Efficient Encrypted Data Search as
A Mobile Cloud Service
Abstract:
Abstract—Document storage in the cloud infrastructure is rapidly gaining
popularity throughout the world. However, it poses risk to consumers unless the
data is encrypted for security. Encrypted data should be effectively searchable
and retrievable without any privacy leaks, particularly for the mobile client. Although recent research has solved many
security issues, the architecture cannot be applied on mobile devices directly
under the mobile cloud environment. This is due to the challenges imposed by
wireless networks, such as latency sensitivity, poor connectivity, and low
transmission rates. This leads to a long search time and extra network traffic
costs when using traditional search schemes. This
study addresses these issues by proposing an efficient Encrypted Data Search
(EnDAS) scheme as a mobile cloud service. This innovative scheme uses a
lightweight trapdoor (encrypted keyword) compression method, which optimizes
the data communication process by reducing the trapdoor’s size for traffic
efficiency. In this
study, we also propose two optimization methods for document search, called the
Trapdoor Mapping Table (TMT) module and Ranked Serial Binary Search (RSBS)
algorithm, to speed the search time. Results show that EnDAS reduces search
time by as well as network traffic.
ALGORITHMS:
FAH ALGORITHM: FAST ACCUMULATED
HASHING
A new no trapdoor accumulator for
cumulative hashing is introduced. It can be efficiently realized in practise
using existing cryptographic hash algorithms and pseudorandom sequence
generators. The memory requirement is less than in comparable signature based
solutions.
RSS ALGORITHM: RANKED
SERIAL SEARCH.
Cryptography is a method of storing and transmitting data in
a particular form so that only those for
whom it is intended can read and process it.
Plaintext: Cliphertext:
ciphertext
is then back again (known as decryption). Individuals who
practice this field are known as cryptographers.
.RSBS ALGORITHM: RANKED
SERIAL BINARY SEARCH.
LSB: Last
significant bit
CBS:
Centre Bit Signification.
SYSTEM ANALYSIS
EXISTING SYSTEM:
Here
the FAH encryption algorithm for document indexes is employed in previous
literature . Utilizing this FAH algorithm, we encrypt slices of each index. detailed
encryption process for one slice Slicec of the index Ic is that encrypting
l-bit term t in Slicec is used by the hash function , and mapping l-bit
encrypted term into r-bit optimized term is by the mapping function, where and then accumulating all the r-bit optimized
terms together. Finally we get the encrypted slice Slice . In this way, we can
encrypt the index Ic by accumulating all the slices (s slices), and obtain the
encrypted index I\ c equals accumulatin all the optimized terms in this
document,
PROPOSED SYSTEM:
The
ranked keyword search will return documents to the relevance score. Zero et al.
proposed a novel technique that makes the server side carry out the search
operation. However, it should send many unrelated documents back and let the
user filter them. This is a waste of traffic, which is unsuitable for the
mobile cloud. Bowers et al. proposed
a distributed cryptographic system that preserved the security of the document
retrieval process and the high availability of The
system, but this system suffers from two network round trips and calculation
complexity for target documents. Wang et al. proposed a single round trip encrypted search scheme,
but their system is not secure enough, as it leaks the keyword and associated
document information from multiple keyword searches. Li et al. proposed a single-keyword encryption search
scheme utilizing ranked keyword search, which network communication between the
user and the cloud by transferring the computing burden from the user to the
cloud.
Advantages:
we
proposed a novel encrypted search system EnDAS over the mobile cloud, which
improves network traffic and search time efficiency compared with the
traditional system. We started with a thorough analysis of the
traditional encrypted search system and. analyzed its
bottlenecks in the mobile cloud: network traffic and search time inefficiency.
Then we developed an efficient architecture of EnDAS which is suitable for the
mobile cloud to address these issues, where we utilized the TMT module. RSBS
algorithm to cope with the inefficient search time issue, while a trapdoor
compression method was employed to reduce network traffic costs. Finally our
evaluation study experimentally demonstrates the performance advantages of
EnDAS
FLOW
DIAGRAM:
Cloud Computing:
Cloud
computing is a
computing term or metaphor that evolved in the late 1900s, based on utility and consumption of computer resources.
Cloud computing involves deploying groups of remote servers and software networks that allow different
kinds of data sources be uploaded for real time processing to generate
computing results without the need to store processed data on the cloud. Clouds
can be classified as public, private or hybrid.
Encryption
and Decryption
Encryption
In an encryption scheme, the message or
information (referred to as plaintext) is encrypted using an encryption algorithm, turning it into an
unreadable cipher
text (ibid.). This is usually done with the
use of an encryption key, which specifies how the
message is to be encoded. Any adversary that can see the cipher text, should
not be able to determine anything about the original message.
Decryption
An
authorized party, however, is able to decode the ciphertext using a decryption algorithm, that usually
requires a secret decryption key, That adversaries do not
have access to. For technical reasons, an encryption scheme usually needs a
key-generation algorithm, to randomly produce keys
Hence, it is an
especially important thing to explore an effective multi-keyword ranked
searching service over encrypted outsourced data.
MODULE DESCRIPTION:
Experiment environmental. Search Time Evolution. Network Traffic Evolution. Experiment
environmental.
To evaluate the EnDAS
system, we implemented our system on the private cloud with Openstack Essex
[19] from our lab. We rented a virtual machine with 8G memory for the cloud. We
also implemented the RSBS algorithm, written as a python program, to search and
return the retrieved documents to the user. Here, the user utilized a mobile
device utilized an Android tablet with a Cortex- A9 Quad 1.4GHz CPU, and 2GB
memory. The tablet is connected to a mobile network with 72Mbps rate. The
trapdoor mapping table is pre-computed on a PC and uploaded to the mobile
device before experiments, which consumes 0.31MB of device storage.
Search Time Evolution.
To reduce the search time and improve the calculation
efficiency, we utilized the TMT module and the RSBS algorithm in the EnDAS
system. In this part, we first evaluate the overall search time and its
breakdown. Then we present the performance or the RSBS algorithm in terms of
the search time
Network Traffic
Evolution.
EnDAS system, which benefits from the trapdoor compression
method and the TMT module, we reduced network traffic significantly. Next we
evaluate and analyze the overall system network traffic reduction and the
performance of the trapdoor compression method.
SYSTEM SPECIFICATION
Hardware Requirements:
v System : Pentium IV 2.4 GHz.
v Hard Disk
: 40 GB.
v Floppy Drive :
1.44 Mb.
v Monitor
: 14’ Colour Monitor.
v Mouse : Optical Mouse.
v Ram :
512 Mb.
Software Requirements:
v Operating system : Windows 7 Ultimate.
v Coding Language : ASP.Net with C#
v Front-End : Visual Studio 2010 Professional.
v Data Base : SQL Server 2008.
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