Secure Two-Party Differentially Private Data Release for

Vertically Partitioned Data

ABSTRACT:

Privacy-preserving data publishing addresses the problem of disclosing sensitive data when

mining for useful information.Among the existing privacy models, _-differential privacy

provides one of the strongest privacy guarantees. In this paper, we addressthe problem of private

data publishing, where different attributes for the same set of individuals are held by two parties.

In particular, wepresent an algorithm for differentially private data release for vertically

partitioned data between two parties in the semihonest

adversary model. To achieve this, we first present a two-party protocol for the exponential

mechanism. This protocol can be used as asubprotocol by any other algorithm that requires the

exponential mechanism in a distributed setting. Furthermore, we propose a twopartyalgorithm

that releases differentially private data in a secure way according to the definition of secure

multiparty computation.Experimental results on real-life data suggest that the proposed

algorithm can effectively preserve information for a data mining task.

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EXISTING SYSTEM:

Among the existing privacy models, _-differential privacy provides one of the strongest privacy

guarantees. In this paper, we addressthe problem of private data publishing, where different

attributes for the same set of individuals are held by two parties. In particular, wepresent an

algorithm for differentially private data release for vertically partitioned data between two parties

in the semihonestadversary model

PROPOSED SYSTEM:

we propose a twopartyalgorithm that releases differentially private data in a secure way

according to the definition of secure multiparty computation.

Experimental results on real-life data suggest that the proposed algorithm can effectively

preserve information for a data mining task. In this paper, we adopt differential privacy arecently

proposed privacy model that provides a provableprivacy guarantee. Differential privacy is a

rigorous privacymodel that makes no assumption about an adversary’sbackground knowledhave

proposed a top-down specialization(TDS) approach to generalize a data table. LeFevre et al.

have proposed another anonymization technique forclassification using multidimensional

recoding .we show that the proposed two-party algorithmprovides similar data utility for

classification analysiswhen compared to the single-party algorithmand it performs better than the

recently proposed two-party algorithm

CONCLUSION:

In this paper, we have presented the first two-partydifferentially private data release algorithm

for verticallypartitioned data. We have shown that the proposedalgorithm is differentially private

and secure under thesecurity definition of the semihonest adversary model.Moreover, we have

experimentally evaluated the datautility for classification analysis. The proposed algorithmcan

effectively retain essential information for classificationanalysis. It provides similar data utility

compared to the

recently proposed single-party algorithm [38] and betterdata utility than the distributed k-

anonymity algorithm forclassification analysis

SYSTEM CONFIGURATION:-

HARDWARE CONFIGURATION:-

Processor - Pentium –IV

Speed - 1.1 Ghz

RAM - 256 MB(min)

Hard Disk - 20 GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA

SOFTWARE CONFIGURATION:-

Operating System : Windows XP

Programming Language : JAVA

Java Version : JDK 1.6 & above.