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A Recommendation System for Email Recipients
Vitor R. Carvalho and William W. Cohen,
Carnegie Mellon University
March 2007
Preventing Email Leaks
On July 6th 2001, the news agency Bloomberg.com published…
“California Governor Gray Davis’s office released data
on the state’s purchases in the spot electricity market — information Davis has been trying to keep secret — through a misdirected e-mail. The e-mail, containing data on California’s power purchases yesterday, was intended for members of the governor’s staff, said Davis spokesman Steve Maviglio. It was accidentally sent to some reporters on the office’s press list, he said. Davis is fighting disclosure of state power purchases, saying it would compromise negotiations for future contracts”.
Information Leaks via Email
Email leak = email message accidentally sent to “unintended” recipients.
1. Similar first/last names, aliases
2. Aggressive auto-completion of email addresses
3. Typos
4. Keyboard settings
Email leaks may contain sensitive information leading to disastrous consequences.
Detecting Email Leaks: Method
Idea1. Goal: to detect emails
accidentally sent to the wrong person
2. 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.
3. Method: Look for outliers
Look for Outliers1. Build model for (msg-
recipients) pairs: train classifier on real data to detect simulated outliers (added to the “true” recipient list).
2. Features: textual(subject, body), network features (frequencies, co-occurrences, etc).
3. Rank potential outliers - Detect outlier and warn user based on classifier’s confidence
Detecting Email Leaks: Method
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”.
Look for Outliers
1. Build model for (msg-recipients) pairs: train classifier on real data to detect simulated outliers (added to the “true” recipient list).
2. Features: textual(subject, body), network features (frequencies, co-occurrences, etc).
3. Rank potential outliers - Detect outlier and warn user based on classifier’s confidence
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.
• 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
Data Preprocessing
• Used the Enron Email Dataset
• 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.
• Self-addressed messages were disregarded
Still Data Preprocessing
• 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 (Textual Features)
• Some stop words removed
Experiments:Textual Features only
• Three Baseline Methods– Random
• Rank recipient addresses randomly– Cosine or TfIdf Centroid (Rocchio)
• 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: Textual
Features only
Email Leak Prediction Results: Accuracy (or Prec@rank_1) in 10 trials.
On each trial, a different set of outliers is generated
Accuracy
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.
Finding Real Leaks in Enron
• How can we find it?– Look 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.
• Found 2 real “valid” cases! (“valid” = testable)– Message germanyc/sent/930, message has 20 recipients, leak is
alex.perkins@– kitchen-l/sent items/497, it has 44 recipients, leak is rita.wynne@
Finding Real Leaks in Enron
– Very Disappointing Results!!
– Reason: alex.perkins@ and rita.wynne@ were never observed in the training set!
[Accuracy, Average Rank], 100 trials
“Smoothing” the leak generation
Else: Randomly select an address book entry
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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, out of 20.
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
Conclusions
• Privacy and Email papers are rare. To the best of our knowledge, this was the first paper on preventing information leaks via email.
• Can prevent HUGE problems
• Easy to implement in any email client – no change in email server side.
• The Email Leak paper was accepted in SDM-2007.
“This is a feature I would like to have in the email client I use myself” “Personally, I am eager to use such a tool if its accuracy is good.”
&
A Recommendation System for Email Recipients
Vitor R. Carvalho and William W. Cohen,
Carnegie Mellon University
March 2007
Preventing Email Leaks
Recommending Email Recipients
1. Prevent a user from forgetting to add an important collaborator or manager as recipient, preventing costly misunderstandings and communication delays. Cost of errors in task management is high: for instance, deadlines can be missed or opportunities wasted because of such errors.
2. Find people in an organization that are working in a similar topic or project, or to find people with appropriate expertise or skills.
• Valuable addition to email systems, particularly in large corporations
Email systems that can suggest who recipients of a message might be while the message is being composed, given its current contents and given its previously-specified recipients.
Two Recommendation Tasks
TO+CC+BCC
PredictionCC+BCC
Prediction
Method: 1. Extract Features: Textual and non-Textual2. Build model for (msg-recipients) pairs:
train classifier to detect “true” missing recipients.
3. Rank all email addresses in Address Book according to classifier’s confidence
Methods
• Large-scale multi-class multi-label classification tasks:
• Address Books have hundreds, sometimes thousands, of email addresses (classes)
• One-vs-all training is too expensive, even for users having a small collections of messages
• Using Information Retrieval Techniques as baselines:• Rocchio (TfIdf-Centroid) and KNN
• Enron Dataset with similar preprocessing steps as Leak Problem
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 (CC+BCC only)– 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. Recency features– How frequent a recipient is in the last 20, 50, 100
messages.
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
Conclusions
• Submitted to KDD-07
• The Recipient Prediction task can be seen as the negative counterpart of the Email Leak Prediction task. In the former, we want to find the intended recipients of email messages, whereas in the latter we want to find the unintended recipients or email-leaks.
• A desirable email system addition to avoid misunderstandings and communication delays. Efficient, easy to implement and integrate, particularly in email systems where traditional search is already available.