1. Decentralized Access Control with Anonymous Authentication of Data Stored in Clouds
Abstract
We propose a new decentralized access control scheme for secure data storage
in clouds that supports
anonymous authentication. In the proposed scheme, the
cloud verifies the authenticity of the series
without knowing the user’s identity before
storing data. Our scheme also has the added feature of access control in which only valid
users are able to decrypt the stored information. The scheme prevents replay
attacks and supports
creation, modification, and reading data stored in the cloud. We also
address user revocation. Moreover, our authentication and access control scheme is
decentralized and robust, unlike
other access
control schemes designed for clouds
which are centralized. The communication, computation, and storage overheads are
comparable to centralized approaches.
2. Modeling of Distributed File Systems for
Practical Performance Analysis
Abstract—Cloud
computing
has received significant attention recently. Delivering quality guaranteed services in clouds is highly
desired. Distributed file systems
(DFSs) are the key component of any cloud-scale data
processing middleware. Evaluating the
performance of DFSs is accordingly very important. To avoid cost for late life cycle performance fixes and architectural redesign,
providing performance analysis before the deployment of DFSs is also particularly important. In this paper, we propose a systematic and practical performance analysis
framework, driven by architecture and design
models for defining the structure
and
behavior of typical master/slave
DFSs. We put forward a configuration guideline for specifications of
configuration alternatives of such DFSs, and a practical approach for both qualitatively and quantitatively performance analysis of DFSs
with various configuration settings in a
systematic way. What distinguish our
approach from others
is
that
1)
most
of existing works
rely on
performance measurements under a variety of
workloads/strategies, comparing with other DFSs or running application programs, but our approach is based on architecture and
design level models
and systematically derived performance models; 2) our approach is able to both qualitatively and quantitatively evaluate the performance of DFSs; and 3) our
approach not only can evaluate the overall
performance of a DFS
but also its components and individual steps. We demonstrate the effectiveness of our approach by evaluating
Hadoop distributed file system (HDFS). A series of real-world experiments on EC2
(Amazon Elastic Compute Cloud),
Tansuo and Inspur Clusters, were conducted to qualitatively evaluate the effectiveness of our approach. We also performed a set of
experiments of HDFS on EC2 to quantitatively analyze the performance and
limitation of the metadata server of DFSs. Results show that our approach can achieve sufficient performance analysis. Similarly, the proposed approach could be also applied to evaluate
other DFSs such as MooseFS, GFS,
and zFS.
3. Balancing Performance, Accuracy, and Precision for Secure Cloud Transactions
Abstract—In
distributed transactional database systems deployed over cloud
servers, entities cooperate to form proofs of authorizations that are justified by collections of
certified credentials. These proofs and credentials may be evaluated and collected
over extended time periods under the risk of having the underlying authorization policies or
the user credentials being in inconsistent
states. It therefore becomes possible for policy-based authorization systems to make
unsafe decisions
that might threaten sensitive resources. In this paper, we highlight the
criticality of the problem. We then define the notion of trusted transactions when dealing with proofs of authorization. Accordingly, we
propose several increasingly stringent levels
of policy consistency constraints, and present different
enforcement approaches
to guarantee the trustworthiness of transactions executing on cloud servers. We propose a
Two-Phase Validation Commit
protocol as a solution, which is a modified version of the
basic Two-Phase Validation Commit protocols. We finally analyze the different
approaches presented using both analytical
evaluation of the overheads and simulations
to guide the decision makers to which approach
to use.
4. A Scalable Two- Phase Top-Down Specialization
Approach for Data
Anonymization
Using MapReduce on Cloud
Abstract—A large number of cloud services require users to share private data like
electronic health records for data analysis or mining, bringing privacy concerns. Anonymizing data sets via generalization to satisfy certain privacy requirements such as
kanonymity is a widely used category of
privacy preserving techniques. At present, the
scale of data in many cloud applications increases tremendously in accordance with
the Big Data trend, thereby making it a challenge for commonly used software tools
to capture, manage, and process such large- scale data within a tolerable elapsed time. As a result, it is a challenge for existing anonymization approaches to achieve privacy
preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. In this paper, we propose a
scalable two-phase top-down specialization
(TDS) approach to anonymize large-scale data sets using the MapReduce framework on
cloud. In both phases of our approach, we
deliberately design a group of innovative
MapReduce jobs to concretely accomplish the specialization computation in a highly
scalable way. Experimental evaluation results demonstrate that with our approach, the scalability and efficiency of TDS can be
significantly improved
over existing approaches.
