Chapter 11 — Privacy and data protection: assets and context
Lesson map
Lesson 11 published. Map, example, EX031.
flowchart TD
N0["Learning outcomes"]
N1["11.1 Asset classes"]
N2["11.2 Expectations"]
N3["11.3 Context"]
N4["11.4 Priorities"]
N0 --> N1 --> N2 --> N3 --> N4
N4 --> EX["Worked example"]
EX --> Q["Exercise EX031"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["11.1 Asset classes"]
N2["11.2 Expectations"]
N3["11.3 Context"]
N4["11.4 Priorities"]
N0 --> N1 --> N2 --> N3 --> N4 --> Q["EX031"]
[Learning outcomes]
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v
[11.1 Asset classes]
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v
[11.2 Expectations]
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v
[11.3 Context]
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v
[11.4 Priorities]
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v
[Worked example] --> [Exercise EX031]
Learning outcomes
You will (1) classify personal data assets; (2) understand member expectations; (3) relate context to appropriate use.
11.1 Asset classes
Aisha classifies assets: identity, payment, workout, heart-rate, CCTV and marketing. She rates sensitivity and volume for each. Identity data include names, emails, phones and photos on membership cards. Payment data include bank details and card tokens. Workout data include classes and exercises. Heart-rate data come from straps. CCTV covers entrances and gym floors. Marketing data include preferences and campaign responses. She rates heart-rate and CCTV as most sensitive, and identity and workout data as the largest by volume. Payment data rank third because card tokens are held by the processor.
11.2 Expectations
She surveys twenty volunteers about expectations. Most expect workout data to stay within the app and be deleted when they leave. The volunteers are members who responded to a notice in two clubs. She asks short questions about where data go, who can see them and how long they are kept. Several volunteers are surprised that class screens show heart-rate zones with first names, and two say they stopped wearing straps for that reason. She records their views anonymously. She feeds these views into the risk work.
11.3 Context
Using contextual integrity, Aisha notes that heart-rate data shared for training should not flow to advertisers. Members' expectations match this. Contextual integrity asks whether an information flow matches the norms of the context where data were shared. Members share heart-rate data with trainers to train better. A flow to advertisers serves a different purpose and breaks those norms, even if a notice mentions it. Aisha uses this framework to explain to the app team why some legal flows still feel wrong to members.
11.4 Priorities
She ranks assets by sensitivity and exposure; heart-rate and CCTV come first. Mr. Reilly notes that context helps decide where protection matters most. She combines sensitivity with how widely data are exposed. Heart-rate data are sensitive and reach vendors and screens. CCTV footage is sensitive and accessed by local managers without logging. Payment data are sensitive but already protected by the payment processor. This ranking guides which data flows she examines first in the next chapter and which controls she proposes later.
Worked example
Aisha classifies six asset types, surveys expectations, applies contextual integrity to heart-rate data and ranks heart-rate and CCTV highest.
Synthetic case. The survey is synthetic; Nissenbaum (2004) is real.
Errors and limits
Twenty volunteers are not representative.
Exercises
- EX031 — Asset classes. Name four asset classes. Rubric: 3 = identity, payment, workout, heart-rate, CCTV, marketing;; 2 = three;; 1 = vague;; 0 = none.
- EX032 — Member expectation. What did most volunteers expect about workout data? Rubric: 3 = stays in the app and is deleted on leaving;; 2 = one part;; 1 = vague;; 0 = none.
- EX033 — Top priorities. Which assets rank highest? Rubric: 3 = heart-rate and CCTV;; 2 = one;; 1 = wrong;; 0 = none.
Continue studying — lesson 11 (chapter 11 of 30)
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