Например, Бобцов

Об использования данных полученных от анонимных электронных сервисов в нынешнее время

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Describing how the data obtained from DEA services is being used nowadays

Smirnov A. asmirn3@uic.edu
University of Illinois at Chicago, Chicago, IL, USA
The paper describes what hidden purposes users follow when set up an account at one of the DEA services. The definition of DEA services is provided. The purposes of using DEA services are then described and split into different categories and the estimation is given in the end of the paper. Each category is described for understanding the meaning of splitting the data. The results include the final statistics which breaks down all the messages and provides detailed analysis of users using DEA service. The matter of legitimate use is discussed in the end of the paper.
Keywords: DEA, Naïve Bayes, supervised learning, spam, ham.
Об использования данных полученных от анонимных электронных сервисов в нынешнее время
Смирнов А. asmirn3@uic.edu
Университет шт. Иллинойс в Чикаго, США
Данная статья описывает скрытые причины, по которым пользователи создают профайл в анонимных электронных сервисах. Определение анонимных электронных сервисов приведено в работе. Затем рассматриваются причины, по которым пользователи используют данные сервисы и, разделяются по категориям, после чего приводится предположение дальнейших действий. Каждая категория описана для понимания сути разделения данных об использовании сервисов. Результаты работы включают в себя итоговую статистику, которая показывает, по каким причинам пользователи используют данные электронные сервисы и приведен детальный анализ использования сервиса по каждой категории. Причина легального использования сервиса обсуждается в конце работы.
Ключевые слова: анонимные электронные сервисы, наивный байесовский классификатор, обучение с учителем, спам, не спам.
In this paper I was working on the data that was obtained from five various independent DEA services. DEA services are such services that do not require a user to create an account in order to receive an email to any email address supported by the service. Thus, all the data becomes public due to no identification required. Since the DEA services are used by many users from all over the world for a variety of purposes, then the data that was obtained such services is obviously to be very diverse. It is very important to understand that users may use DEA services for both legitimate and non-legitimate purposes [1]. Since all the data is public and some users use the DEA services for legitimate purposes, these users may not realize how public the data are and what risk they take. This is the main factor why DEA services may not be very helpful as they seem to be at first. In order to protect personal information and avoid an identity theft, the users should be informed beforehand what risk they take. Some non-legitimate users may find this data (name,

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address, SSN, credit card number, etc.) very useful and the victim can suffer adverse consequences if they are held accountable for the perpetrator's actions.
The main purpose of the paper was to characterize this found email data. Once I are done splitting the data into different categorize, I can look at the informative data. The informative data would be categorized as „ham‟. A closer look at the messages in the „ham‟ category will benefit us in some ways such are presented below:
Understand what people use DEA services for legitimate purposes.
What did influence these users use DEA service over the standard email account?
Is there any risk that people undergo when using DEA services for legitimate
purposes, if there is, how high is that risk?

After processing all the data, I will be able to demonstrate how much of this data is used for legitimate purposes, non-legitimate purposes and any other purposes (if any); show whether the users take any risk of loss of any personal information when using DEA services

Found email data sets
Disposable email addressing (DEA) is a method of sharing and managing email addressing [2]. DEA services allow users to set up a new, unique email address for every contact or use an already existing email address that only requires having a username to access the existing account in order to make a connection between the sender and the recipient.
My intuition is that the DEA services are mostly used by people who try to avoid using their personal email accounts due to various factors including receiving spam [2,3]. Thus, these people prefer to use DEA services to stay anonymous.
The main advantage of using the DEA services is that there is no need to register by using your real credentials and, of course, these services are free of charge. The user is given a choice to select any name for the email address and use it, even if it was used before [4].
As any other service, there are also disadvantages of using the DEA services. If this account has been used before, then the user will be able to track all the messages in this account and some private information may become public. Many forums and legitimate services filter out messages sent from DEA domains.

