valuating privacy
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Valuating Privacy. Eytan Adar (with Bernardo Huberman and Leslie Fine) WEIS’05, June 3, 2005. How this started…. Well duh… SHOCK Social Harvesting of Community Knowledge Locally built profile, used for targeting messages Users demanded more control Add and remove profile components - PowerPoint PPT PresentationTRANSCRIPT
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Valuating PrivacyEytan Adar(with Bernardo Huberman and Leslie Fine)
WEIS’05, June 3, 2005
page 2April 22, 2023
How this started…
• Well duh…• SHOCK
– Social Harvesting of Community Knowledge– Locally built profile, used for targeting messages– Users demanded more control
• Add and remove profile components• But, they never touched the feature
• What people say versus what they do
page 3April 22, 2023
Before I go any further…
• For those of you who haven’t read the paper yet:– How much would I have to pay you for your
weight?– Your age?
page 4April 22, 2023
The Goals: What people say and do
• Track what people say, tie it to what they really do
• Who cares about privacy?• How much do they care?
– Can we really figure out how much information is worth?
• Why do they care?– Can we figure out why people price information in
a certain way?
page 5April 22, 2023
Related Work
• Survey/modeling based techniques– Jourard self-disclosure test (Jourard, Cozby, Dindia) – Petronio, 2000– Acquisti and Grossklags (various)– Hann et. al. 2003 (Internet survey)– Jupiter, 2002: 70% consider privacy important– Wolfgang, et. al. “Exploration of Attitudes via Physical
Interpersonal Distance Towards the Obese, Drug Users, Homosexuals, Police, and Other Marginal Figures.” (1971)
– Many more (see paper)• But, surveys don’t tell you what people do
• Just what they think they’ll do• Usually just have a reward and no cost
• Here’s $n for doing the survey
page 6April 22, 2023
Related Work
• Data based studies– Give me your data I’ll give you a cookie– Hard to find good data– Not verified/verifiable– Too many confounders – confuse the why
• Self selecting• Did they have something happen to them before?• Did something happen to their friends?• Did they see something on TV?
page 7April 22, 2023
Our Approach
• Remove as many confounders as possible• Introduce reward and cost
– Privacy calculus • Houston et al. 1987, Altman et al. 1973, etc.• Reveal? reward – cost > 0
– Pay for information– Force revelation of that information
• Adapt approach from behavioral economics/psychology to find where reward really = cost for individuals
page 8April 22, 2023
Our Approach
• Want to study the “why”– Hypothesis: Further people are from mean, the
more they will demand for private information• Use real valued information with notion of relativity
– Not SSN, CC#, etc. these are binary (more or less)– Instead: Weight, Age, GPA, Salary, etc.
– Original title: Privacy and Deviance
page 9April 22, 2023
Deviation Hypothesis
Continuous private information
Mean
page 10April 22, 2023
Deviation Hypothesis
Continuous private information
Price
Mean
page 11April 22, 2023
Our Approach
• The Why:– Hypothesis: Further people are from mean, the
more they will demand for private information• Use real valued information with notion of relativity
– Not SSN, CC#, etc. these are binary (more or less)– Instead: Weight, Age, GPA, Salary, etc.
– Original title: Privacy and Deviance• Not quite right
page 12April 22, 2023
The Experiment
• Groups of 10 - 15– $25 for showing up
• Put them in the same room (around a table)• Perform
– Reverse, – second price,– sealed bid auction
…. $5 $3 $2 $1 $.01Winner
Paid
page 13April 22, 2023
The Experiment
• Groups of 10 - 15– $25 for showing up
• Put them in the same room (around a table)• Perform
– Reverse, – second price,– sealed bid auction
• Data: Weight, Age, GPA • Winner stands up and reveals information and
receives payment (range $0-$100, infinity)– Information is validated (scale, driver license, login)
• Survey
page 14April 22, 2023
The Experiment
ID: 8
2828
28 ID: 8282828Weight: Height:Price: Gender:
ID: 8
2828
28 ID: 8282828Age: Gender:Price:
Only have 1 winner per session, but 10+ data points
page 15April 22, 2023
Survey Questions
• Also coded with ID• Sanity check:
– If you are reluctant to reveal information in this study, you should:• A) List a very low price• B) List a very high price• C) Leave the price blank
• General privacy attitude (how important is privacy to you?)
