Data Leakage Detection And e-mail Filtering

Data Leakage Detection And e-mail Filtering By: Vishal Patil Paresh Rawat Pratik Nikam Satish Patil Under The Guidance Of Prof.Rucha Samant

Agenda • PROBLEM DEFINITION • INTRODUCTION • ISSUES • SCOPE • ANALYSIS • DESIGN • IMPLEMENTATIONS

Problem Definition • To detect whether data has been leaked by agents. • To prevent data leakage .

Introduction In the course of doing business, sometimes sensitive data must be handed over to supposedly trusted third parties. 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.

Existing System • Watermarking

Proposed System • In this system the leakage of data is detected by generating fake objects . • Data leakage prevention and detection of guilty agents is handled by e-mail filtering.

Types of employees that put your company at risk • The security illiterate • The gadget nerds • The unlawful residents • The malicious/disgruntled employees

Scope

Analysis Problem Setup and Notation • Distributor (D) is a system which will distribute data to agents • Valuable Data (T) is the set of sensitive data which the system is going to send to the agents • Agent (U) is the set of agents to whom the system is going to send sensitive data. • Request from client will be either sample request or explicit request.

Allocation Strategies Explicit Data Requests 1. Distributor having data T={t1,t2} 2. Agent request (R ) R1= {t1, t2} R2= {t1} R1 gets both data t1 and t2 R2 gets data t1 Therefore value of sum objective. R1+ R2 2/2 + 1/2 = 1.5 3. Select agent using Randomize function algorithm. SelectAgent {R1,…….,R2} 4. E-optimal solution O(n+n2B)= O(n2B) Where n= number of agents, B= number of Fake objects. In this algorithm, the agent receives the entire data object that satisfies the condition of the agents’ data request. The following algorithm shows the working of Explicit Data Request:

Sample Data Requests With sample data requests, agents are not interested in particular objects. In this algorithm, the agent receives only the subset of data object that can be given. The working of Sample Data Request algorithm is same as the working of Explicit Data Request.

ARCHITECTURE DIAGRAM: Requesting sensitive data Data Distributor Agents Requesting Secured data from the Data Distributor Agents

ARCHITECTURE DIAGRAM: Sensitive data is sent Data Distributor Data distributor sending the secured data to the agents Agents

Agent tries to leak the sensitive data The Sent e-mail don’t contains fake Object/Watermarks. Internet Forwarded to the outside world

Agent tries to leak the sensitive data The Sent e-mail contains fake Object/Watermarks. Internet Infected e-mail containing fake object Notification is sent

Implementation The system has the following Data Allocation -- approach same as watermarking -- less sensitive -- add fake object in some cases Fake Object -- Are real looking object -- Should not affect data -- Limit on fake object insertion(e-mail inbox) -- CREATEFAKEOBJECT (Ri, Fi, CONDi)

Optimization -- One constraint and one objective -- Maximize the probability difference Data Distributor e-mail Filtering

e-mail Filtering Algorithm: Identify the data. Remove spamming stopping words. Remove or change the synonyms. Calculate the priority of the word depending upon the sensitivity of the data. Compare data with predefine company data sets. Filter the data if it has company’s important data sets.

e-mail filtering architecture Agent Attached data is not sensitive data Attached data is a sensitive data E-mail sent successfully E-mail not sent as the data it contains is sensitive

Preferred technologies Hardware requirement O/S : Windows XP. Language : Asp.Net, c#. Data Base : Sql Server 2005 System : Pentium IV 2.4 GHz Hard Disk : 40 GB Monitor : 15 VGA colour Mouse : Logitech. Keyboard : 110 keys enhanced. RAM : 256 MB

Conclusion In the real scenario there is no need to hand over the sensitive data to the agents who will unknowingly or maliciously leak it. However, in many cases, we must indeed work with agents that may not be 100 percent trusted, and we may not be certain if a leaked object came from an agent or from some other source. In spite of these difficulties, it is possible to assess the likelihood that an agent is responsible for a leak, based on the overlap of his data with the leaked data . The algorithms we have presented implement a variety of data distribution strategies that can improve the distributor’s chances of identifying a leaker.

References • R. Agrawal and J. Kiernan, “Watermarking Relational Databases,”Proc. 28th Int’l Conf. Very Large Data Bases (VLDB ’02), VLDB.Endowment, pp. 155-166, 2002. • S. Czerwinski, R. Fromm, and T. Hodes, “Digital Music Distribution and Audio Watermarking,” http://www.scientificcommons. org/43025658, 2007. • F. Guo, J. Wang, Z. Zhang, X. Ye, and D. Li, “An Improved Algorithm to Watermark Numeric Relational Data,” Information Security Applications, pp. 138-149, Springer, 2006. • S. Jajodia, P. Samarati, M.L. Sapino, and V.S. Subrahmanian, “Flexible Support for Multiple Access Control Policies,” ACM Trans. Database Systems, vol. 26, no. 2, pp. 214-260, 2001. • Panagiotis Papadimitriou and Hector Garcia-Molina, “Data Leakage Detection,” IEEE Transactions on Knowledge and Data Engineering, Vol 23, No.1 January 2011. • B. Mungamuru and H. Garcia-Molina, “Privacy, Preservation and Performance: The 3 P’s of Distributed Data Management,” technical report, Stanford Univ., 2008.

Data Leakage Detection And e-mail Filtering