Sensitive data hiding with the mining of association rules
Keywords:
Association rule, sensitive pattern, Privacy preserving data Mining (PPDM), DSRRC (Decrease Support of R.H.S item of Rule Clusters), MDSRRC (Modified Decrease Support of R.H.S. item of Rule Clusters),FADSRRC (Fast Algorithm for Decrease Support of R.H.S. item of Rule Clusters).Abstract
In this Project we are applying a heuristic based algorithm named FADSRRC (Fast Algorithm for Decrease
Support of R.H.S item of Rule Clusters) to hide the sensitive association rules from sensitive item set with multiple items in
subsequent (R.H.S) and precedent (L.H.S). This algorithm overcomes the limitation of existing rule hiding algorithm
DSRRC andMDSRRC. PPDM techniques are helpful to enhance the security of database. FADSRRC algorithm selects the
items and transactions according to some circumstances which remodels transactions to prevent the sensitive information
from getting leaked and by using FADSRRC algorithm we can hide more sensitive data from sensitive item sets. The
proposed FADSRRC algorithm is highly efficient and conserves the virtue of database. And also it gives more accuracy
than both the algorithm MDSRRC and DSRRC.
Association rule hiding problem can be defined as: convert the original database into sanitized database so that data
mining techniques will not be able to mine sensitive rules from the database while all non-sensitive rules remain visible.
Association rule mining technique is widely used in data mining to find consociation between item sets.