• Rank different kinds of information (financial, medical, etc.) by importance.
page 16April 22, 2023
Survey Questions
• Weight– Do you feel very underweight, slightly
underweight, average, slightly overweight, overweight given others in the room
– What do you think the average weight is? (your gender)
– Do you think the average (for your gender) is very underweight, slightly underweight, average, slightly overweight, overweight given others in the room
• Same for age• Familiarity with others in the room
– How many do you know well, recognize, etc.
page 17April 22, 2023
Survey Questions (Simulated Exp)
TOPIC Very low Low Medium High Very high
Demand price to reveal in
this room
Credit Rating
Savings
Salary
Spouse’s Salary
page 18April 22, 2023
The Subjects
• 127 subjects• 59% male• 10 sessions• Recruited from HP and from an area
experimental economics mailing list (mostly Stanford students)
• Allowed to leave at any point• Signed a waiver
• Sessions were mixed, male only, female only, male proctor, female proctor
page 19April 22, 2023
The Good Stuff…
• First of all (mea culpa)– GPA was too hard
• Hard to prove• Stanford students
– Pretty smart anyway– Grade inflation?– Didn’t really care
• Weight (actually BMI=function(weight,height))– Best example– Only 7 individuals demanded “infinity”
• 6 were women
page 20April 22, 2023
Results: Log Price versus BMI
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
20th 40th 60th 80th 100thBMI Percentile
Log
of P
rice
Bid
p-value = 0.018
page 21April 22, 2023
The Good Stuff…
• Encouraging for deviance argument• But…
– Recall perception question• Do you feel very underweight, slightly underweight,
average, slightly overweight, overweight given others in the room?
page 22April 22, 2023
Results: Perceived Weight versus Price
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Very Underweight Somewhat Underweight Average Somewhat Overweight
Log
of P
rice
Bid
p-value = 0.0038
• People who are very underweight don’t consider themselves to be that
page 23April 22, 2023
Deviance
• So deviation from “mean” is not quite right• Something underlying
– Desirable versus undesirable– Self-perception
• Consistent with sociological work– Goffman (self-representation, stigmas)– Simmel– See paper for more.
page 24April 22, 2023
The Good Stuff… Age
P=0.17
20th 40th 60th 80th 100th
3
0
Log Price, age groups binning, 88 participants
• Not so encouraging... •Maybe students don’t care about privacy?
•$57 vs $74• But… lowest bucket ($3.62) versus highest ($18.05), p=.0297• Might mean that middle age groups don’t care…
page 25April 22, 2023
What about gender?
• Suggestive, but weak stats– Take these with a grain of salt
• Mixed sessions versus single sex– Men have slightly higher prices for single sex
sessions• p = 0.24
– Women have slightly higher prices in mixed sessions• p = 0.39
– In general, we can’t tell the difference • mixed versus all women versus all men• p = 1
page 26April 22, 2023
Gender Differences – Men Log Price vs BMI
20% 40% 60% 80% 100%
3
1.5
0p-value=.01
page 27April 22, 2023
Gender Differences – Women Log Price vs BMI
3
1.5
020% 40% 60% 80% 100%
• Top 50% versus bottom, clearer– p-value = .16
p-value=.42
page 28April 22, 2023
Gender Differences – Men log price
3
1.5
0Very Under
Somewhat UnderAverage Somewhat Over
page 29April 22, 2023
Gender differences – Women, log price
3
1.5
0Somewhat UnderAverage Somewhat Over
•p-value = .2•Just somewhat over, and somewhat under, p-value = .099•Lesson: women demand more across most categories
•need more than $100?
page 30April 22, 2023
Friends versus strangers in the room (BMI)
• Coded up friendship with scores– Know well = 4, – Acquainted = 3, etc.
• More people you know, the more you charge– Not so strong, p = .34
• Looking at top 50% and bottom much stronger– 36% versus 23%– p = .05
• “Phenomena of the stranger” (Simmel)
3
1.5
0
20% 40% 60% 80% 100%
page 31April 22, 2023
Salary, credit rating, savings
• Much more sensitive• Hard to say something concrete
– If we did it again: • should have expressed no framing limits
Salary Spouse Salary
Credit Rating
Savings
Participants
77 52 78 77
Want > $100
48% 36% 24% 38%
page 32April 22, 2023
Price versus Privacy Attitude
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Critical Very Important Somewhat Important Not Important
Log
of P
rice
Bid
• Not too bad, p-value = .054• Should be taken with a grain of salt
– Question asked after auction– Subjects may have been matching answer to their behavior (priming)
page 33April 22, 2023
Observations (if you want to try this)
• Auction is always cheap… – max payment was <$1 – Usually a few pennies
• Normalization is important– Weight versus BMI– Year on job, occupation, etc.– Critical to know who subjects are comparing themselves
against• Lots of ways to cut up the data
– Worry about data dilution– Fix more variables
• Cultural issues?– Would love to see this study in some other country
page 34April 22, 2023
Summary and Afterwards…
• Well duh…– We didn’t set out to prove the obvious… – Demonstrate a technique that yields actual numbers
and removes confounders– Experiments with real-valued data demonstrate
something about certain kinds of private information• Those influenced by group norms/attitudes
• Possibly important in the design of survey based studies– Results indicate correct amount to offer
• Offering less means you get a biased sample• Raises new privacy issues?
– Maybe possible to reverse– Given that you want $x, probability is you weight y
page 35April 22, 2023
Summary and Afterwards…
• If you write a paper on weight you will end up on pro-ana (anorexia) blogs
• If you write a paper like “Privacy and Deviance” your work will appear on fetish websites
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