0
Confessions (and Lessons) of a
"Recovering" Data Broker:
Responsible Innovation in the Age of Big Data, Big Brother, and
t...
About Metanautix
• Mission: Next generation big data management & analysis
– Transparency into the big data supply chain w...
I am not an attorney.Confession
Dork
Nerd
Geek Dweeb
You can often do more from the inside than
the outside.
• 20B public records
• 30M visitors per month
• 50M+ reports sold
...
Listen to your toughest criticsLesson
Can I have a little narcissism with my
voyeurism?
• What does my background
check say?
• Privacy controls
– Suppress singl...
Data from Feb 2013
Data from
40,000 BC thru 2002
20
Exabytes
(20M TB)
Lots of data created, transferred, & stored.Confessi...
The “Public” Data Supply Chain of YouConfession
Search
Blogs
Criminal
Records
Risk
Civil
Suits Marketing
Addresses
Directo...
FIND OWNER OF DOG’S
RELATIVE FOR TRANSPLANT
SINGLES CURIOUS ABOUT THE
PEOPLE THEY MEET
PARENTS ENSURING WHO
THEIR KIDS SAF...
Billions of Records
Millions of People
Jim Adler
Houston, TX
Age 70
Jim Adler
Redmond, WA
Age 50
Jim Adler
Denver, CO
Age ...
Opt-out doesn’t always mean deletionConfession
Jane Hampton 06/23/1998 123 Main Peoria, IL
Jane Hampton 123 Main Peoria, I...
When towns were small, personal
anonymity was low …
“The only thing
worse than being
talked about, is
not being talked
abo...
“Good Fences
Make Good
Neighbors”
− Robert Frost
Urban populations grew along with
personal anonymity…
Lesson
0
20
40
60
80
100
120
1850 1890 1930 1970 2010
PrivacyExpectations
“Rockwell” Era “Good Fences” Era “Privacy
Vertigo”
Era...
Privacy is deeply cultural.Lesson
Discretion
Disclosure
EU “Right of Personality”
• Inviolable right of dignity
• Germany: BGB, 1900, post WWII
US: Brandeis & Warren, 1890
• Germ...
How to unpack Privacy? Think PPP.
PERILS
Lesson
Sometimes you’re in a public place when
you think you’re in a private place.
“Gaydar”
A 2009 MIT study found it was
possib...
“To Serve Man” is a Cookbook.Confession
“If you’re not
paying for the
product, you are
the product.”
− Claire Wolfe
(parap...
Peer to Peer Corporation &
Customer/Employee
Government &
Citizen
Your God & You
PrivacyRights
Power Disparity 
In priva...
Target knows you’re pregnant and when
you’re due. So, what’s so perilous?
Confession
Secrets are power equalizers.Confession
DPower µ
1
Trust
µSecrets
Public Places
Powerful Players
Private Places
Powerful Players
Private Places
Weaker Players
Public Places
Weaker Players
...
M O R E P R I VAT E P L A C E S
MOREPLAYERPOWERGAP
News of the World
phone hacking
Rutgers student
commits suicide
after s...
A head in the clouds < 20 yearsPrediction
$27,100
$13,500
$6,800
$3,400
$1,700
$850
$420
$210
$100
$53
$26
$13
$7
$3
$2
$1...
“Watch your thoughts, they become words.
Watch your words, they become actions.
Watch your actions, they become habits.
Wa...
Felon Classifier Overview
ModelLearner
250 M
Defendants Feature
Extraction
15K Labels
15K Predictors
Cleaning
Linking
Samp...
How does the Felon Classifier work?
Gender Eye Color Tattoos Criminal Offenses Score
Over
Threshold
of 3.5?
Likely
Felon?
...
Classifiers depend on policy as much as
technology
ANARCHY
T Y R A N N Y
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
0.0% 5.0% 10....
Privacy, Reasonable Suspicion, & Probable Cause
• Courts have upheld profiling
– US v Sokolow
• Predictive information nev...
NYC Stop & Frisk Found UnconstitutionalRuling
“The city … believes that blacks and Hispanics should be stopped at the same...
M O R E P R I VAT E P L A C E S
MOREPLAYERPOWERGAP
News of the World
phone hacking
Rutgers student
commits suicide
after s...
Technology grows exponentially.
Wisdom grows linearly.
• Gov’t doesn’t trust
people (at least
sysadmins) but does
trust ma...
Hilary Mason’s Maxim
Math + Code = Awesome
Quants
Making a killing on Wall Street but still can’t impress the chicks
Weako...
Corollary to Mason’s Maxim
Values * (Math + Code) = Awesome
Lesson
Norio Ohga
Sony President
74 min CD
Jeff Jonas
Big Data
Privacy by Design
Mark Zuckerberg
Real Names
Steve Jobs
‘nuff said...
