Efficient Encrypted Data Search as A Mobile Cloud Service

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.

Cryptographic: (a process called encryption),

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:

Most often associated with scrambling plaintext (ordinary text, sometimes referred to as cleartext)

    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.