Effective System Based On Diffie Hellman Computer Science Essay

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Cloud computing is one of today's most enticing technology areas due to its cost-efficiency and flexibility. Therefore, outsourcing data to the cloud servers become much reliable. But data are insecure so it is more in force to encrypt the data before outsourcing. Many encryption techniques are used merely searching on encrypted data is a difficult task. So many algorithms where used for searching the encrypted content to make much efficient for the user while retrieval of files. Keyword based search on encrypted data is much efficient and fast when retrieval of data .The survey is done on searching algorithm to find out the best efficient methodology for searching the encrypted data that have been outsourced.

Index Term Keyword based search


Cloud computing, the most trendy computing in information technology where everything is based on on-demand service and pay-for -use service. It is the delivery of computing services over the Internet. Cloud services allow individuals and businesses to use software and hardware that are managed by third parties at remote locations. Examples of cloud services include online file storage, social networking sites, webmail, and online business applications. The cloud computing model allows access to information and computer resources from anywhere that a network connection is available. Cloud computing provides a shared pool of resources, including data storage space, networks, computer processing power, and specialized corporate and user applications.

Existing Techniques

Symmetric key cryptography works by encrypting each word in a file using two layered encryption construction. Probabilistic searching is made on the encrypted data which deals with sequential scan and indexing methodologies Provable secrecy, controllable searching, hidden queries, query isolation are the four techniques which makes the algorithm efficient, simple and fast. Sequential scan meets all the above techniques but it is not effective when searching is made on huge data content. Therefore to induce searching pre-computed index plays a vital role which support advanced search queries. But to make indexing technique secure, Secure Index data structure can be used which admits queries with a trapdoor. It is semantically secure and practicable in multi-user settings where indexes are updated frequently on the remotely located server.

Public Encryption Keyword Search

It is also a searchable encryption technique which corresponds to symmetric key encryption .In this, file is encrypted using public key by the people who wants to store it to the server but only the authorized users can search the file using their private key. The four algorithms are used for this technique. First, keyGen used to generate public key and private key pair for both server and user. Second, PEKS algorithm produces searchable encryption. Third, Trapdoor algorithm used to calculate trapdoor with private key and keyword. Fourth, Test used to match the keyword and requested word. If matches then the file are sent to the user .PEKS means Identity Based Encryption (IBE) which has major advantages such as

Effective system based on Diffie-Hellman problem

Limited system based on general trapdoor definitions

This scheme fails regarding access policy and dictionary attack. The major disadvantages are trapdoor contains meaningful keywords and one-one mapping takes place between trapdoor and keyword.


Public key secret key



Sender Receiver



Hidden Vector Encryption

HVE supports continuative queries whereas PEKS supports only comparison and subset queries. In which it works with four algorithms namely Setup, Encrypt, GenToken and Query .First, Setup creates a bilinear group of elements using random primes and random elements, Second, Encrypt chooses the random element and using public key it encrypt the contents in a file. Third, GenToken will generate the token for the predicate using a secret key. Fourth, Query finds the keyword from the cipher text and if matches it return the file. Even so HVE fails for disjunctive queries because cipher text is linear to attribute.

Attribute Based Encryption

ABE affirms one upload many download policy most formally PEKS and HVE does not support. ABE uses access policy while searching on encrypted data with its Boolean expressions. It works on the basis of nine algorithms .The first is the Setup algorithm used to compute secret key and master key by the trusted authority. Second, KeyGen algorithm used to generate the public/private key pair. Fourth, fifth and sixth algorithm such as PseudoGen, Encrypt, AttrScm used for outsourcing the data using cryptographic primitives such as access structure, bilinear maps and attribute scrambling procedure. Seventh, eighth and ninth algorithm such as query, retrieve and decrypt is mainly for the retrieval of data. In which query algorithm works as the retriever take on pseudonym list from the cloud service provider and receiver sends the scrambled index to the CSP. Then the CSP checks whether the request made by the retriever and the encrypted index stored are same by using Retrieve algorithm. If it matches decrypt algorithm works where the encrypted data are decrypted and sent to the retriever. It provides best quality for searching over encrypted data and faster in accessing.

Predicate Privacy Preserving in Public Key Encryption

Predicate privacy preserving keyword search get the better of PEKS by using randomization technique. In which keywords are randomized and therefore trapdoors does not provide any meaningful keywords. The user and the receiver share a secret key which is not logical when there are huge number of users .To make tolerant of guessing attacks, two framework were introduced namely PEKSrand-BG for brute-force guessing and PEKSrand-SG for statistical guessing.

PEKSrand-BG provides a proxy server which in advance processes the PEKS cipher text from the sender.

