Harnessing The
Cloud For Securely Outsourcing Large Scale Systems of Linear Equations
Abstract:
Cloud Computing has great potential of
providing robust computational power to the society at reduced cost. It enables
customers with limited computational resources to outsource their large
computation workloads to the cloud, and economically enjoy the massive
computational power, bandwidth, storage, and even appropriate software that can
be shared in a pay-per-use manner. Despite
the tremendous benefits, security is the primary obstacle that prevents the
wide adoption of this promising computing model, especially for customers when
their confidential data are consumed and produced during the computation.
On the one hand,
the outsourced computation workloads often contain sensitive information, such
as the business financial records, proprietary research data, or personally
identifiable health information etc. To combat against unauthorized information
leakage, sensitive data have to be encrypted before outsourcing so as to
provide end to- end data confidentiality assurance in the cloud and beyond.
However, ordinary
data encryption techniques in essence prevent cloud from performing any
meaningful operation of the underlying plaintext data, making the computation
over encrypted data a very hard problem. On the other hand, the operational
details inside the cloud are not transparent enough to customers.
As a result, there
do exist various motivations for cloud server to behave unfaithfully and to
return incorrect results, i.e., they may behave beyond the classical semi
honest model.
Existing
System:
Despite the
tremendous benefits, outsourcing computation to the commercial public cloud is
also depriving customers’ direct control over the systems that consume and
produce their data during the computation, which inevitably brings in new
security concerns and challenges towards this promising computing model.
On the one hand,
the outsourced computation workloads often contain sensitive information, such
as the business financial records, proprietary research data, or personally
identifiable health information etc.
To combat against
unauthorized information leakage, sensitive data have to be encrypted before
outsourcing. So as to provide end to- end data confidentiality assurance in the
cloud and beyond. However, ordinary data encryption techniques in essence
prevent cloud from performing any meaningful operation of the underlying
plaintext data, making the computation over encrypted data a very hard problem.
On the other hand, the operational details
inside the cloud are not transparent enough to customers. As a result, there do
exist various motivations for cloud server to behave unfaithfully and to return
incorrect results, i.e., they may behave beyond the classical semi hones model.
For example, for
the computations that require a large amount of computing resources, there are
huge financial incentives for the cloud to be “lazy” if the customers cannot
tell the correctness of the output.
Besides, possible software bugs, hardware
failures, or even outsider attacks might also affect the quality of the
computed results.
Thus, we argue that
the cloud is intrinsically not secure from the viewpoint of customers.
Without providing a mechanism for secure computation outsourcing, i.e., to
protect the sensitive input and output information of the workloads and to
validate the integrity of the computation result, it would be hard to expect
cloud customers to turn over control of their workloads from local machines to
cloud solely based on its economic savings and resource flexibility. For practical
consideration, such a design should further ensure that customers perform fewer
amounts of operations following the mechanism than completing the computations
by themselves directly.
Otherwise, there is
no point for customers to seek help from cloud. Recent researches in both the
cryptography and the theoretical computer science communities have made steady
advances in “secure outsourcing expensive computations”
Proposed
System:
On the one hand,
the outsourced computation workloads often contain sensitive information, such
as the business financial records, proprietary research data, or personally
identifiable health information etc.
To combat against
unauthorized information leakage, sensitive data have to be encrypted before
outsourcing so as to provide end to- end data confidentiality assurance in the
cloud and beyond.
However, ordinary
data encryption techniques in essence prevent cloud from performing any
meaningful operation of the underlying plaintext data, making the computation
over encrypted data a very hard problem. On
the other hand, the operational details inside the cloud are not transparent
enough to customers. As a result, there do exist various motivations for cloud
server to behave unfaithfully and to return incorrect results, i.e., they may
behave beyond the classical semi honest model.
Fully holomorphic
encryption (FHE) scheme, a general result of secure computation outsourcing has
been shown viable in theory, where the computation is represented by an
encrypted combinational Boolean circuit that allows to be evaluated with
encrypted private inputs.
System
Architecture:
System
Specification:
Hardware System Requirement:
·
Processor - Pentium –III
·
Speed -
1.1 GHz
·
RAM -
256 MB (min)
·
Hard Disk -
20 GB
·
Floppy Drive - 1.44 MB
·
Key Board -
Standard Windows Keyboard
·
Mouse -
Two or Three Button Mouse
·
Monitor -
SVGA
S/W System Requirement
v Operating System
: Windows 95/98/2000/NT4.0.
v Application
Server : Tomcat6.0
v Front End : HTML, Java.
v Scripts :
JavaScript.
v Server side Script : Java Server Pages.
v Database : Mysql.
v
Database
Connectivity : JDBC.
REFERENCE:
Cong
Wang, Kui Ren and Jia Wang, “Secure and Practical Outsourcing of Linear
Programming in Cloud Computing”, IEEE
INFOCOM 2011.
