cloud computing using fuzzy search

1. SSPM’s
SOMESHWAR 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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2. CONTENTS
Introduction
Problem definition
Existing System
Literature Survey
Disadvantages of Existing System
Architecture
Methodology
Mathematical Model
Advantages/Features
Conclusion
References
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3. INTRODUCTION
Cloud Computing
 Scalable
 Elastic Storage
 Computation Resources
Outsourcing Data Services.
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4. PROBLEM DEFINITION
 High privacy
Keyword collection
Search and a trapdoor
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5. 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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6. LITERATURE SURVEY
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7. DISADVANTAGES OF EXISTING SYSTEM
 Secure ranked search over encrypted data problem.
Efficient encrypted data search mechanism.
Privacy problem.
Time delay.
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8. ARCHITECTURE
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Fig-System Architecture of Search over Encrypted data on cloud computing

9. 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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10. 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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11. ADVANTAGES
Multi-Keyword Fuzzy Search
Privacy Guarantee
Result Accuracy
No Predefined Dictionary
Security
Performance
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12. FEATURES
Efficiency
Enhanced Scheme
F-measure
Security Analysis of Enhanced Scheme
Performance Analysis
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13. 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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14. 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.
1.D. Boneh, G. D. Crescenzo, R. Ostrovsky, and G. Persiano, “Public key encryption with
keyword search,” EUROCRYPTO 2004, pp. 506–522,2004.
2. 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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15. 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.
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16. QUESTIONS???
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17. THANK YOU…!!!
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