1. 01-Feb-12Data Leakage Detection1

2. CONTENTS  ABSTRACT  INTRODUCTION  OBJECTIVES  STUDY AND ANALYSIS  FLOW CHART  FUTURE SCOPE  LIMITATIONS  APPLICATIONS  CONCLUSION  REFERENCES 01-Feb-122Data Leakage Detection

3. ABSTRACT  A data distributor has given sensitive data to a set of supposedly trusted agents. Some of the data are leaked and found in an unauthorized place.  The distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means.  We propose data allocation strategies that improve the probability of identifying leakages.  These methods do not rely on alterations of the released data (e.g., watermarks). 01-Feb-123Data Leakage Detection

4. INTRODUCTION  DISTRIBUTER: He is the owner of the data who distributes the data to the third parties.  THIRD PARTIES: Trusted recipient’s of the distributer’s data who are also called as agents.  PERTURBATION: Technique where the data are modified and made less sensitive before being handed to agents.  ALLOCATION STRATEGIES: Tactics used by the distributer to allocate the sensitive data in order to increase the probability of detecting the data leakage. 01-Feb-124Data Leakage Detection

5. OBJECTIVES  Avoiding the perturbation of the original data before being handed to the agents.  Detecting if the distributer’s sensitive data has been leaked by the agents.  The likelihood that an agent is responsible for a leak is assessed. 01-Feb-125Data Leakage Detection

6. STUDY AND ANALYSIS EXISTING SYSTEM  Traditionally, leakage detection is handled by watermarking, e.g., a unique code is embedded in each distributed copy.  If that copy is later discovered in the hands of an unauthorized party, the leaker can be identified. DRAWBACKS OF EXISTING SYSTEM  Watermarking involves some modification of the original data.  Watermarks can sometimes be destroyed if the data recipient is intelligent. 01-Feb-126Data Leakage Detection

7. PROPOSED SYSTEM ALLOCATION STRATEGIES: The proposed system uses two allocation strategies through which the data is allocated to the agents. They are,  Sample request Ri=SAMPLE (T, mi): Any subset of mi records from T can be given to agent.  Explicit request Ri=EXPLICIT (T, condition): Agent receives all T objects that satisfy condition. 01-Feb-127Data Leakage Detection

8. 01-Feb-12Data Leakage Detection8 start User’s explicit request Check the Condition Select the agent. Create Fake Object is Invoked User Receives the Output. end Loop Iterates exit else FLOW CHART:

9. Example:  Say that T contains customer records for a given company A. Company A hires a marketing agency U1 to do an online survey of customers.  Since any customers will do for the survey, U1 requests a sample of 1,000 customer records.  At the same time, company subcontracts with agent U2 to handle billing for all California customers.  Thus, U2 receives all T records that satisfy the condition “state is California.” 01-Feb-12Data Leakage Detection9

10. FUTURE SCOPE  Future work includes the investigation of agent guilt models that capture leakage scenarios.  The extension of data allocation strategies so that they can handle agent requests in an online fashion. 01-Feb-1210Data Leakage Detection

11. LIMITATION  The presented strategies assume that there is a fixed set of agents with requests known in advance.  The distributor may have a limit on the number of fake objects. 01-Feb-1211Data Leakage Detection

12. APPLICATIONS  It helps in detecting whether the distributer’s sensitive data has been leaked by the trustworthy or authorized agents.  It helps to identify the agents who leaked the data.  Reduces cybercrime. 01-Feb-1212Data Leakage Detection

13. CONCLUSION  Though the leakers are identified using the traditional technique of watermarking, certain data cannot admit watermarks.  In spite of these difficulties, we have shown that it is possible to assess the likelihood that an agent is responsible for a leak.  We have shown that distributing data judiciously can make a significant difference in identifying guilty agents using the different data allocation strategies. 01-Feb-1213Data Leakage Detection

14. REFERENCES [1] P. Buneman and W.-C. Tan, “Provenance in Databases,” Proc. ACM SIGMOD, pp. 1171- 1173, 2007. [2] Y. Cui and J. Widom, “Lineage Tracing for General Data Warehouse Transformations,” The VLDB J., vol. 12, pp. 41-58, 2003. [3] S. Czerwinski, R. Fromm, and T. Hodes, “Digital Music Distribution and Audio Watermarking,” http://www.scientificcommons. org/43025658, 2007.http://www.scientificcommons [4] F. Guo, J. Wang, Z. Zhang, X. Ye, and D. Li, “An Improved Algorithm to Watermark Numeric Relational Data,” Information 01-Feb-1214Data Leakage Detection

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