Wednesday, 23 July 2014
Private Searching on Streaming Data Based on Keyword Frequency
PRIVATE SEARCHING ON STREAMING DATA BASED ON KEYWORD FREQUENCY
Private searching on streaming data is a process to dispatch to a public server a program, which searches streaming sources of data without revealing searching criteria and then sends back a buffer containing the findings. From an Abelian group homomorphic encryption, the searching criteria can be constructed by only simple combinations of keywords, for example, disjunction of keywords. The recent breakthrough in fully homomorphic encryption has allowed us to construct arbitrary searching criteria theoretically. In this paper, we consider a new private query, which searches for documents from streaming data on the basis of keyword frequency, such that the frequency of a keyword is required to be higher or lower than a given threshold. This form of query can help us in finding more relevant documents. Based on the state of the art fully homomorphic encryption techniques, we give disjunctive, conjunctive, and complement constructions for private threshold queries based on keyword frequency. Combining the basic constructions, we further present a generic construction for arbitrary private threshold queries based on keyword frequency. Our protocols are semantically secure as long as the underlying fully homomorphic encryption scheme is semantically secure.
Ostrovsky and Skeith gave two solutions for private searching on streaming data. One is based on the Paillier cryptosystem and allows to search for documents satisfying a disjunctive condition i.e., containing one or more classified keywords. Another is based on the Boneh cryptosystem and can search for documents satisfying an AND of two sets of keywords. Bethencourt also gave a solution to search for documents satisfying a condition. Like the idea of, an encrypted dictionary is used. However, rather than using one large buffer and attempting to avoid collisions like, Bethencourt stored the matching documents in three buffers and retrieved them by solving linear systems. Yi proposed a solution to search for documents containing more than t out of n keywords, so-called (t; n) threshold searching, without increasing the dictionary size. The solution is built on the state of the art fully homomorphic encryption (FHE) technique and the buffer keeps at most m matching documents without collisions. Searching for documents containing one or more classified keywords like can be achieved by (1; n) threshold searching.
DISADVANTAGES OF EXISTING SYSTEM:
v It have not considered keyword frequency, the number of times that keyword is used in a document
In this paper, we consider a new private query, which searches for documents from streaming data based on keyword frequency, such that a number of times that a keyword appears matching document is required to be higher or lower than a given threshold. For example, find documents containing keywords k1; k2; . . . ; kn such that the frequency of the keyword kiði ¼ 1; 2; . . . ; nÞ in the document is higher (or lower) than ti. We take the lower case into account because terms that appear too frequently are often not very useful as they may not allow one to retrieve a small subset of documents from the streaming data.
ADVANTAGES OF PROPOSED SYSTEM:
v It encrypts the frequency threshold for each keyword because different keywords may have different frequency thresholds.
v A new type of private threshold query based on keyword frequency, which can help us in finding more relevant documents from streaming data.
Speed - 1.1 Ghz
RAM - 512 MB(min)
Hard Disk - 40 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - LCD/LED
Operating system : Windows XP.
Coding Language : JAVA
Data Base : MySQL
Tool : Netbeans.
Xun Yi, Elisa Bertino, Jaideep Vaidya, and Chaoping Xing, “Private Searching on Streaming Data Based on Keyword Frequency” IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL. 11, NO. 2, MARCH/APRIL 2014.