CSE 163

Data & Privacy�

Suh Young Choi��🎶 Listening to: Unwed Sailor�💬 Before Class: What’s your favorite emoticon?

Announcements

THA 5 Peer Reviews due tonight!

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Checkpoint 5 due on Monday (3/9) !

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Final course evals open!

• Current response rate is 24%

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Project due dates

• Report/Code due Monday, 3/9
• Presentations in section on Thursday, 3/12
• Peer Feedback due Sunday, 3/15

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This Time

• Privacy
• Anonymity
• Differential Privacy

Last Time

• Defining Fairness
• Quantifying error

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Anonymous Data Isn’t

Mid 1990s, insurance group in Massachusetts published anonymous records of hospital visits with attributes like name, address, social security removed but left in demographic information.

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Turns out this data release was not so anonymous!

• Latanya Sweeney was able to link demographic information in hospital data with voter rolls. Found which hospital record corresponded to governor.

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Sweeney estimates 87% of the US is uniquely identified by knowing 1) date of birth, 2) sex, and 3) zip code.

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k-anonymity

K-anonymity: A first definition of privacy by Sweeney that requires every query results in at least k people in the dataset.

• Achieved by removing columns or fuzzing values

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Weakness: Fails under composition

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Differential Privacy

A stronger notion of privacy that guarantees how much information you can learn about a person.

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Consider two worlds, one where A participates in a study and one where they don’t. If results of the study are similar, we say it respects differential privacy.

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Differential Privacy

Say an algorithm or analysis is 𝜀-differentially private if results with or without any single person in the dataset are “at most 𝜀” off.

• Defining how close results are is a little complex, but is a statement of probabilities
• If 𝜀 = 0, require results to be exactly the same
• If 𝜀 is small, require results to be very similar
• If 𝜀 is large, require more deviation in results (less privacy)

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Two methods for commonly achieving 𝜀-differential privacy

• Jittering Result (Laplace Mechanism)
• Randomized Response

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Jittering

Take result of analysis and add a small amount of random noise to result.

• Example: Report average age of census but add a small random number to it

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Specifically if you add noise that follows a Laplace distribution with parameter 𝜀, you can achieve 𝜀-differential privacy.

• See below for 𝜀=0.5 (red dashes), 𝜀=1 (blue solid), 𝜀=2 (purple dots)

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Randomized Response

What if we don’t trust the data collector with our data?

• Even with differentially private statistics published, they still have access to the raw data. What if they get hacked?

�Change the differential privacy mechanism to be done locally rather than centrally!

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Differentially Private Polling Procedure:

• Call up person. Ask them to flip a coin (don’t tell us result)
• If Heads, tell us their honest answer to question (“Yes”/”No”)
• If Tails, flip coin again
• If Heads, report “Yes”
• If Tails, report “No”

�Key idea: Can learn aggregate trends without knowing true result of individual

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Randomized Response Analysis

Key property: People tell the truth ¾ of the time and lie ¼ of the time. ½ of the time they are honest, and then half of the time they tell us a random answer that lines up with the truth.

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To see why this work, suppose we know the answer is “Yes” for ⅓ of people. How many “Yes” responses would we expect in this procedure?

• ⅓ of the population has true answer “Yes”. ¾ of them will tell us the truth so we will get a total of ¼ of responses being honest “Yes”es
• ⅔ of the population has the true answer “No”, but ¼ of the time they will randomly tell us “Yes”. This means we would expect ⅙ of the population to lie and tell us “Yes”
• Total of “Yes” received (on average): ¼ + ⅙ = 5/12

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In general, work backwards to solve for underlying probability

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Case Studies

With the people around you, choose the COVID-19 or the “Rides of Glory” case study from the lesson.

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Then, create a slide in the Google Slides document at tinyurl.com/cse163wi26-ethics2

and share your responses to the “Food for Thought” questions in the lesson!

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Other discussion questions to help you get started:

• Both case studies involve the tracking of location data. What other kinds of data would you be concerned about regarding privacy?
• Both case studies report group patterns and aggregates rather than individuals. Does this change how you view the privacy concerns?

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Before Next Time

• Read through Lesson 24 and participate in the discussion
• Fill out course evals
• Go to section tomorrow
• Complete THA 5 Peer Review!
• Keep working on projects and portfolios!

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Next Time

• Ethics case studies
• Come to class!

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