Security Analysis on One-to-Many Order Preserving
Encryption Based Cloud Data search
Abstract
For ranked search in encrypted cloud data, order preserving encryption
(OPE) is an efficient tool to encrypt relevance scores of the inverted index.
When using deterministic OPE, the ciphertexts will reveal the distribution of
relevance scores. Therefore, Wang et al. proposed a probabilistic OPE, called
One-to-Many OPE, for applications of searchable encryption, which can
flatten the distribution of the plaintexts.In this paper, we proposed a
differential attack on One-to-Many OPE by exploiting the differences of the
ordered ciphertexts.The experimental results show that the cloud server can
get a good estimate of the distribution of relevance scores by a differential
attack. Furthermore, when having some background information on the
outsourced documents, the cloud server can accurately infer the encrypted
keywords by using the estimated distributions.
Existing System:
We have proposed, implemented and evaluated an initiative data
prefetching approach on the storage servers for distributed file systems, which
can be employed as a backend storage system in a cloud environment that
may have certain resource-limited client machines.To be specific, the storage
servers are capable of predicting future disk I/O access to guide fetching data
in advance after analyzing the existing logs, and then they proactively push
the prefetched data to relevant client file systems for satisfying future
applications’ requests. For the purpose of effectively modeling disk I/O
access patterns and accurately forwarding the prefetched data.
PROPOSED SYSTEM:
Furthermore, the cloud server may identify what the encrypted keywords are
by using the estimated distributions and some background knowledge. On the other
hand, some methods can be used to resist the proposed attack. One is to improve
the One-to-Many OPE itself. For instance, we can divide plaintexts having the
same value into several sets and divide the corresponding bucket into several subbuckets. By mapping each plaintext set into one sub- bucket, some new change
points will appear in the differential attack, which will cover up the original
distribution of plaintexts. Another possible method is to add noise into the inverted
index by adding some dummy documents IDs and keywords, and forging
corresponding relevance scores. In our future work, we will elaborate these ideas
to design secure methods of probabilistic OPE and schemes for search in encrypted
data.
ADVANTAGE:
In ranked search of encrypted cloud data, probabilistic OPE is needed to preserve
the order of relevance scores and conceal their distributions at the same time. Oneto-Many OPE is a scheme designed for such a purpose.
However, in this paper, we demonstrate that the cloud server can estimate the
distribution of relevance scores by change point analysis on the differences of
ciphertexts of One-to-Many OPE
MODULE DESCRIPTION
MODULE
 Home
 Searching Module
 Cryptography
 Encryption and Decryption Module
 File Sharing
MODULE DESCRIPTION
 Home
Distributed file systems for mobile clouds. Moreover many studies about
the storage systems for cloud environments that enable mobile client devices
have been published. A new mobile distributed file system called
mobile.
DFS has been proposed and implemented in which aims to reduce computing
in mobile devices by transferring computing requirements to servers. Hyrax,
which is a infrastructure derived from Hadoop support cloud computing on
mobile devices. But Hadoopis designed for general distributed computing,
and the client machines are assumed to be traditional computers. In short,
neither of related work targets at the clouds that have certain resource-limited
client machines, for yielding attractive performance enhancements.
 Searching Module
In practice, to realize effective data retrieval on large amount of
documents, it is necessary to perform relevance ranking on the results.
Ranked search can also gnificantly reduce network traffic by sending back
only the most relevant data. In ranked search, the ranking function plays an
important role in calculating the relevance between files and the given
searching query. The most popular relevance score is defined based on the
model of , where term frequency is the number of times a term (keyword)
appears in a file and inverse document requency (IDF) is the ratio of the
total number of files to the number of files containing the term. There are
many variations of -based ranking functions, and in [16], the following one
is adopted. Score Herein, w denotes the keyword and denotes the TF of
term w in file denotes IDF where is the number of files that contain term w
and is the total number of documents in the collection; and is the number
of indexed terms containing in file the length
Cryptography Module
OPE is a symmetric cryptosystem, so it is also called order-preserving
symmetric encryption (OPSE). The order preserving property means that if
the plaintexts have such a relationship as then the corresponding ciphertexts
and
satisfy. Boldyreva et al. initiated the cryptographic study of OPE
schemes, and they defined the security of an OPE scheme using the ideal
object. Note that any order-preserving function g from domain D Ng can be
uniquely defined by a combination of M out of N ordered items. The ideal
object is just a function that is randomly selected from all rder-preserving
functions, which is called a random order-preserving function (ROPF). bucket
is determined by a binary search based on a random HGD sampler. In , the
procedure of binary search is described as Algorithm 1, where is a random
coin generator.
 Encryption and Decryption Module
Filebench which allows generating.A large variety of workloads to
assess the performance of storage systems. Besides, Filebench is quite
flexible and enables to minutely specify a collection of applications, such as
mail, web, file, and database servers .We chose Filebench as one of
benchmarks, as it has been widely used to evaluate file systems by emulating
a variety of several server-like applications. I Ozone, which is a microbenchmark that evaluates the performance of a file system by employing a
collection oads with regular patterns, such as sequential, random, reverse
order, and strided That is why we utilized it to measure read data throughput
of the file systems with various prefetching schemes, when the workload
have different access patterns.
 File Sharing Module
Applications of privacy preserving keyword search, if a deterministis used to
encrypt relevance scores, the ciphertexts will share exactly the same
distribution as its plain counterpart, by which the server can specify the
keywords. Therefore, Wang et al. modified the original OPE to a probabilistic
one, called “One-to-Many OPE”. For a given plaintext “One-to-Many OPE”
first employs Algorithm 1 to select a bucket for m, and then randomly
chooses a value in the bucket as the Ciphertext. The randomly choosing
procedure in the bucket is seeded by the unique file IDs together with the
plaintext m, and thus the same relevance score in the Inverted Index will be
encrypted as different ciphertexts. The encryption process of “One-to- Many
OPE” is described in Algorithm. which is also illustrated in Picture.
ALGORITHM:
1. RSA Algorithm.
2. Binary searching Algorithm.
OPE ALGORITHM:
Order preserving Encryption:
Binary searching algorithm.
SYSTEM SPECIFICATION
Hardware Requirements:
• System
: Pentium IV 2.4 GHz.
• Hard Disk
: 40 GB.
• Floppy Drive
: 1.44 Mb.
• Monitor
: 14’ Colour Monitor.
• Mouse
: Optical Mouse.
• Ram
: 512 Mb.
Software Requirements:
• Operating system : Windows 7 Ultimate.
• Coding Language
: ASP.Net with C#
• Front-End
: Visual Studio 2010 Professional.
• Data Base
: SQL Server 2008.