We’re making this up as we go.
Austin Alleman @allemanau
Innocence Frontier Regulation Innovation
Confession
“Can’t we all just get along?”Plea
Social
Entrepreneur
High-Tech
Mercenary
Responsible
Innovator
Traditional
Capitalist
− ...
Lesson “No one here gets out alive.”
− Jim Morrison
Adapt, Inve
nt,
Test
Question, S
crutinize, Cr
iticize
Watch, List
en,...
Questions!
Jim Adler
www.metanautix.com
jimadler@metanautix.com
Confessions (and Lessons) of a "Recovering" Data Broker
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  1. 1. Confessions (and Lessons) of a "Recovering" Data Broker: Responsible Innovation in the Age of Big Data, Big Brother, and the Coming Skynet Terminators Jim Adler Vice President, Products Metanautix jimadler@metanautix.com @jim_adler http://jimadler.me Usenix Security Conference Washington, DC Aug 15 2013
  2. 2. About Metanautix • Mission: Next generation big data management & analysis – Transparency into the big data supply chain within and across organizations • Engineering led, product company – Team of veterans (10-20+ years experience) – Built massive data analysis systems at Google, FB, MSFT, AMZN, Pixar – Solidly funded by top VCs – Working with lighthouse customers • Started Nov 2012, slowly emerging from stealth mode • Stay tuned!
  3. 3. I am not an attorney.Confession Dork Nerd Geek Dweeb
  4. 4. You can often do more from the inside than the outside. • 20B public records • 30M visitors per month • 50M+ reports sold Confession
  5. 5. Listen to your toughest criticsLesson
  6. 6. Can I have a little narcissism with my voyeurism? • What does my background check say? • Privacy controls – Suppress single address or phone number • Comment on your own public profile Lesson
  7. 7. Data from Feb 2013 Data from 40,000 BC thru 2002 20 Exabytes (20M TB) Lots of data created, transferred, & stored.Confession
  8. 8. The “Public” Data Supply Chain of YouConfession Search Blogs Criminal Records Risk Civil Suits Marketing Addresses Directory Phone Numbers Payments Background Resumes Public Posts Name s Data Uses Government Commercial Self-Reported Big Data Engines
  9. 9. FIND OWNER OF DOG’S RELATIVE FOR TRANSPLANT SINGLES CURIOUS ABOUT THE PEOPLE THEY MEET PARENTS ENSURING WHO THEIR KIDS SAFETY GENEALOGISTS CULTIVATING THEIR FAMILY TREE BUSINESSES THAT NEED TO UPDATE CONTACT INFORMATION ON CUSTOMERS FINDING LONG-LOST FRIENDS, MILITARY BUDDIES, ROOMMATES, OR CLASSMATES ANYONE CURIOUS ABOUT WHO'S EMAILING OR CALLING THEM THOSE IN LEGALLY ENTANGLED LOOKING FOR COURT RECORDS ANYONE WHO NEED ADDRESS HISTORIES FOR PASSPORTS SOCIAL NETWORKERS LOOKING TO EXPAND THEIR FRIENDS LIST PROFESSIONALS LEARNING ABOUT COLLEAGUES AT CONFERENCES RECONNECTING OUT-OF-TOUCH FAMILY MEMBERS ONLINE SHOPPERS VERIFYING ONLINE SELLERS INVESTIGATIVE JOURNALISTS RUNNING DOWN LEADS SALES PROFESSIONALS LOOKING FOR NEW PROSPECTS NETWORKERS SEEKING BUSINESS OPPORTUNITIES LAW ENFORCEMENT NON-PROFIT ORGANIZATIONS LOOKING FOR SUPPORTERS FIANCÉS AND THEIR CURIOUS FAMILY MEMBERS SOCIAL WORKERS WHO NEED TO KNOW MORE ABOUT THEIR CLIENTS CALLER ID OF HARASSING PHONE CALLS ALUMNI GROUPS ARRANGING REUNIONS ADOPTED KIDS SEEKING THEIR BIOLOGICAL PARENTS AIRLINES TRYING TO RETURN LOST LUGGAGE CHECKING OUT A PROSPECTIVE SOCIAL NETWORK CONNECTION CHECKING OUT A PROSPECTIVE DATE CHECKING OUT A PROSPECTIVE TENANT FINDING PEOPLE THAT HAVE THE SAME ILLNESS AS YOU RESEARCHING A PROSPECTIVE EMPLOYEE ANYONE RETRIEVING COURT RECORDS REGULATED Lots of uses for your data … some regulated LAWYERS NEEDING QUICK ACCESS TO COURT RECORDS BANKING SERVICES RESEARCH SHARING LEARNING ABOUT A BUSINESS Confession
  10. 10. Billions of Records Millions of People Jim Adler Houston, TX Age 70 Jim Adler Redmond, WA Age 50 Jim Adler Denver, CO Age 48 Jim Adler McKinney, TX Age 57 Jim Adler Canaan, NH Age 59 Jim Adler Hastings, NE Age 32 213 records linked to the correct 37 Jim Adlers Philip Collins 375 People Jim Adler 213 Records 37 People Randolph Hutchins 5 People Gwen Fleming 2 PeopleCarol Brooks 9800 Records 1250 People Confession We don’t know you all that well
