Preventing Information Leaks in Email

Vitor

Text Learning Group MeetingJan 18, 2007 – SCS/CMU

Outline

1. Motivation2. Idea and method

Leak Criteria, text-based baselines Crossvalidation, network features

3. Results4. Finding Real Leaks in the Enron Data5. Predicting Real leaks in the Enron Data

Smoothing the leak criteria

6. Related Work7. Conclusions

Information Leaks

What’s being leaked? Credit card, New products information Social Security Numbers Software pre-release versions Business Strategy, Health records, etc.

Multi-million dollar industry (ILDP) Anonymity and Privacy of data Information Leakage Detection and Prevention (from

Wikipedia)

Information Leak using Email Hard to estimate, but according to PortAuthority

Technologies

How data is being leaked

Email Leaks make good headlines. Just google it…

California Power-Buying Data Disclosed in Misdirected E-Mail

Leaked email exposes MS charity as PR exercise

Bush Glad FEMA Took Blame for Katrina, According to Leaked Email

More Email leak in the headlines

Dell leaked email shows channel plans -Direct threat haunts dealers-A leaked email reveals Dell wants to get closer to UK resellers.

Business group say Liberals handled leaked email badly.

Is Leaked eMail a SCO-Microsoft Connection?

“Leaked email may be behind Morgan Stanley's Asia economist's sudden resignation”

Detecting Email Leaks

Idea Goal: to detect emails

accidentally sent to the wrong person

Generate artificial leaks: Email leaks may be simulated by various criteria: a typo, similar last names, identical first names, aggressive auto-completion of addresses, etc.

Method: LOOK FOR OUTLIERS.

Email Leak: email accidentally sent to wrong person

Email Leak

Avoiding Expensive Email Errors

Method Create simulated/artificial email recipients Build model for (msg.recipients): train classifier on

real data to detect synthetically created outliers (added to the true recipient list). Features: textual(subject, body), network features

(frequencies, co-occurrences, etc). Rank potential outliers - Detect outlier and warn user

based on confidence.

Rec_6

Rec_2

…

Rec_K

Rec_5

Most likely outlier

Least likely outlier

P(rec_t)

P(rec_t) =Probability recipient t is an outlier given “message text and other recipients in the message”.

Leak Criteria: how to generate (artificial) outliers

Several options: Frequent typos, same/similar last names,

identical/similar first names, aggressive auto-completion of addresses, etc.

In this paper, we adopted the 3g-address criteria: On each trial, one of the msg recipients is randomly

chosen and an outlier is generated according to:

Else: Randomly select an address book entry

1

2

3

Marina.wang @enron.com

Dataset: Enron Email Collection

Why? Large, thousands of messages Natural email, not email lists Real work environment Free No privacy concerns More than 100 users (with sent+received msgs)

Enron Data Preprocessing 1

Setup a realistic temporal setup For each user, 10% (most recent) sent messages

will be used as test

All users had their Address Books extracted List of all recipients in the sent messages.

Enron Data Preprocessing 2

ISI version of Enron Remove repeated messages and inconsistencies

Disambiguate Main Enron addresses List provided by Corrada-Emmanuel from UMass

Bag-of-words Messages were represented as the union of BOW

of body and BOW of subject Some stop words removed Self-addressed messages were

removed

Experiments: using Textual Features only

Three Baseline Methods Random

Rank recipient addresses randomly Cosine or TfIdf Centroid

Create a “TfIdf centroid” for each user in Address Book. A user1-centroid is the sum of all training messages (in TfIdf vector format) that were addressed to user user1. For testing, rank according to cosine similarity between test message and each centroid.

Knn-30 Given a test msg, get 30 most similar msgs in

training set. Rank according to “sum of similarities” of a given user on the 30-msg set.

Experiments: using Textual Features only

Email Leak Prediction Results: Prec@1 in 10 trials.

On each trial, a different set of outliers is generated

Network Features

How frequent a recipient was addressed

How these recipients co-occurred in the training set

Using Network Features

1. Frequency features Number of received messages (from this user) Number of sent messages (to this user) Number of sent+received messages

2. Co-Occurrence Features Number of times a user co-occurred with all other

recipients. Co-occurr means “two recipients were addressed in the same message in the training set”

3. Max3g features For each recipient R, find Rm (=address with max score

from 3g-address list of R), then use score(R)-score(Rm) as feature. Scores come from the CV10 procedure. Leak-recipient scores are likely to be smaller than their 3g-address highest score.

To combine textual features with network features: Crossvalidation

Training Use Knn-30 on 10-Fold crossvalidation setting to get

“textual score” of each user for all training messages Turn each train example into |R| binary examples, where

|R| is the number of recipients of the message. |R|-1 positive (the real recipients) 1 negative (leak-recipient)

Augment “textual score” with network features Quantize features Train a classifier VP5- Classification-based ranking

scheme (VP5=Voted Perceptron with 5 passes over training set)

Results: Textual+Network Features

Finding Real Leaks in Enron

How can we find it? Grep for “mistake”, “sorry” or “accident”. We were

looking for sentences like “Sorry. Sent this to you by mistake. Please disregard.”, “I accidentally send you this reminder”, etc.

How many can we find? Dozens of cases. Unfortunately, most of these cases were originated by non-

Enron email addresses or by an Enron email address that is not one of the 151 Enron users whose messages were collected

Our method requires a collection of sent (+received) messages from a user. Only 150 Enron users .

Finding Real Leaks in Enron

Found 2 good cases:1. Message germanyc/sent/930, message

has 20 recipients, leak is alex.perkins@2. kitchen-l/sent items/497, it has 44

recipients, leak is rita.wynne@

Prepared training data accordingly (90/10 split) and no simulated leak added

Results: Finding Real Leaks in Enron

Very Disappointing!!

Reason: alex.perkins@ and rita.wynne@ were never observed in the training set!

[Prec@1, Average Rank], 100 trials

“Smoothing” the leak generation

Else: Randomly select an address book entry

1

2

3

Marina.wang @enron.comGenerate a random email address NOT in Address Book

•Sampling from random unseen recipients with probability

Some Results:

•Kitchen-l has 4 unseen addresses out of the 44 recipients,

•Germany-c has only one.

Mixture parameter :

Germany Leak Case

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 0.1 0.2 0.3 0.4 0.5

a

AvgRank

Prec@1

Mixture parameter :

Kitchen-l Leak Case

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0 0.05 0.1 0.15 0.2

a

Prec@1

Kitchen-l Leak Case

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 0.05 0.1 0.15 0.2a

AvgRank

Back to the simulated leaks:

What’s next

Modeling Better, more elegant model

Email Server side application Predict based on all users on mail

server In companies, use info from all email

users Privacy issues

Integration with cc-prediction

Related Work

Email Privacy Enforcement System Boufaden et al. (CEAS-2005) - used information

extraction techniques and domain knowledge to detect privacy breaches via email in a university environment. Breaches: student names, student grades and student IDs.

CC Prediction Pal & McCallum (CEAS-06) Counterpart problem:

prediction of most likely intended recipients of email msg. One single user, limited evaluation, not public data

Expert finding in Email Dom et al.(SIGMOD-03), Campbell et al(CIKM-03) Balog & de Rijke (www-06), Balog et al (SIGIR-06) Soboroff, Craswell, de Vries (TREC-Enterprise 2005-06-

07…) Expert finding task on the W3C corpus

Thanks!

Questions? Comments?

Ideas?