Computer Science
Grade 11
20 min
Privacy in the Digital Age
Privacy in the Digital Age
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1
Introduction & Learning Objectives
Learning Objectives
Analyze a software system's data collection practices using established ethical frameworks.
Differentiate between key privacy concepts such as anonymity, pseudonymity, and de-identification.
Evaluate the societal impact of large-scale data aggregation and algorithmic decision-making.
Apply the principle of 'Privacy by Design' to a given system specification.
Articulate the trade-offs between data utility and individual privacy in the context of machine learning models.
Identify potential sources of bias in datasets and explain how they can lead to privacy harms.
Ever wonder how your phone knows you want to buy new running shoes before you've even searched for them? 🤔 Let's explore the ethics behind the code that powers our world....
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Key Concepts & Vocabulary
TermDefinitionExample
Personally Identifiable Information (PII)Any data that can be used to identify a specific individual. This can be direct (like a name or email) or indirect when combined with other information (like date of birth and zip code).A database table storing user profiles contains columns for `full_name`, `email_address`, and `ip_address`. All three are considered PII.
Data MinimizationA principle stating that only the data absolutely necessary for a specific, legitimate purpose should be collected and processed.A weather app asking for your location to provide a forecast is practicing data minimization. If it also demanded access to your contacts list, it would be violating this principle.
Anonymization vs. PseudonymizationAnonymization is the process of removing PII so th...
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Core Syntax & Patterns
Privacy by Design (PbD)
Proactive not Reactive; Privacy as the Default Setting; Privacy Embedded into Design; Full Functionality (Positive-Sum, not Zero-Sum); End-to-End Security; Visibility and Transparency; Respect for User Privacy.
This is a framework for system engineering that treats privacy as a core requirement from the very start of a project, not an afterthought. When designing a new feature, ask 'How can we build this in the most privacy-preserving way?' at every stage.
The Principle of Least Privilege (PoLP)
A subject (user, process, program) should only have the minimum set of privileges or access rights necessary to perform its stated task.
In algorithm and system design, this means a function or service should only access the data it absolutely needs....
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Challenging
You are tasked with designing a system for a city's public transit authority to analyze commuter flow without tracking individuals. Which combination of techniques from the tutorial would be most effective in achieving this goal while maximizing privacy?
A.Collect PII like names and phone numbers but apply Differential Privacy to all database queries.
B.Use Pseudonymization to track individual riders over time to understand their specific travel patterns.
C.Implement a system based on Deontology, refusing to collect any data at all.
D.Employ Data Minimization by collecting only entry/exit points and times (no PII), use Anonymization by stripping device IDs, and apply Differential Privacy for aggregate reports.
Challenging
A tech company defends its practice of collecting vast amounts of user data by arguing, 'The data allows us to provide free services and personalized ads, which brings convenience and economic value to millions of users. This collective benefit outweighs the minimal privacy risk to any single user.' What is the most significant flaw in this utilitarian argument?
A.The argument is deontological, not utilitarian.
B.The argument assumes that economic value is the only 'good' to be considered.
C.The argument fails to adequately weigh the severe, long-term harms of data aggregation, such as manipulation, discrimination, and the chilling effect on speech, which are difficult to quantify.
D.The argument is flawed because data collection is always illegal without explicit consent.
Challenging
An algorithmic system is used to determine eligibility for social welfare benefits. It is later found that the system has a higher rate of denying benefits to individuals from low-income zip codes, partly because the training data associated those zip codes with higher fraud risk. From a societal impact perspective, this system's primary ethical failure is that it:
A.Violates the Principle of Least Privilege for the social workers using it.
B.Creates a system of algorithmic discrimination that reinforces existing societal inequalities, causing significant allocative and dignitary harm.
C.Fails to be 'Proactive not Reactive' as defined by Privacy by Design.
D.Uses pseudonymized data instead of fully anonymized data, which would have solved the bias problem.
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