An Efficient Guilt Detection Approach for Identifying Data Leakages
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International Journal of Computer Trends and Technology (IJCTT) | 
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| © 2015 by IJCTT Journal | ||
| Volume-25 Number-2 |
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| Year of Publication : 2015 | ||
| Authors : Anand Kiran | ||
| DOI : 10.14445/22312803/IJCTT-V25P111 |
Anand Kiran "An Efficient Guilt Detection Approach for Identifying Data Leakages". International Journal of Computer Trends and Technology (IJCTT) V25(2):62-67, July 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
In this paper we develop a model for
assessing the “guilt” of agents. We also present algorithms
for distributing objects to agents, in a way that improves our
chances of identifying a leaker. Finally, we also consider the
option of adding “fake” objects to the distributed set. Such
objects do not correspond to real entities but appear
realistic to the agents. In a sense, the fake objects acts as a
type of watermark for the entire set, without modifying any
individual members. 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.
A distributor owns a set T = {t1, t2, . . . , tm} of
valuable data objects. The distributor wants to share some
of the objects with a set of agents U1, U2,… Un, but does
not wish the objects be leaked to other third parties. 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 subset of objects Ri ? 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.
A data distributor has given sensitive data to a set of
supposedly trusted agents (third parties). Some of the
data is leaked and found in an unauthorized place (e.g.,
on the web or somebody’s laptop). 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.
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Keywords
Fake Object, Guilty Agent, Data Object,
Third Party, Watermark, Data Warehousing.