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Differentially Private Frequent Item set Mining via Transaction Splitting
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Differentially Private Frequent Item set Mining via Transaction Splitting

Category : Data Mining


Sub Category : JAVA


Project Code : ITJDM13


Project Abstract

Differentially Private Frequent Item set Mining via Transaction Splitting

 

ABSTRACT:-

       In this paper, we explore the possibility of designing a differentially private FIM algorithm which can not only achieve high data utility and a high degree of privacy, but also offer high time efficiency. To this end, we propose a differentially private FIM algorithm based on the FP-growth algorithm, which is referred to as PFP-growth. The PFP-growth algorithm consists of a preprocessing phase and a mining phase. In the preprocessing phase, to improve the utility and privacy tradeoff, a novel smart splitting method is proposed to transform the database. For a given database, the preprocessing phase needs to be performed only once. In the mining phase, to offset the information loss caused by transaction splitting, we devise a run-time estimation method to estimate the actual support of item sets in the original database. In addition, by leveraging the downward closure property, we put forward a dynamic reduction method to dynamically reduce the amount of noise added to guarantee privacy during the mining process. Through formal privacy analysis, we show that our PFP-growth algorithm is ϵ-differentially private. Extensive experiments on real datasets illustrate that our PFP-growth algorithm substantially outperforms the state-of-the-art techniques.
 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

Existing work presents an APriori-based differentially private FIM algorithm. It enforces the limit by truncating transactions (i.e., if a transaction has more items than the limit, deleting items until its length is under the limit).

 In particular, in each database scan, to preserve more frequency information, it leverages discovered frequent item sets to re-truncate transactions.

PROPOSED CONCEPT: -

We propose our private FP-growth (PFP-growth) algorithm, which consists of a preprocessing phase and a mining phase.

In the mining phase, a run-time estimation method is proposed to offset the information loss incurred by transaction splitting.

EXISTING TECHNIQUE:-

Frequent Item Set Mining (FIM) Algorithm.

PROPOSED TECHNIQUE:-

FP-Growth(PFP-Growth) Algorithm.

TECHNIQUE DEFINITION:-

FIM tries to find item sets that occur in transactions more frequently than a given threshold.

 If the data is sensitive (e.g., web browsing history and medical records), releasing the discovered frequent item sets might pose considerable threats to individual privacy.

TECHNIQUE DEFINITION:-

FP-growth is a partitioning-based, depth-first search algorithm. It adopts a divide-and-conquer manner to decompose the mining task into many smaller tasks for finding frequent item sets in conditional pattern bases.

FP-growth constructs a FP-Tree, FP tree for the database. For the frequent items in each transaction, they are arranged according to the order of HT and inserted into FP-Tree as a branch.

DRAWBACKS:-

In this process can’t split long transaction, so the transaction time is taking long period.

It can’t split method to transform the database.

ADVANTAGES:-

We devise a smart splitting method to transform the database.

We divide into multiple subsets (i.e., sub-transactions) and guarantee each subset is under the limit.


 
 
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