Fuzzy Keyword Search over Encrypted
Data in Cloud Computing
作者:Jin Li, Qian Wang, Cong Wang, Ning Cao, Kui Ren, and Wenjing Lou
出處:IEEE Transactions on Knowledge and Data Engineering(2011)
日期:2012/05/15
報告人:葉瑞群
Outline
 1. INTRODUCTION
 2. RELATED WORK
 3. PROBLEM FORMULATION
 4. THE STRAIGHTFORWARD APPROACH
 5. CONSTRUCTIONS OF EFFECTIVE FUZZY
KEYWORD SEARCH IN CLOUD
 6. CONCLUSION
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1. INTRODUCTION(1/2)
 As Cloud Computing becomes prevalent, more and more
sensitive information are being centralized into the cloud.
 Although traditional searchable encryption schemes allow a
user to securely search over encrypted data through
keywords and selectively retrieve files of interest,these
techniques support only exact keyword search.
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1. INTRODUCTION(2/2)
 On the other hand, are typical user searching behavior and
happen very frequently. This significant drawback makes
existing techniques unsuitable in Cloud Computing as it
greatly affects system usability, rendering user searching
experiences very frustrating and system efficacy very low.
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2. RELATED WORK(1/2)
 Enabling fuzzy keyword search service that aims at
accommodating various typos and representation
inconsistencies in different user searching inputs is of
crucial importance for the high system usability and
overall user search experience.
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2. RELATED WORK(2/2)
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3. PROBLEM FORMULATION(1/4)
 We consider a cloud data system consisting of data owner,
data user and cloud server. Given a collection of n encrypted
data files C = (F1, F2, . . . , FN) stored in the cloud server, a
predefined set of distinct keywords W = {w1, w2, ...,wp}, the
cloud server provides the search service for the authorized
users over the encrypted data C.
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3. PROBLEM FORMULATION(2/4)
 Edit Distance:
The edit distance ed(w1, w2) between two words w1 and w2 is
the number of operations required to transform one of them
into the other.
1) Substitution:changing one character to another in a word;
2) Deletion:deleting one character from a word;
3) Insertion:inserting a single character into a word.
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3. PROBLEM FORMULATION(3/4)
 Edit Distance Example:
AAACAGC→AAATTGAGTC (distance=4)
AAACAGC
AAAGAGTC
AAATGAGTC
AAATTGAGTC
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3. PROBLEM FORMULATION(4/4)
 Fuzzy Keyword Search
1.C=(F1,F2,…,Fn)
2.W={W1,W2,…,Wn}
3.Edit distance d
4.A searching input (w, k) (k<=d)
 Π=(Setup(1λ), Enc(sk, ·), Dec(sk, ·))
 Twi = f(sk,wi)
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4. THE STRAIGHTFORWARD
APPROACH(1/2)
Data Owner
FIDwi Enc(sk,FIDwi||wi)
{({Tw’i}w’i ∈Swi,d，Enc(sk,FIDwi||wi))}wi∈W
Server
Tw = f(sk,wi) 、w
Users
Enc(sk,FIDwi||wi)
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4. THE STRAIGHTFORWARD
APPROACH(2/2)
 Assume
d=1
d=2
2k*26
2k2*26
 For example, assume there are 104 keywords in the file
collection with average keyword length 10, d = 2,and the
output length of hash function is 160 bits, then, the resulted
storage cost for the index will be 30GB. Therefore, it brings
forth the demand for fuzzy keyword sets with smaller size.
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5. CONSTRUCTIONS OF EFFECTIVE FUZZY
KEYWORD SEARCH IN CLOUD(1/3)
 For example,for the keyword CASTLE with the pre-set edit
distance 1, SCASTLE,1 = {CASTLE, *CASTLE, *ASTLE,
C*ASTLE,C*STLE, · · · , CASTL*E, CASTL*, CASTLE*}.
13 × 26 + 1 = (2 L+ 1) × 26 + 1
 The wildcard-based fuzzy set of wi with edit distance d is
denoted as Swi,d={Swi,0, Swi,1, · · · , Swi,d}.
d=1 (2L+1)*26+1
d=2 C1L+1+C1L*C1L+2C2L+2
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5. CONSTRUCTIONS OF EFFECTIVE FUZZY
KEYWORD SEARCH IN CLOUD(2/3)
 Reduce the storage of the index from 30GB to approximately
40MB.
 Π=(Setup(1λ), Enc(sk, ·), Dec(sk, ·))
 Twi = f(sk,wi)
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Tw’i= f(sk,w’i) for each w’i∈ Swi,d
5. CONSTRUCTIONS OF EFFECTIVE FUZZY
KEYWORD SEARCH IN CLOUD(3/3)
Data Owner
FIDwi Enc(sk,FIDwi||wi)
{({Tw’i}w’i ∈Swi,d，Enc(sk,FIDwi||wi))}wi∈W
Server
{Tw’}w’ ∈ Sw,k
Users
Enc(sk,FIDwi||wi)
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6. CONCLUSION(1/1)
 We design an advanced technique (i.e., wildcard-based
technique) to construct the storage-efficient fuzzy keyword
sets by exploiting a significant observation on the similarity
metric of edit distance. Based on the constructed fuzzy
keyword sets, we further propose an efficient fuzzy keyword
search scheme. Through rigorous security analysis, we show
that our proposed solution is secure and privacy-preserving,
while correctly realizing the goal of fuzzy keyword search.
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END
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