Chapter 9 — Privacy and data protection: tools and methods
Lesson map
Lesson 09 published. Map, example, EX033.
flowchart TD
N0["Learning outcomes"]
N1["9.1 Choosing tools"]
N2["9.2 Methods before tools"]
N3["9.3 Repeatability"]
N4["9.4 Responsible use"]
N0 --> N1 --> N2 --> N3 --> N4
N4 --> EX["Worked example"]
EX --> Q["Exercise EX033"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["9.1 Choosing tools"]
N2["9.2 Methods before tools"]
N3["9.3 Repeatability"]
N4["9.4 Responsible use"]
N0 --> N1 --> N2 --> N3 --> N4 --> Q["EX033"]
[Learning outcomes]
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v
[9.1 Choosing tools]
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v
[9.2 Methods before tools]
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v
[9.3 Repeatability]
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v
[9.4 Responsible use]
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v
[Worked example] --> [Exercise EX033]
Learning outcomes
You will (1) choose tools for privacy checks; (2) use standard-library scripts responsibly; (3) document methods for repeatability.
9.1 Choosing tools
Leila needs tools to search exports, analyse logs, check cookies and review audit trails. Mr. Okafor sets a rule for the course: use built-in system features and Python's standard library before adding new software, because every new tool needs approval, updates and a check of what data it sends elsewhere. Some commercial scanners upload files to the vendor's cloud for analysis, which would itself be a disclosure of patient data. Leila adds a question to her tool checklist: where does this tool send the data it reads?
9.2 Methods before tools
Leila writes a method for each check before choosing a tool: the question, the data source, the steps, the expected output and how to interpret it. For example, the method for checking log retention asks: what is the oldest log file in each location, and is it within thirty days? The tool is then a short script or a system command that answers that question. She learns that a clear method makes it easier to explain results and to swap tools later without changing the conclusion.
9.3 Repeatability
Each method is saved with its script, the synthetic test data and the expected results. Another student can run the same method and compare outputs. Leila uses fixed random seeds in synthetic data generators so results are identical across runs. When a result changes unexpectedly, the saved expected output shows immediately that something in the system or the script has changed. Mr. Okafor explains that repeatable checks turn privacy controls into something that can be tested like any other technical requirement. Expected outputs are kept under version control.
9.4 Responsible use
Tools that read personal data are themselves processing. Leila's scripts run only on the restricted analysis computer, read only the exports named in the scope and produce counts or masked outputs. She records each run in a log with the date, method, input file name and result summary. When she finishes a check, she deletes working copies of exports. She explains that privacy tools should be held to the same standards as the systems they examine, otherwise they become a new source of risk.
Worked example
Leila prefers built-in features and the standard library, asks where each tool sends data, writes methods before choosing tools, saves expected outputs with fixed seeds and logs every run.
Synthetic case. Tools and methods are synthetic.
Errors and limits
Built-in tools have limits; document what a method cannot detect.
Exercises
- EX033 — Tool question. What question did Leila add to her tool checklist? Rubric: 3 = where does the tool send the data it reads;; 2 = partly correct;; 1 = vague;; 0 = none.
- EX034 — Method parts. What does each method define? Rubric: 3 = question, source, steps, expected output, interpretation;; 2 = most;; 1 = few;; 0 = none.
- EX035 — Fixed seeds. Why use fixed random seeds? Rubric: 3 = identical synthetic data across runs;; 2 = mentions repeatability;; 1 = vague;; 0 = none.
- EX036 — Tools as processing. Why are privacy tools themselves a risk? Rubric: 3 = they process personal data too;; 2 = mentions processing;; 1 = vague;; 0 = none.
Continue studying — lesson 09 (chapter 09 of 18)
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