When a company is hacked, and sensitive user data released without authorization, it's clear that information security has been breached. However companies may be willingly, unknowingly leaking information in non-obvious ways today, and the villain is also a hero of the big data era: data science. Applying data science to released data sets may discover latent information within that wasn't intended to be made public. In this webinar, we'll look at a few high-profile examples of this, and discuss what went wrong and how it could have been avoided. For two cases, we'll explore the problem and solution in more detail with technologies like Apache Spark: the New York City Taxi and Limousine Commission's release of data on taxi rides in the city, and Netflix's release of movie rating data as part of its $1M Netflix Prize.

1. 1© Cloudera, Inc. All rights reserved.
Sean Owen | Director, Data Science
Fuzzy Data Leaks

2. 2© Cloudera, Inc. All rights reserved.
(Not Fuzzy) Data Leak:
In 2007, hackers stole
details 45.7 million
credit and debit cards
from TJ Maxx and
Marshall's. Possibly
the "largest hack
ever."

3. 3© Cloudera, Inc. All rights reserved.
Fuzzy Data Leak:
ACME Co. shares this
non-personally-
identifiable data with
a partner for market
research. Where's the
leak?
City,Gender,DOB,…
Duluth,M,18-Sep-1975,…
Reno,F,04-Feb-1954,…
Tacoma,F,01-Mar-1944,…
Austin,M,09-Oct-1980,…
Spokane,M,09-Feb-1970,…
Reno,M,10-Oct-1980,…
Boise,F,01-Jan-1970,…
...

4. 4© Cloudera, Inc. All rights reserved.
"About half of the U.S. population
… are likely to be uniquely
identified by only place, gender,
date of birth …"
Uniqueness of Simple Demographics in the U.S. Population,
by Latanya Sweeney

5. 5© Cloudera, Inc. All rights reserved.
AOL Search Data Leak
Raw user input is sensitive

6. 6© Cloudera, Inc. All rights reserved.
In 2006, AOL released
20 million raw search
queries for 658,000
users for research
purposes, with users
identified only by
number

7. 7© Cloudera, Inc. All rights reserved.
User 4417749: Thelma Arnold
www.nytimes.com/imagepages/2006/08/08/business/09aol-graphic.html

8. 8© Cloudera, Inc. All rights reserved.
Secure Raw User Input, Even Internally
v
v
Landing Zone Data Hub
Tokenization
…

9. 9© Cloudera, Inc. All rights reserved.
Netflix Prize Data Set
A little noise is not enough

10. 10© Cloudera, Inc. All rights reserved.
In 2008, Netflix
released 100 million
anonymized ratings
from 480,189 users to
17,770 movies and
offered $1M for the
best recommender
built from this data

11. 11© Cloudera, Inc. All rights reserved.
"... all customer identifying
information has been removed ...
only a small sample was included
(less than one-tenth of our
complete dataset) and that data
was subject to perturbation."
Netflix | http://www.netflixprize.com/faq

12. 12© Cloudera, Inc. All rights reserved.
"Using [IMDB] …,
we successfully identified the
Netflix records of known users,
uncovering their apparent political
preferences …"
Robust De-anonymization of Large Sparse Datasets, by
Arvind Narayanan and Vitaly Shmatikov

13. 13© Cloudera, Inc. All rights reserved.
Netflix Netflix Prize Data IMDB

14. 14© Cloudera, Inc. All rights reserved.
Obscure movies carry
orders of magnitude
more information
about identity.
https://www.cs.utexas.edu/~shmat/shmat_oak08netflix.pdf

15. 15© Cloudera, Inc. All rights reserved.
Perturbation doesn't
hurt ability to model
much.
Simple Apache Spark
implementation,
adding a fake movie
rating for every real
one, increases RMSE
by ~ 0.1
val dataSet = sc.textFile(".../training_set.csv").map { line =>
val Array(item, user, rating, _) = line.split(",")
Rating(user.toInt, item.toInt, rating.toDouble)
}
val allItems = dataSet.map(_.product).distinct.collect
val perturbedData = dataSet.mapPartitions { partition =>
val r = new scala.util.Random()
partition.flatMap { case rating@Rating(user, _, value) =>
val fakeItem = allItems(r.nextInt(allItems.size))
Seq(rating, new Rating(user, fakeItem, value))
}
}
val Array(trainSet, testSet) =
dataSet.randomSplit(Array(0.99, 0.01), 42)
...
val model = ALS.train(trainSet, 100, 20, 0.05)
...
println(s"RMSE = ${math.sqrt(MSE)}")

