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Building Confidential and Efficient Query Services in the Cloud with Rasp Data Perturbation

ABSTRACT

This paper random space perturbation (RASP) data perturbation can require the efficient query service in data perturbation and secure range data recovery in cloud. The randomly transform the multidimensional data sets with a combination of order preserving encryption, dimensionality expansion, random noise injection, and random project, so that the utility for processing range queries is preserved. RASP query service is process the kNN-R algorithm using KNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Define the information leakage caused by the leaked query and access patterns, and then precisely analyze the data and query confidentiality affected by this information leakage.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:- In existing system, If the attacker knows the original distributions and manages to identify the mapping between the original attribute and its encrypted counterpart, a bucket based distribution alignment can be performed to break the encryption for the attribute.	PROPOSED  CONCEPT :- We proposed define the RASP perturbation method, describe its major properties, and analyze the attacks to the RASP perturbed data. We also introduce the framework for constructing the query services with the RASP perturbation.
EXISTING TECHNIQUE:- Order preserving encryption (OPE).	PROPOSED TECHNIQUE:- K nearest-neighbor (kNN-R).
TECHNIQUE DEFINITION:- Order-preserving symmetric encryption (OPE) is a deterministic encryption scheme whose encryption function preserves numerical ordering of the plaintext	TECHNIQUE DEFINITION:- K-Nearest Neighbors algorithm is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.
DRAWBACKS:- Perturbation data is efficiency of searching user. Affects the efficiency of query processing and the inhouse workload.	ADVANTAGES:- It is efficiency and security policy in perturbation data. Scalability and cost saving in data query privacy.