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Experimental Results of Data Leakage Detection
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EXPERIMENTAL RESULTS OF DATA LEAKAGE
DETECTION
In this section we provide experimental results of our project. The main thing in
experimentation is to estimate how much different allocation techniques, that we have
implemented, are efficient in identifying the correct ‘guilty’ agent.
Experimental Setup:
Before going to undertake experimentation let us consider following experimental
arrangement:
1. There is one distributor
2. There are four agents U1, U2, U3, U4
3. There is one customer
4. Distributor has total 30 profiles in his database ( i.e. |T| = 30 )
Experiments:
So to begin with, we have to consider different cases for both ‘Sample Request’ and ‘Explicit
Request’. We show results in terms of graph.
A) Sample Request
Here we have to test implementation of both ‘s-random’ and ‘s-overlap’ algorithms.
Case I: When M > |T|, where M = ∑i=1,..,n mi
( i.e. total of number of profiles requested by all agents is more than available profiles)
Let us consider following scenario:
Agent
(Ui)
Number of profiles requested
from distributor (mi)
Number of profiles given to
customer
U1 8 5
U2 7 -
U3 10 10
U4 10 -
Table 7.1. Scenario 1 For Sample Request
Here, M = 35. i.e. M > |T|
After executing the ‘Guilt Model’ on above scenario we get following results.
1) Result for s-random:
Graph 1.1.1: Guessing Probability (p) = 0.3
Graph 1.1.2: For all values (0 to 1) of Guessing Probability (p)
2) Result for s-overlap:
Graph 1.2.1: Guessing Probability (p) = 0.3
Graph 1. 2.2: For all values (0 to 1) of Guessing Probability (p)
By considering Scenario No.1( shown in Table 1.1) and the results obtained for both of
allocation strategies, s-random and s-overlap, it becomes clear that agents U3 and U1 are ‘more
guilty’ than other two agents U2 and U4. To be more specific, agent U3 is guiltier than others.
Case II: When M < |T|, where M = ∑i=1,..,n mi
( i.e. total of number of profiles requested by all agents is less than available profiles)
Let us consider following scenario:
Agent
(Ui)
Number of profiles requested
from distributor (mi)
Number of profiles
given to customer
U1 8 8
U2 7 -
U3 8 5
U4 6 -
Table 7.2. Scenario 2 For Sample Request
Here, M = 29. i.e. M < |T|
After executing the ‘Guilt Model’ on above scenario we get following results, we show results in
terms of graph.
1) Result for s-random:
Graph 2.1.1: Guessing Probability (p) = 0.3
Graph 2.1.2: For all values (0 to 1) of Guessing Probability (p)
2) Result for s-overlap:
Graph 2.2.1: Guessing Probability (p) = 0.3
Graph 2.2.2: For all values (0 to 1) of Guessing Probability (p)
By considering Scenario No.2( shown in Table 2.1) and the results obtained for both of
allocation strategies, s-random and s-overlap, it becomes clear that agents U1 and U3 are ‘more
guilty’ than other two agents U2 and U4. To be more specific, agent U1 is guiltier than others.
From several experimental results for above two cases, it also becomes clear that, the ‘s-
overlap’ allocation strategy gives more accurate results than ‘s-random’ strategy.
B) Explicit Request
In this, we have to consider the case in which distributor doesn’t use the fake object(s) (i.e. fake
profile(s)); in addition to case where it makes use of them. And also we provide results for both
implementations of algorithms ‘e-random’ and ‘e-optimal’.
Let us consider following scenario:
Agent (Ui) Condition 1 Condition 2 Number of profiles
requested from
distributor (mi)
Number of
profiles given
to customer
U1 Kolhapur ME 8 -
U2 Kolhapur BCS 8 8
U3 Kolhapur MCom 8 -
U4 Kolhapur MTech 8 -
Table 7.3 Scenario For Explicit Request
Case I: Distributor doesn’t use fake object
Graph: Guessing Probability (p) = 0.3
Graph: For all values (0 to 1) of Guessing Probability (p)
The result shows that, it is very difficult to identify the correct leaker if the distributor do
not makes use of fake object(s). This is because all agents receive same set of objects.
Now, let’s see the results when the distributor adds some fake objects to set of objects
received by individual agents.
Case II: Distributor uses fake objects
1) Result for e-random
Graph: Guessing Probability (p) = 0.3
Graph: For all values (0 to 1) of Guessing Probability (p)
In e-random, as we have mentioned earlier, while allocating fake object, agent is chosen
randomly from the set of agents that can receive fake objects. Looking at above two results it is
quite clear that we get improved results as compared to the case-I. But, still this result fails to
clearly distinguish between ‘guilty agent’ and ‘innocent agent’, that is here we get the same
‘guilt probability’ values for agents U1, U3 and U2, U4. This happened because of random
selection of agent.
2) Result for e-optimal
Here, instead of selecting randomly, agent is selected (for fake object allocation) in such a way
that will improve our chances to identify him/her if it leaks data.
Graph: Guessing Probability (p) = 0.3
Graph: For all values (0 to 1) of Guessing Probability (p)
From result, we are sure that agent U2 is the leaker.
Youtube Video : Click Here If anyone interested to buy the project, then contact on : [email protected]