Cis-601 Graduate Seminar

Privacy-Preserving Using

Data mining Technique

in Cloud Computing

Submitted by: Rajan Sharma

CSU ID: 2659829

Outline

� Introduction

� Related work

� Preliminaries

� Association Rule Mining with item privacy

� Association Rule Mining with transaction privacy

� Association Rule mining with Database privacy

� Performance Analysis

� Conclusion and future work

Introduction

� Study about the customer behavior

� The discovery of frequent patterns,

association rules, correlation among

huge amount of data is useful to business

intelligence.

This Photo by Unknown Author is licensed under CC BY-SA

Cloud Computing

� The Goal of cloud computing is to

allow users to take benefits from

all these technologies, without

need for deep knowledge.

� The Cloud aims to cut the cost,

and help the user to focus on their

core business instead of being

impeded by IT obstacles.

Data mining-as-a-service in cloud

computing

� In this paradigm, a company (data owner), lacking data storage, computational resources and expertise, stores its data in the cloud and outsources its mining tasks to the cloud service provider (server).

� The association rules mined, k-anonymity, k-support, and k-privacy techniques have been proposed to perturb the data before it is uploaded to the server.

� These techniques are computationally expensive.

� To mine association rules from its data, the user outsources the task to n(≥2) “semi-honest” servers, which co-operate to perform association rule mining on the encrypted data in the cloud and return encrypted association rules to the user.

� we provide three solutions to protecting data privacy during association rule mining.

Data mining-as-a-service in cloud

computing

� Solutions are built on the distributed ElGamal cryptosystem and achieve item

privacy, transaction privacy and database privacy, respectively, as long as at

least one out of the n servers is honest.

� To protect data privacy, the data owner employs the ElGamal cryptosystem to

encrypt all items in a transaction, i.e., the data owner generates its

public/private key pair and then encrypts all items with the public key,

before uploading the transaction onto the cloud.

� When outsourcing the data mining task, the data owner chooses n(n≥2)

different servers in the cloud. It splits its private key into n pieces and

distributes them to the n servers, respectively. The private key is secure as

long as not all the n servers collude

THREE Solutions

Plaintext Equality

Test (PET)

Fake Transactions

Server Counts

Plaintext Equality

Test

� In this n servers can cooperate to

determine the equality of two plaintexts

on the basis of their encryptions without

the need for decryption.

� The basic idea is using PET to identify the

encryptions of the same item in the

encrypted transactions and replace them

with the same encryption.

� Then the Aprori algorithm is applied to the

replacement of the transaction database.

Plaintext Equality

Test

� Problem with this solution that does not hide

the support of each Item set and therefore it

may be vulnerable to the background

knowledge-based attack.

Fake Transaction

� “Wong et el” in 2007 was the first man who

addressed the knowledge based attack and

came up with some idea to prevent it.

� Idea was One to one n item mapping that

transform transactions non deterministically.

� Adding a fake items Into transaction Database.

Fake Transaction

� Example

� I is the set of items in the original database and mis

� a one-to-one mapping from items to integers. The data owner adds a set of fake items F to the dictionary

� J(i.e., |J|=|I|+|F|) and

� maps each item x on to M(x) = {m(x)} ∪ f, where f is a random

� subset of F will be I={a, b, c},m(a) =1, m(b)=2, m(c)=3and F={4,5}, a possible one-to-n item

� mapping M can be defined as M(a) = {1,4,5},M(b) = {2},

� M(c) = {3,5},M(a, b) = M(a)∪M(b) = {1,2,4,5}.