Cloud Computing is an advanced technology for
optimizing and innovating business models in organizations. It
can be used for providing software and infrastructure services
deployed in data centers. Encryption of data and information by
data owners and uploading them to the cloud data center leads
to different efficiency and secrecy problems. In cloud computing,
a user who has authorized credentials should have the ability to
access confidential data, such as data owners or cloud providers.
In traditional methods of securing data, data are encrypted
and stored in a trusted server and their access is controlled
by an access control policy. If the cloud server is breached
from unauthorized users, the confidentiality of sensitive data
will be disclosed. This paper explores an enhanced cloud access
control policies over encrypted data using XACML framework
and proof of ownership (POW) methodologies. The proposed
approach controls the access mechanism over encrypted data by
generating a security token for sending responses and receiving
user requests for decrypting data based on the previously stored
attributes in the XACML policy. The security mechanism will be
strengthened by deploying a fingerprint authentication parameter
for ensuring confidentiality over untrusted user requests. By
applying the cloud access control of XACML framework, the
cloud services will perform its agreed functions with preventing
data leakage, data loss, and abuse of cloud services.
Keywords�XACML, cloud computing, proof of

: in this paper I will present two different genetic and ant colony algorithms for solving a classic computer science problem: shortest path problems. I will first give a brief discussion on the general topics of the shortest path problem, genetic and ant colony algorithms. I will conclude by making some observations on the advantages and disadvantages of using genetic and ant colony algorithms to solve the shortest path problem and my opinion on the usefulness of the solutions and the future of this area of computer science
Keywords: genetic programming, ant colony algorithms, shortest path, optimization problems