Privacy-Preserving Using Data mining Technique in Cloud Computing

Transcription

1 Cis-601 Graduate Seminar Privacy-Preserving Using Data mining Technique in Cloud Computing Submitted by: Rajan Sharma CSU ID:

2 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

3 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

4 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.

5 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.

6 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

7 THREE Solutions Plaintext Equality Test (PET) Fake Transactions Server Counts

8 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.

9 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.

10 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.

11 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 oneto-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}.