5. Dynamic Optimization of Multiattribute Resource Allocation in Self-Organizing Clouds
By leveraging virtual machine
(VM) technology which provides performance and fault isolation, cloud resources can be
provisioned on demand in a fine grained,
multiplexed manner rather than in monolithic pieces. By
integrating
volunteer computing into cloud architectures, we envision a gigantic self-organizing cloud (SOC) being
formed to reap the huge potential
of untapped commodity computing power over the Internet. Toward this new architecture where
each participant may autonomously act as both
resource consumer and provider, we
propose a fully distributed, VM-multiplexing
resource allocation
scheme to manage
decentralized resources. Our approach not
only
achieves maximized resource
utilization using the proportional share model
(PSM),
but also delivers provably and adaptively optimal
execution efficiency. We also design
a novel multi attribute range query protocol
for locating qualified nodes. Contrary to
existing solutions
which often generate bulky messages per request, our protocol produces
only
one lightweight query message per task
on the Content Addressable Network (CAN).
It
works effectively to find for each task its qualified resources under a randomized policy that mitigates the
contention among requesters. We show the SOC
with our optimized algorithms can make an
improvement by 15-60 percent in system throughput than a P2P Grid model. Our
solution also exhibits fairly high adaptability in a dynamic node-churning environment.
6. Scalable and Secure Sharing of Personal Health
Records in Cloud Computing Using Attribute-Based Encryption
Personal health record (PHR) is an emerging patient-centric
model of health information
exchange, which is often outsourced to be stored at a third party, such as cloud
providers. However, there have been wide privacy concerns as personal health
information could be exposed to those
third party servers and to Un authorized parties. To
assure the patients' control over access to their
own PHRs, it is a promising method
to encrypt the PHRs before outsourcing. Yet, issues such as risks of privacy exposure, scalability in key management, flexible
access, and efficient user revocation, have
remained the most important challenges
toward achieving
fine-grained, cryptographically
enforced
data access control. In this paper, we propose a novel
patient-centric framework and a suite of
mechanisms for data access control
to PHRs stored in semitrusted servers. To achieve fine-
grained and scalable data access control
for PHRs, we leverage attribute-based encryption (ABE) techniques
to encrypt each patient's PHR file. Different from previous works in
secure data outsourcing, we focus on the multiple data owner scenario, and divide the users
in the PHR system into
multiple security domains that greatly reduces the key management complexity for owners and users. A high degree of patient privacy is guaranteed simultaneously by exploiting
multiauthority ABE. Our scheme also enables
dynamic modification of access policies or
file attributes, supports efficient on-demand user/attribute
revocation and break-glass access under emergency scenarios. Extensive analytical and experimental results are presented
which show the
security, scalability, and
efficiency
of
our
proposed scheme.
7.On Data
Staging Algorithms for Shared Data Accesses in
Clouds
In this paper,
we study
the
strategies
for efficiently achieving data staging and caching on a set of vantage sites in a cloud system with
a minimum cost. Unlike
the traditional research, we do not intend to identify the
access patterns to
facilitate the future
requests. Instead, with such a kind of information presumably known in advance,
our goal is to efficiently stage the shared data items to predetermined sites at advocated time
instants to align with the patterns while
minimizing the monetary costs for caching and
transmitting the requested data items. To this end, we follow the cost and network models in [1] and extend the
analysis to multiple
data items, each with single or multiple copies. Our results
show that under homogeneous cost model,
when the ratio of transmission
cost and caching cost is
low, a single copy of each data item can
efficiently serve all the user requests. While in multicopy situation, we also consider the tradeoff between the transmission cost and
caching cost by controlling the upper bounds
of transmissions and copies. The upper bound
can
be given either on per-item basis or on all-item basis. We present efficient optimal
solutions based on dynamic programming
techniques to all these cases provided that the upper bound is polynomially bounded by the number of service requests and the number of distinct
data items. In addition to the
homogeneous cost model, we also briefly
discuss this problem under a heterogeneous
cost model with some simple yet practical
restrictions and present a 2-approximation
algorithm to the general case. We validate our
findings by implementing a
data staging
solver, whereby conducting extensive
simulation studies on the behaviors of the
algorithms.
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