis choices depend on national education law; check locally.

Exercises

EX040 — Contextual flow. How does contextual integrity apply to family information shared with a teacher? Rubric: 3 = it should not flow to other parents or vendors;; 2 = mentions norms;; 1 = free to share;; 0 = none.
EX041 — Consent misuse. Why is consent wrong for data needed to teach? Rubric: 3 = parents cannot really refuse without consequence;; 2 = mentions choice;; 1 = consent always;; 0 = none.
EX042 — Equality data. What did Elena find about equality monitoring data? Rubric: 3 = kept with names when aggregate analysis would suffice;; 2 = mentions names;; 1 = vague;; 0 = none.

Synthesis

Chapter 14 lays foundations. Chapter 15 measures evidence.

Continue studying — chapter 14 of 40

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