The primary work for this paper was to characterize the found email data from various email services listed below:
Dispostable Mailinator Mytrashmail Staging Tempemail

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I have competed two separate stages of characterizing the data. I were mainly interested in breaking down the category of “ham” email versus “spam” category. The categories used at the first and second stages are listed below:
Stage 1:
Spam The messages considered to be under the category of spam if it‟s obvious that the
recipient has no pre-existing relations with the sender. For example, any message that looks like an advertisement and it was sent to thousands of people simultaneously, this message will be considered as spam [3].

Ham The messages considered to be under the category of ham if the user seems to have
pre-existing relation such as the message seems to indicate a specific action taken by the person to cause it (an invoice for purchased products, or a response mentioning that a purchase could not go through etc.) will be considered ham [3].
Non English The messages considered to be non English if the original language in what the
message was written is other than English, e.g., Russian, Spanish, Italian, Chinese, Arabic, Hebrew, Ukrainian (the listed languages were found in the found email data set).
Other The messages considered to be under the category of other if the messages couldn‟t
be assigned to any other category mentioned above. For example, an email with no text in it and a plain black background would be described as other.
Errors The messages considered to be under the category of other if the messages couldn‟t
be parsed by program or broken files. Stage 2:
Buying The messages considered to be under the category of buying if the email that was
received by the recipient mentions that he or she has purchased something. For example, an email received from paypal service would be considered as buying email.

Signing Up
The messages considered to be under the category of signing up if the email that was received by the percipient shows that the user has subscribed to some service. For example, an email received from the forum web sites is considered as signing up email.

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Non Ham (Spam) The definition provided above in stage 1.


The messages considered to be under the category of personal if the user has requested a subscription from the social websites. For example, an email received from Linkedin is considered as a personal email. The found email data, as mention above, was taken from five independent DEA services. Please refer to the table to see what the size of the data was from each service.

DEA Service Dispostable

Size of the data (GB)

% of data 24.21%
















There are two main sets of machine learning techniques that can be used for classifying
the data: supervised and unsupervised learning [5]. The supervised learning is when the
data is tagged before the algorithm makes any further decisions. On the other hand, if there
is no input to the algorithm, then unsupervised learning has to be used. Algorithms in the
group of unsupervised learning find the similarities and/or correlations in the data and
require no input to classify the data.
There was a choice of using either supervised or unsupervised learning. Since the data set of found email data was 8.67 GB, it was more appropriate to use supervised learning. Naïve
Bayes method was chosen to be used as a method for supervised learning. For the purposes of the describing the data, I decided to use the Naïve Bayes algorithm
which is suitable for handling big sets of data, in our example the data set is 8.76 GB and
diverse data. A Naïve Bayes classifier is a simple probabilistic classifier based on applying Bayes'
theorem with naive independence assumptions. In simple terms, a naive Bayes classifier
assumes that the presence (or absence) of a particular feature of a class is unrelated to the
presence (or absence) of any other feature. For example, a fruit may be considered to be an
apple if it is red, round, and about 4" in diameter. Even if these features depend on each
other or upon the existence of the other features, a Naive Bayes classifier considers all of
these properties to independently contribute to the probability that this fruit is an apple. Depending on the precise nature of the probability model, Naïve Bayes classifiers can be
trained very efficiently in a supervised learning setting [5]. In many practical applications, parameter estimation for Naïve Bayes models uses the method of maximum likelihood; in other words, one can work with the Naïve Bayes model without believing in Bayesian
probability or using any Bayesian methods [6].
Abstractly, the probability model for a classifier is a conditional model: P(C | F1,..., Fn ) over
a dependent class variable C with a small number of outcomes or classes, conditional on

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several feature variables F1through Fn . The problem is that if the number of features n is

large or when a feature can take on a large number of values, then basing such a model on

probability tables is infeasible [5]. I therefore reformulate the model to make it more


Using Bayes' theorem, this can be written as following:

P(C | F1,..., Fn )

p(C) p(F1,..., Fn | C) p(F1,..., Fn )