16. 16© Cloudera, Inc. All rights reserved.
But, why did Netflix even release
movie names?
"Other datasets provide it. Cinematch
doesn’t currently use this data. Use it if
you want."
(HT Matthew Wright)

17. 17© Cloudera, Inc. All rights reserved.
NYC Taxi Ride Data Set
There's a wrong and right way to obfuscate

18. 18© Cloudera, Inc. All rights reserved.
The New York City Taxi
& Limousine
Commission retains
detailed records of
taxi rides in the city. In
2014 this was
obtained by request
under the Freedom Of
Information Law
(FOIL)
upload.wikimedia.org/wikipedia/commons/8/84/NYC_Hybrid_Taxi.JPG

19. 19© Cloudera, Inc. All rights reserved.
medallion,hack_license,vendor_id,rate_code,store_and_fwd_flag,
pickup_datetime,dropoff_datetime,passenger_count,trip_time_in_secs,
trip_distance,pickup_longitude,pickup_latitude,
dropoff_longitude,dropoff_latitude
89D227B655E5C82AECF13C3F540D4CF4,BA96DE419E711691B9445D6A6307C170,CMT,1,N,
2013-01-01 15:11:48,2013-01-01 15:18:10,4,382,
1.00,-73.978165,40.757977,
-73.989838,40.751171
2Y92? 5002921?

20. 20© Cloudera, Inc. All rights reserved.
1844173304
.hashCode
"Why, then the world's mine oyster,
Which I with sword will open."

21. 21© Cloudera, Inc. All rights reserved.
D41D8CD98F00B204E9800998ECF8427E
MD5
""
tech.vijayp.ca/of-taxis-and-rainbows-f6bc289679a1

22. 22© Cloudera, Inc. All rights reserved.
3N49
FY985
BXJ761
26,000
+ 676,000
+ 17,576,000
= 18,278,000

23. 23© Cloudera, Inc. All rights reserved.
Time to MD5 hash all
18,278,000 possible
medallions on a 6-
node Apache Spark
cluster:
2 seconds

24. 24© Cloudera, Inc. All rights reserved.
val digits = '0' to '9'
val alpha = 'A' to 'Z'
val nxnn = sc.parallelize(digits).flatMap(a =>
for (b <- alpha; c <- digits; d <- digits)
yield new String(Array(a, b, c, d)))
val xx = for (a <- alpha; b <- alpha) yield Array(a, b)
val xxnnn = sc.parallelize(xx).flatMap(prefix =>
for (c <- digits; d <- digits; e <- digits)
yield new String(prefix ++ Array(c, d, e)))
val xxxnnn = sc.parallelize(xx).flatMap(prefix =>
for (c <- alpha; d <- digits; e <- digits; f <- digits)
yield new String(prefix ++ Array(c, d, e, f)))
sc.union(nxnn, xxnnn, xxxnnn).mapPartitions { medallions =>
val md5 = java.security.MessageDigest.getInstance("MD5")
medallions.map { medallion =>
val bytes = medallion.getBytes(StandardCharsets.UTF_8)
(toHex(md5.digest(bytes)), medallion)
}
}.collectAsMap()

25. 25© Cloudera, Inc. All rights reserved.
"In Brad Cooper’s
case, we now know
that his cab took him
to Greenwich Village,
possibly to have
dinner at Melibea,
and that he paid
$10.50, with no
recorded tip."
research.neustar.biz/2014/09/15/riding-with-the-stars-passenger-privacy-in-the-nyc-taxicab-dataset/

26. 26© Cloudera, Inc. All rights reserved.
Solution 1: Random Mapping
• Make an arbitrary mapping from
inputs to random, unique, obfuscated
identifiers
• A0AA = 348599828
• A0AB = 118201402
• …
• As secure as it is random
• Must construct complete mapping
Solution 2: Salting
• Hash some private transformation of
the input, like: input + "salt"
• Easy, same characteristics
• MD5("3N49") reversible since
preimage was small, obvious
• MD5("3N49"+"salt") not
reversible since preimage is unknown
without knowing "salt"
A Little Salt Goes a Long Way

27. 27© Cloudera, Inc. All rights reserved.
Tokenize,
Perturb
& Salt

28. 28© Cloudera, Inc. All rights reserved.
Thank you
@sean_r_owen
sowen@cloudera.com