17-03-2012, 11:00 PM
im chaithra shree, doing my 8th sem BE, i have selected "secure and practical outsourcing of linear programming in cloud computing ieee april 2011 ppt" as my seminor topic, so plz help me by providing power point presentation on this topic.
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. Treating the cloud as an intrinsically insecure
computing platform from the viewpoint of the cloud customers,
we must design mechanisms that not only protect sensitive
information by enabling computations with encrypted data, but
also protect customers from malicious behaviors by enabling
the validation of the computation result. Such a mechanism of
general secure computation outsourcing was recently shown to be
feasible in theory, but to design mechanisms that are practically
efficient remains a very challenging problem.
Focusing on engineering computing and optimization tasks,
this paper investigates secure outsourcing of widely applicable
linear programming (LP) computations. In order to achieve
practical efficiency, our mechanism design explicitly decomposes
the LP computation outsourcing into public LP solvers running
on the cloud and private LP parameters owned by the customer.
The resulting flexibility allows us to explore appropriate security/
efficiency tradeoff via higher-level abstraction of LP computations
than the general circuit representation. In particular, by
formulating private data owned by the customer for LP problem
as a set of matrices and vectors, we are able to develop a set of
efficient privacy-preserving problem transformation techniques,
which allow customers to transform original LP problem into
some arbitrary one while protecting sensitive input/output information.
To validate the computation result, we further explore
the fundamental duality theorem of LP computation and derive
the necessary and sufficient conditions that correct result must
satisfy. Such result verification mechanism is extremely efficient
and incurs close-to-zero additional cost on both cloud server and
customers. Extensive security analysis and experiment results
show the immediate practicability of our mechanism design