For the purpose of understanding, in simple English the equation provided above can be

written as following:

posterior prior*likelihood evidence


Due to two factors such as diversity of the data and the size of the data, the algorithm was run twice in order to split the data. At the first stage the following tags were introduced into the model. The description of each tag is provided in the introduction section. The size of data considered at the first stage was 8.76 GB.
1. Spam
2. Ham
3. Non English
4. Other
5. Errors

After training the algorithm, the size of the database which contains the words and its probabilities was 1878 KB (234 messages). The results obtained after the first stage are as following:

Stage 1 - Results


83726 77090


Non English Ham Other Spam Errors

Since the main idea was to break down the category of ham email, the algorithm at the second stage filtered out the emails that were tagged as „Ham‟ after completing the first stage. In order to improve the accuracy of the algorithm, I had to include the „Spam‟ tag into the list of tags used at the second stage. The list of the tags is introduced below (the description of the tags in provided in the introduction section). The size of data considered at the second stage was 1.24 GB.
1. Buying
2. Signing Up

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3. Non Ham (Spam) 4. Personal
After training the algorithm, the size of the database which contains the words and its probabilities was 1620 KB (202 messages). The results obtained after the first stage are as following:
Stage 2 - Results

4207 3163



Buying Signing Up Non Ham (Spam) Personal

Due to the big amount of data, the algorithm accuracy was computer using 50 messages as following:
Accuracy # MatchedMessages *100% ; the message is considered to be matched if the tag
assigned by human is the same as assigned by the Naïve Bayes Classifier.
Accuracy 41 *100% 82% 50
After completing all the stages of the algorithm it‟s obvious that mostly the DEA services are used for „spam‟, which is more than 70%. The minority of usage belongs to the „ham‟ category; it‟s only 6.7% of all the data. The other small usage goes under the category „other‟; it‟s only 6.2%. It‟s important to note that a little less 1/5 of the data is used by foreigners (i.e. in language other than English). The detailed breakdown is provided below:

Category Non English Ham Other Spam Errors

# of messages 83726 36746 33559 387535 11418

% of messages 15.14% 6.67% 6.07% 70.1% 2.06%

The main purpose of the paper was to classify and breakdown the „ham‟ category of emails, but it couldn‟t be done without completing two stages of the algorithm and split out the „spam‟ data at the second stage. Mostly the „ham‟ emails are used for signing up for
different services and the percentage of this data is over 60% (4.11% of the whole data set).

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The second largest subcategory which is being used in the „ham‟ category is „personal‟; its percentage is 22.5% (1.53% of the whole data set). The least used subcategory of the „ham‟ category belongs to „buying‟; its percentage is roughly around 17% (1.14% of the whole data set). Please refer to the table below for the breakdown of the „ham‟ category.


# of messages

% of messages




Signing Up






The future work mainly consists of considering the messages that are considered to be in the „ham‟ category. A closer look at those messages will provide an image of what are the
main purposes and premises of preferring using the DEA services over using standard email accounts for legitimate purposes. Answering the question “Can human readable filter be found among this data to look at” will be answered by manual processing of the split data.

1. Seigneur, J-M., and Christian Damsgaard Jensen. "Privacy recovery with disposable email addresses." Security & Privacy, IEEE 1.6 (2003): 35-39
2. Reed, Micheal G., Paul F. Syverson, and David M. Goldschlag. "Anonymous connections and onion routing." Selected Areas in Communications, IEEE Journal on 16.4 (1998): 482-494
3. Bradley, David (2009-05-13). "Spam or Ham?" Sciencetext. 2011-09-28 4. Shields, Clay, and Brian Neil Levine. "A protocol for anonymous communication
over the Internet." Proceedings of the 7th ACM conference on Computer and communications security. ACM, 2000. 5. Smirnov, Arthur. “Artificial Intelligence: Concepts and Applicable Uses.” Lambert Academic Publishing (2013). 6. Schneider, Karl-Michael. "A comparison of event models for Naive Bayes anti-spam e-mail filtering." Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics-Volume 1. Association for Computational Linguistics, 2003.