Keywords
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| Sensitive data, Data leakage, Data allocation strategies, Fake records, E-mail filtering. |
INTRODUCTION
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Data leakage is defined as the accidental or unintentional distribution of private or sensitive data to an unauthorized
entity.In the course of doing business, sometimes it is necessary to give organizations sensitive data to trusted third
parties. Sensitive data in companies and organizations includes intellectual property (IP), financial information, patient
information, personal credit-card data, and other information depending on the business and the industry. When these
are leaked out it leaves the company unprotected and goes outside the jurisdiction of the corporation. This uncontrolled
data leakage puts business in a vulnerable position. Once this data is no longer within the domain, then the company is
at serious risk. Data leakage poses a serious issue for companies as the number of incidents and the cost to those
experiencing them continue to increase. |
In the course of doing business, sometimes sensitive data must be handed over to supposedly trusted third parties.
The owner of the data is the distributor and the supposedly trusted third parties are the agents. It may happen that any
of agents gives this sensitive data to any unauthorized user without knowledge of owner. So our goal is to detect when
the distributor's sensitive data has been leaked by agents, and if possible to identify the agent that leaked the data and
what data has been leaked. Data can be leaked through e-mails. So there is a need to provide security to the data. For
this purpose e-mail filtering concept has been implemented in the system. In this, even if any of the agents sends
distributors sensitive data to unauthorized person via e-mail, unauthorized person will not be able to see or download
that e-mail. |
EXISTING SYSTEM
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Traditionally, Watermarking techniques were used for handling data leakage detection. Watermarks were initially
used in images, video or audio data whose digital representation includes considerable redundancy. Watermarking aims
to identify a data owner and, hence, is subject to attacks where a pirate claims ownership of the data or weakens a
merchant’s claims. In watermarking technique, a unique code is embedded in the original data copy. And if that copy of
data later found at unauthorized place or with unauthorized person then the leaker can be identified. But the drawback
of this technique is that- it requires some modification of data. Also in some cases the watermarks can be destroyed if
the data recipient is malicious. Hence there is need to propose efficient technique to find data leakage. |
PROPOSED SYSTEM
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In the proposed system, we develop a model for finding guilty agents. For this purpose different data allocation
strategies are used. We are using “Fake records” which are not real but appear as real records in order to find the guilty
agent. Here these Fake objects acts as a watermarks like in watermarking technique. It improves over the limitations of
watermarking technique as it does not require any modification of original data. If it turns out an agent was given one
or more fake objects that were leaked, then the distributor can be more confident that agent was guilty. Also in
proposed system, we have implemented e-mail filtering technique in which unauthorized users will be unable to see
and download the contents of the e-mails which is send by guilty agent. So Distributors sensitive data remains secure. |
PROBLEM SETUP AND NOTATIONS
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| T is a set of data objects. |
| T= {t1, t2... tn}; |
| Set of agents= {U1, U2... Un} |
| The objects in T could be of any type and size, e.g., they could be tuples in a relation, or relations in a database. |
An agent Ui receives a objects Ri, where Ri is a subset of T, determined either by a sample request or an explicit
request: |
| • Sample request Ri = SAMPLE (T, mi): Any subset of mi records from T can be given to Ui. |
| • Explicit request Ri = EXPLICIT (T, condi): Agent Ui receives all the T objects that satisfy condi. |
We say an agent Ui is guilty and if it contributes one or more objects to the target. We denote the event that agent Ui is
guilty as Gi and the event that agent Ui is guilty for a given leaked set S as Gi|S. |
MODULE DESCRIPTION
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• Fake objects: Fake objects are objects generated by the distributor in order to increase the chances of
detecting agents that leak data. The distributor is capable to add fake objects to the distributed data in order to
improve his effectiveness in detecting guilty agents. The use of fake objects by the system is inspired by the
use of "trace" records in mailing lists. Fake objects are the objects which look exactly like the real objects.
Fake objects are created by the distributor before sending data to agents. In every data which distributor will
send to his agents, the position and number of fake objects will differs. Depending on the number of records
the number of fake objects will differ so that it will be easy for system to detect the guilty agent. |
• Data allocation strategies: The data allocation problem as how can the distributors “intelligently” gives data
to agents in order to improve the chances of detecting a guilty agent. Data allocation depends on the request
done by the agent and whether system can add fake object to it. The request done by the agent can be of two
types: |
| Sample- In sample data request agent receives a subset of distributers data which required by agent. |
| Explicit- In explicit data request data satisfying a special condition is given to agent. |
• E-Mail filtering: The Mail is being sent to authorized user and unauthorized user. As the unauthorized user
receives the mail, the system detects that the mail has been send to the unauthorized user illegally; the system
filters the data and block the contents of the mail. Here, on the user side, if the unauthorized user downloads
that mail, the mail does not display the original contents of the mail and the downloaded file will be of size
zero. |
As shown in fig 1, A distributor can insert original as well as fake records in the Database. A new agent can be
registered by entering personal details. A registered agent can Login and make a request to the distributor for data. The
request can be of two types- Sample or Explicit. The system then extracts the requested data from the main database
and performs the addition of fake records to the set of original records. It then provides this data to the agent. The agent
may pass on this data to an unauthorized party. The agent or the unauthorized party may leak the sensitive data on the
internet, television or other media via email, instant messaging, and webmail or by any other means. Whenever the
distributor discovers the leaked set of his data, he will note the objects present in it. He will compare these objects to
those present in the data sets handed over to different agents. He will list out the names of the agents whose data set contains the objects found in the leaked set. The fake records present in the data sets of the agents will be checked and
the probability of an agent being guilty will be computed for each agent in the list. The agent having the maximum
probability will be the guilty agent. |
CONCLUSION
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To deal with the problem of Data leakage, we have presented implementation a variety of data distribution strategies
that can improve the distributor's chances of identifying a leaker. Also we have implemented the concept of E-mail
filtering to secure the distributors data. Scope of this system can be extended by making provisions for the generation
of fake records dynamically according to the agent’s request. Our future work includes the investigation of agent guilt
models that capture leakage scenarios that are not studied in this paper. |
ACKNOWLEDGEMENT
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The success of any project is never limited to an individual. Similarly, our project is also an outcome of ideas
contributed by many and we would like to gratefully acknowledge them here. We express our sincere thanks to all
those who have provided us with valuable guidance towards the completion of this report as a part of the syllabus of the
degree course. |
We deeply thank our HOD Prof. P. Pankhti for her useful guidance. We also thank our Project Guide Prof. Arti
Bhore without whom this project would have been a distant reality. We also thank them for giving us moral support,
timely comments and discussion in all phases of the project. |
We express our sincere thanks and gratitude to the project coordinator Prof. A. Nadaph for inspiring us throughout
the completion of our project. We would also like to extend our sincere thanks to all teachers and staff for their
valuable suggestions and feedback. |
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Figures at a glance
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| Figure 1 |
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References
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