cloud fuzzy search

SSPM’sSOMESHWAR ENGINEERING COLLEGE

SOMESHWARNAGAR Department of Computer Engineering

Seminar On

“Privacy-preserving Multi-keyword Fuzzy Search Over Encrypted Data In The Cloud”

Guide Name: Presented By, Prof. Shinde V.D. Miss-Limbarkar Archana

Roll no:-22

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CONTENTS

IntroductionProblem definitionExisting SystemLiterature SurveyDisadvantages of Existing SystemArchitectureMethodologyMathematical ModelAdvantages/FeaturesConclusionReferences

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INTRODUCTION

Cloud Computing

Scalable

Elastic Storage

Computation Resources

Outsourcing Data Services.

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PROBLEM DEFINITION

High privacy

Keyword collection

Search and a trapdoor

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EXISTING SYSTEM

Cloud computing

Ranking to meet the effective data retrieval need.

Searchable encryption

Cloud computing contains more private and sensitive information’s such as emails,

government documents, personnel records

MRSE technique

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LITERATURE SURVEY

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DISADVANTAGES OF EXISTING SYSTEM

Secure ranked search over encrypted data problem.

Efficient encrypted data search mechanism.

Privacy problem.

Time delay.

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ARCHITECTURE

17/03/16 8Fig-System Architecture of Search over Encrypted data on cloud computing

METHODOLOGY

1.KeyGen(m)-Key Generation

2.Index Enc(SK; I)-Index Encryption

3.Query Enc(SK;Q)-Query Encryption

4.BuildIndex(D; SK; l)-Index Building

5.Trapdoor(Q; SK)-Trapdoor Generation

6.Search(EncSK(Q);EncSK(ID))-Searching

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MATHEMATICAL MODEL

Let us consider S as a set of inputs.

S= { }

INPUT:

Identify the inputs as keyword transformation

information

F= {f1, f2, f3 ....., fn| ‘F’ as set of functions to execute the process}

I= {i1, i2, i3…|’I’ sets of inputs to the function set}

O= {o1, o2, o3….|’O’ Set of outputs from the function sets}

S= {I, F, O}

I = {keyword transformation information}

O = {Index,Query}

F = {Build index,trapdoor generation}

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ADVANTAGES

Multi-Keyword Fuzzy Search

Privacy Guarantee

Result Accuracy

No Predefined Dictionary

Security

Performance

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FEATURES

Efficiency

Enhanced Scheme

F-measure

Security Analysis of Enhanced Scheme

Performance Analysis

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CONCLUSION

In this seminar, we tackled the challenging multi-keyword fuzzy search problem over the encrypted data.

We proposed and integrated several innovative designs to solve the multiple keywords search and the fuzzy search problems simultaneously with high efficiency.

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REFERENCES

1. D. Song, D. Wagner, and A. Perrig, “Practical techniques for searches on encrypted

data,” S&P 2000, vol. 8, pp. 44–55, 2000.

2.W. Sun, B. Wang, N. Cao, M. Li, W. Lou, T. Hou, and H. Li, “Privacypreserving multi-

keyword text search in the cloud supporting similaritybased ranking,” in ASIACCS

2013, May 2013.

3.D. Boneh, G. D. Crescenzo, R. Ostrovsky, and G. Persiano, “Public key encryption with

keyword search,” EUROCRYPTO 2004, pp. 506–522,2004.

4. N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, “Privacy-preserving multi-keyword

ranked search over encrypted cloud data,” INFOCOM 2011, pp. 829–837, 2011

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CONTINUES….

1.Y. Hwang and P. Lee, “Public key encryption with conjunctive keyword

search and its extension to a multi-user system,” Pairing 2007, pp. 2–

22,2007.

2. D. Boneh and B. Waters, “Conjunctive, subset, and range queries on

encrypted data,” Theory of Cryptography, vol. 4392, pp. 535–554, 2007.

3.P. Golle, J. Staddon, and B. Waters, “Secure conjunctive keyword search

over encrypted data,” ACNS 2004, vol. 3089, pp. 31–45, 2004.

4.C. Wang, N. Cao, J. Li, K. Ren, and W. Lou, “Secure ranked keyword

search over encrypted cloud data,” ICDCS 2010, pp. 253–262, 2010.17/03/16 15

QUESTIONS???

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THANK YOU…!!!

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