ABSTRACT:
With wide use of cloud computing and
storage services, sensitive information is increasingly centralized into the
cloud to reduce the management costs, which raises concerns about data privacy.
Encryption is a promising way to maintain the confidentiality of outsourced
sensitive data, but it makes effective data utilization to be a very
challenging task. In this paper, we focus on the problem of private matching
over outsourced encrypted datasets in identity-based cryptosystem that can
simplify the certificate management. To solve this problem, we propose an
Identity-Based Private Matching scheme (IBPM), which realizes fine-grained
authorization that enables the privileged cloud server to perform private
matching operations without leaking any private data. We present the rigorous
security proof under the Decisional Linear Assumption and Decisional Bilinear
Diffie-Hellman Assumption. Furthermore, through the analysis of the asymptotic
complexity and the experimental evaluation, we verify that the cost of our IBPM
scheme is linear to the size of the dataset and it is more efficient than the
existing work of Zheng [30]. Finally, we apply our IBPM scheme to build two
efficient schemes, including identity-based fuzzy private matching as well as
identity-based multi-keyword fuzzy search.
PROJECT OUTPUT
VIDEO: (Click the below link to see the project output video):
EXISTING SYSTEM:
·
In Liu et al.’s scheme, the users outsource their datasets to the cloud
by hashing each element and delegate matching operations to the cloud. However,
it’s not fine-grained authorization secure, meaning that if the cloud is
delegated to compute set intersection between the datasets of user Alice and
Bob, followed by that between the datasets of user Alice and Carlos, then the
cloud will get set intersection between the datasets of user Bob and Carlos
without their consent.
·
The scheme proposed by Zheng et al. is a verifiable solution based on
proxy re-encryption technique but it’s also not fine-grained authorization
secure.
·
Recently, Adabi et al. proposed a new delegated solution by leveraging
homomorphic encryption and polynomial evaluation. However, in their scheme, the
client must download and decrypt as many as 2n ciphertexts (n is the size of
dataset).
·
DISADVANTAGES OF EXISTING SYSTEM:
·
Suppose there are two cloud users, they encrypt their datasets and
outsource them to the cloud. The cloud server with the corresponding
authorization token can conduct the heavy duty computational matching
operations over ciphertexts.
·
Runs the complex algorithm factorizing polynomials to get the result.
·
It’s not a practical solution for our problem.
·
How to construct an efficient and secure identity-based private matching
scheme over outsourced encrypted datasets is a promising open problem.
·
PROPOSED SYSTEM:
·
In this paper, we focus on the problem of how the cloud carries out
private matching over outsourced encrypted datasets if and only if the cloud
server is authorized to do so.
·
We propose a novel cryptographic primitive: identity-based private
matching over outsourced encrypted datasets (IBPM), which can simplify
certificate management due to the advantage of identity-based cryptosystem.
Identity-based encryption was applied to cross domain data sharing in
distributed Electronic Health Records (EHR) systems, which allows users from
different domains to directly authenticate with each other.
·
Our IBPM can be used to provide privacy-preserving cross-domain EHR
matching when the EHR data are outsourced in an encrypted form to a cloud
platform. Furthermore, with our novel primitive, users gain the following
controls on the private matching over the outsourced encrypted datasets:
·
A user has fine-grained control over who can do private matching with
him/her, by negotiating the corresponding authorization token;
·
A user has fined-grained control over who can perform private matching,
by choosing the semi-trusted cloud.
·
ADVANTAGES OF PROPOSED SYSTEM:
·
We propose a novel cryptographic primitive: identity-based private
matching over outsourced encrypted datasets (IBPM), and formally define the
framework and the security for IBPM. Then we present a concrete construction of
the IBPM under the DLN and DBDH assumptions.
·
Our solution is in identity-based setting so that it can simplify the
certificate management;
·
The cloud users delegate the costly private matching operations without
giving the cloud any capability in breaching the secrecy of the datasets;
·
Our scheme realizes fine-grained authorization for private matching over
outsourced encrypted datasets. In other words, only the cloud server who has
the authorization token can perform private matching between two users’
encrypted datasets. What’s more, with our scheme, the users can delegate the
cloud server to check whether they have outsourced the same data to cloud
before uploading the encrypted data.
·
We give a rigorous security proof and implementation of our scheme.
·
Through the real experimental evaluation, we verify that the
computational cost of our scheme is linear to the size of the dataset and the
matching algorithm is more efficient than the existing work.
·
We apply our IBPM scheme to solve the problems of fuzzy private matching
and multi-keyword fuzzy search, and present two efficient schemes, i.e.,
identity-based fuzzy private matching scheme and identity-based multi-keyword
fuzzy search scheme.
·
SYSTEM ARCHITECTURE:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
·
System : Pentium Dual Core.
·
Hard Disk : 120 GB.
·
Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
·
Ram : 1 GB
SOFTWARE REQUIREMENTS:
·
Operating system : Windows 7.
·
Coding Language : JAVA/J2EE
·
Tool : Netbeans 7.2.1
·
Database : MYSQL
REFERENCE:
Shuo Qiu, Jiqiang
Liu, Yanfeng Shi, Ming Li, Member, IEEE, and Wei Wang, “Identity-Based Private
Matching over Outsourced Encrypted Datasets”, IEEE TRANSACTIONS ON CLOUD COMPUTING, 2017.