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01-Feb-12Data Leakage Detection1CONTENTSABSTRACTINTRODUCTIONOBJECTIVESSTUDY AND ANALYSISFLOW CHARTFUTURE SCOPELIMITATIONSAPPLICATIONSCONCLUSIONREFERENCES01-Feb-122Data Leakage DetectionABSTRACTA 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 DetectionINTRODUCTIONDISTRIBUTER: He is the owner of the data who distributes the data to the third parties.

THIRD PARTIES: Trusted recipients of the distributers 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 DetectionOBJECTIVESAvoiding the perturbation of the original data before being handed to the agents.

Detecting if the distributers sensitive data has been leaked by the agents.

The likelihood that an agent is responsible for a leak is assessed.

01-Feb-125Data Leakage DetectionSTUDY AND ANALYSISEXISTING SYSTEMTraditionally, 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 SYSTEMWatermarking involves some modification of the original data.

Watermarks can sometimes be destroyed if the data recipient is intelligent.01-Feb-126Data Leakage DetectionPROPOSED 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 Detection7 01-Feb-12Data Leakage Detection8startUsers explicit request

Check the Condition Select the agent. Create Fake Object is Invoked

User Receives the Output.

endLoop Iteratesexit elseFLOW CHART: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 Detection9FUTURE SCOPEFuture 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 DetectionLIMITATIONThe 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 DetectionAPPLICATIONSIt helps in detecting whether the distributers 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 DetectionCONCLUSIONThough 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 DetectionREFERENCES[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.[4] F. Guo, J. Wang, Z. Zhang, X. Ye, and D. Li, An Improved Algorithm to Watermark Numeric Relational Data, Information01-Feb-1214Data Leakage DetectionTHANK YOU 01-Feb-1215Data Leakage Detection