  11. 11. Opt-out doesn’t always mean deletionConfession Jane Hampton 06/23/1998 123 Main Peoria, IL Jane Hampton 123 Main Peoria, IL jane@facebook.com Jane Hampton jane@facebook.com Jane Hampton 06/23/1998 123 Main Peoria, IL Jane Hampton 123 Main Peoria, IL jane@facebook.com Jane Hampton jane@facebook.com
  12. 12. When towns were small, personal anonymity was low … “The only thing worse than being talked about, is not being talked about.” − Oscar Wilde Lesson
  13. 13. “Good Fences Make Good Neighbors” − Robert Frost Urban populations grew along with personal anonymity… Lesson
  14. 14. 0 20 40 60 80 100 120 1850 1890 1930 1970 2010 PrivacyExpectations “Rockwell” Era “Good Fences” Era “Privacy Vertigo” Era … we’re suffering from Privacy Vertigo.Confession Urban Density ↓ Personal Anonymity ↓
  15. 15. Privacy is deeply cultural.Lesson Discretion Disclosure
  16. 16. EU “Right of Personality” • Inviolable right of dignity • Germany: BGB, 1900, post WWII US: Brandeis & Warren, 1890 • Germany: “Source Right” • Esra “kiss & tell” plaintiff won, book banned US “Privacy Torts” • Statutory harm • US: Prosser’s Torts, 1960 • US: Sectorial privacy regime • Bonome “kiss & tell” case dismissed EU Rights versus US Torts http://paulschwartz.net/pdf/SchwartzPeifer_Prosser_FINAL.pdf
  17. 17. How to unpack Privacy? Think PPP. PERILS Lesson
  18. 18. Sometimes you’re in a public place when you think you’re in a private place. “Gaydar” A 2009 MIT study found it was possible to predict men’s sexual orientation by analyzing the gender and sexuality of their social network contacts – even if the rest of the information on their profile was set to private. Confession
  19. 19. “To Serve Man” is a Cookbook.Confession “If you’re not paying for the product, you are the product.” − Claire Wolfe (paraphrased)
  20. 20. Peer to Peer Corporation & Customer/Employee Government & Citizen Your God & You PrivacyRights Power Disparity  In privacy contexts, Power matters.Lesson
  21. 21. Target knows you’re pregnant and when you’re due. So, what’s so perilous? Confession
  22. 22. Secrets are power equalizers.Confession DPower µ 1 Trust µSecrets
  23. 23. Public Places Powerful Players Private Places Powerful Players Private Places Weaker Players Public Places Weaker Players M O R E P R I VAT E P L A C E S MOREPLAYERPOWERGAP Mapping Places-Players-Perils Cases
  24. 24. M O R E P R I VAT E P L A C E S MOREPLAYERPOWERGAP News of the World phone hacking Rutgers student commits suicide after spied by webcam FBI GPS criminal surveillanceGoogle privacy policy unification Target finds out teen pregnant before parents GM OnStar tracks users Woman caught naked by Google Street View Actress sues IMDB over revealing her age US deports British tourists over Tweets FB user sets fire to home after de- friending "Girls Around Me" pulled from market Health orgs use Twitter to track illness NSA internet citizen surveillance Georgia teacher fired after posting vacation pics Places-Players-Perils Cases
  25. 25. A head in the clouds < 20 yearsPrediction $27,100 $13,500 $6,800 $3,400 $1,700 $850 $420 $210 $100 $53 $26 $13 $7 $3 $2 $1 $1 $10 $100 $1,000 $10,000 $100,000 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 CostperMonth(000s) Year • 20,000 TFlops • 2,500 Terabytes • Less than $700K per year Chris Westbury, University of Alberta
  26. 26. “Watch your thoughts, they become words. Watch your words, they become actions. Watch your actions, they become habits. Watch your habits, they become your character. Watch your character, it becomes your destiny.” – Lao Tzu “… the essential crime that contained all others in itself. Thoughtcrime, they called it.” – George Orwell Big data inferences are not thoughtcrimesLesson
  27. 27. Felon Classifier Overview ModelLearner 250 M Defendants Feature Extraction 15K Labels 15K Predictors Cleaning Linking Sampling Objective If someone has minor offenses on their criminal record, do they also have any felonies? Today’s Bloomberg piece: http://bloom.bg/1eMtnug