PEKSrand-SG has two methods Proxy Farm and Random Walk, in which several proxies can be maintained for storing the secret key and indirect mapping between keyword and trapdoor respectively.

Therefore overall communication, computation and storage overhead are sensible when predicate privacy made in PEKS.





Privacy Preserving Keyword Search

It is a multi-round protocol which does not involves any public key. It satisfies major requirements such as correctness, limited bandwidth, storage space and security requirements.PPKS uses per-file index where each file has an index keyword .This keywords are encrypted using pseudorandom bits .While searching is made by a keyword

Secure Privacy Preserving Keyword Search

SPKS allows cloud service provider to decrypt the data and return the file containing the keywords. This technique overcomes the computation and communication overhead, provides query and data privacy for the users. It figures out six algorithms for efficient searching on encrypted data. First, KeyGen used to generate a public/private key pair. Second, EMBEnc&KWEnc encrypts all the content in the file and keywords are encrypted respectively which then stored in the server. Third, Tcompute used on the retrieving phase where user generates a trapdoor and pass it to CSP. Fourth, KWTest checks whether the keyword contain in the encrypted data. Fifth, PDecrypt mainly for CSP to decrypt the intermediate result partly and sends the cipher text and the partial decrypted content .Sixth, Recovery runs by the user to decrypt the plain text. Thus it provides semantic security in plain text attack.









Authorized Private keyword Search

APKS deals with multi-keyword search while above techniques conducts with single keyword which misses query flexibility and efficiency. Fine-grained authorization framework is in which every user obtain search capabilities under authorization from Local Trusted Authority (LTA). Hierarchical Predicate Encryption (HPE), a cryptographic primitive uses attribute hierarchy for simple range queries.





APKS based on HPE

The following steps are required while searching in encrypted data using multi-keyword

Converting multi-dimensional query to its CNF (Conjunctive Normal Form) formula.

Queries define attributes in a hierarchical way. i.e., attribute hierarchy.

Indexes and capabilities are generated by GenIndex and GenCap algorithm respectively.

When the user wants to retrieval a file using a keyword from LTA, LTA checks whether the user has an attribute value set and if it matches then user can retrieval the file from the server. But the major disadvantage is APKS also does not prevent keyword attack.

APKS+ adds a secret key while encryption and decryption takes place which hides the data from the attackers. Therefore it prevents dictionary keyword attack and accomplishes index privacy and query privacy.

Fuzzy Keyword Search

It enhances system usability when searching input exactly matches. Keywords are measured using edit distance and fuzzy keyword sets are constructed. Straight forward and wild card based are the two approaches are dealt with edit distance .In straight forward approach edit distance are calculated where all the forms of keywords are to be listed .Based on this indexing is built .Trapdoor are shared between user and the owner while retrieving file user computes the trapdoor receiving the request server compares with the index table and return all possible identifiers. The major disadvantage is large storage is needed and so lack of efficiency .Wild card based approach overcomes the disadvantage by building a wild card fuzzy sets which calculates edit distance , keyword takes place at the same position are put together in a set.

Thus the above all techniques based on searchable encryption supports only Boolean search which has two major drawbacks. They are,

After retrieving the file based on the keyword user wants to decrypt every file that contains the keyword to match their file

Retrieving all files that contains the keyword leads to network traffic.

Ranked Keyword Search over Encrypted Data

The major disadvantages of the above techniques get the better of in ranked keyword search. Ranked Searchable Symmetric Encryption (RSSE) framework is used to support rank search which built over the SSE cryptographic primitive. Four algorithms are used namely KeyGen, BuildIndex, TrapdoorGen, SearchIndex. Two phases such as setup phase which uses KeyGen alg. for generating public/private key pair and BuildIndex alg. to generate index file containing keywords educed from file. The file collection and the index file are outsourced after encryption with frequency based relevance score. While the retrieval phase uses TrapdoorGen alg. generates a trapdoor using the user’s request. Upon the user request server runs SearchIndex alg. which searches the files based on the ids and their relevance scores and sent the files to the user. But RSSE has huge communication overhead when ranking is on the user side and two round trip time is taken .Therefore efficient RSSE frame work is used using Order Preserving Symmetric Encryption Scheme(OPSE). It supports deterministic property and TapeGen (.) a random coin generator and HYGEINV (.) for sampling function implemented .OPSE is used instead of encrypting scores in RSSE and in reterival phase OPSE values are much more relevant. They provide better efficiency while retrieving files with top-k retrieval.


The analyse through all the papers show that the retrieval of files from the outsourced encrypted data has many techniques. We first putforth many searchable encryption schemes and then conclude that rank based retrieval of data is the best technique for keyword based search which provides more computation and communication overhead.