  28. 28. How does the Felon Classifier work? Gender Eye Color Tattoos Criminal Offenses Score Over Threshold of 3.5? Likely Felon? NO YES Hazel (+1.7) Male Blue 2 + (+1.3) Traffic only Female (-0.5) Brown < 2 4 or fewer misdemeanors (+1.9) YES Green 8 or fewer misdemeanors NO 4.4 Hazel Male (+0.1) Blue 2 + Traffic only (-0.5) Female Brown (+1.2) < 2 (+0.1) 4 or fewer misdemeanors YES Green 8 or fewer misdemeanors NO 0.9 widget: http://jimadler.me
  29. 29. Classifiers depend on policy as much as technology ANARCHY T Y R A N N Y 0.0% 20.0% 40.0% 60.0% 80.0% 100.0% 0.0% 5.0% 10.0% 15.0% 20.0% FalseNegativeRate False Positive Rate Threshold: 0.66 FP Rate: 5% FN Rate: 22% Threshold: 1.1 FP Rate: 1% FN Rate: 40% Threshold: -1.82 FP Rate: 19% FN Rate: 0% Confession
  30. 30. Privacy, Reasonable Suspicion, & Probable Cause • Courts have upheld profiling – US v Sokolow • Predictive information never enough 1. Reliable 2. Efficient 3. Particularized 4. Detailed 5. Timely 6. Corroborated • More • Andrew Guthrie Ferguson, Predictive Policing http://ssrn.com/abstract_id=2050001 • Bernard Harcourt, Rethinking Racial Profiling http://www.law.uchicago.edu/files/files/rethinking_racial_profiling.pdf • The “Not Ready For Prime Time” Classifier http://jimadler.me/post/47374264398/the-not-ready-for-prime-time-classifier
  31. 31. NYC Stop & Frisk Found UnconstitutionalRuling “The city … believes that blacks and Hispanics should be stopped at the same rate as their proportion of the local criminal suspect population.” − US District Judge Shira Scheindlin = 2*3%=10% 100% greater chance that a minority stop is justified over a random stop P~A|M = 90% PA|M = 100% 50% 5% NYC assumes 87% Minorities in NYC If it’s not ok to stop & frisk 95% of the general population for nothing, why is it ok to stop & frisk 90% of minorities for nothing? PA|M = PM|A PM PA PA|M = Chance a minority should be arrested P~A|M= Chance a minority should not be arrested PM|A = Chance that someone arrested is a minority PM = Chance someone is a minority PA = Chance someone is arrested Bayes’ Rule
  32. 32. M O R E P R I VAT E P L A C E S MOREPLAYERPOWERGAP News of the World phone hacking Rutgers student commits suicide after spied by webcam FBI GPS criminal surveillanceGoogle privacy policy unification Target finds out teen pregnant before parents GM OnStar tracks users Woman caught naked by Google Street View Actress sues IMDB over revealing her age US deports British tourists over Tweets FB user sets fire to home after de- friending "Girls Around Me" pulled from market Health orgs use Twitter to track illness NSA internet citizen surveillanceGeorgia teacher fired after posting vacation pics Big brother is watching (duh) “We’re being asked to trust without being able to verify.” − Alex Howard Pres. Obama calls for more transparency in FISA court and surveillance laws NSA chief announces plan to replace 1,000 sysadmins with machines Confession
  33. 33. Technology grows exponentially. Wisdom grows linearly. • Gov’t doesn’t trust people (at least sysadmins) but does trust machines • Little Transparency • Wisdom is hard to come by • Sentient (?) brain in the cloud in < 20 years Lesson Wisdom Knowledge Information Data
  34. 34. Hilary Mason’s Maxim Math + Code = Awesome Quants Making a killing on Wall Street but still can’t impress the chicks Weakonomics.com Lesson
  35. 35. Corollary to Mason’s Maxim Values * (Math + Code) = Awesome Lesson
  36. 36. Norio Ohga Sony President 74 min CD Jeff Jonas Big Data Privacy by Design Mark Zuckerberg Real Names Steve Jobs ‘nuff said Eclectic generalists drive innovation Richard Feynman Physics Lesson
  37. 37. We’re making this up as we go. Austin Alleman @allemanau Innocence Frontier Regulation Innovation Confession
  38. 38. “Can’t we all just get along?”Plea Social Entrepreneur High-Tech Mercenary Responsible Innovator Traditional Capitalist − Rodney King
  39. 39. Lesson “No one here gets out alive.” − Jim Morrison Adapt, Inve nt, Test Question, S crutinize, Cr iticize Watch, List en, Learn Geeks WonksSuits
  40. 40. Questions! Jim Adler www.metanautix.com jimadler@metanautix.com
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