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Comparative Study of IFC methods for Cloud Computing
Shreyansh Zatakia
Department of Computer
Engineering
Dwarkadas J. Sanghvi College of
Engineering
shreyzatakia@outlook.com
Drumil Bakhai
Department of Computer
Engineering
Dwarkadas J. Sanghvi College of
Engineering
drums.b123@gmail.com
ABSTRACT
Cloud computing is internet based computing where
information, resources and devices are made available to
consumers on demand. The large data servers residing at
the back end address a wide range of needs. However,
this emerging technique has its share of issues relating to
data security and access controls as users outsource their
information to clouds. Security concerns are widely seen
as an obstacle that affects the adoption of cloud
computing solutions. A major problem is the presence of
unsafe information flows that that can be leaked to
unauthorized users. Such leakage of critical information
gave rise to the concept of Information Flow Control.
Information Flow Control is a method that enforces
information flow policies on data. This paper discusses
some of the proposed methods of Information Flow
Control in Cloud Computing. After a brief description of
each of the methods, the methods are compared with
each based on the various factors.
General Terms
Cloud Computing, Information Flow Control
Keywords
Cloud Computing, Information Flow Control, IFC,
FlowK, FlowK2, CloudIFC, Chinese wall Policy
1. INTRODUCTION
Cloud Computing can be often regarded as On Demand
Computing and the reason for this analogy is, it provides
computational services for the users and based on that it
can charge nominal fees. Cloud computing can be like
utility, just as electricity, where each individual is given
services by some major powerhouse and in return they
charge for the same. Cloud Computing is new promising
field in the field of distributed computing and has proved
to provide many business applications. Large Banks,
Power Companies, Industrial Houses are shifting their
enormous datasets over the cloud for processing and
large infrastructures are created in order to support the
client’s business requirements.The cloud computing
applications can be broadly divided into 3 platforms.
1.1 SaaS
The applications based on cloud just like services that are
running on the remote computers. These remote based
computers are owned by companies providing services
and connect to the user computers via the Internet. The
cloud computing companies provide the software as their
services to the users.
1.2 PaaS
In this type, PaaS provides entire cloud based
environment suitable for building and delivering entire
Lakshmi Kurup
Department of Computer
Engineering
Dwarkadas J. Sanghvi College of
Engineering
lakshmi.kurup@djsce.ac.in
web based applications. Here the advantage is, the clients
are shunned away from hardware complexity, publishing
hosting.
1.3 IaaS
Infrastructure as a service is service that provides
networks, storage, and all the other computing resources
that a small business companies cannot afford to
maintain.
However in order to provide these services, cloud
computing is often exposed to many threats and
vulnerabilities. Different problems when cloud
computing is concerned are data breaches, data loss,
Traffic Hijacking, Insecure APIs. Every threat needs to
be dealt with different approaches.
1.4 Information Flow Control
Tremendous amount of information is passed from cloud
computing servers to the client machines and vice versa.
This information needs some mechanism or rules based
on which it decides how the exchange takes place. Many
objects and subjects clash with Conflict of Interest and
much other access the information to which they do not
hold legitimate permission. Thus it is very necessary to
have Control for the information flow. Information Flow
Control deals with this problem.The paper mentions
about a detail comparative studies of the different
techniques that can be implemented with usage of IFC.
In the second section we describe about the techniques of
IFC in brief. In the third section we describe a detail
comparative study of all the techniques based on the
parameters given. The forth section includes conclusion
and the last section includes the references.
2. Information Flow Control Methods
2.1 FlowK
Since most of the cloud service providers use Linux OS,
FlowK is Linux kernel module implementing
information flow control within a Linux operating
system. There is a concept of trusted processes that
manages labels and assigns privileges to other processes.
The idea of FlowK is such that authentication and
authorization is enforced by permitting only certain type
of information flows. Certain rules that control the
various flows are:
• Implementing the information flow via the use of
secrecy and integrity labels.
• The child entity inherits the labels of the parent.
• There are privileges for removing and adding the
labels. Certain processes have the privileges to modify
their labels.
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• The conflict of interest between the principles should
be clearly defined.
• Trusted entities are used to assign labels and privileges
to processes.
Whenever an action is to take place, system calls are
generated. FlowK intercepts the system calls that are
generated and converts them to information flows. The
rules of information are then applied to system calls.
Assuming that shared memory is disabled, there are four
types of entities. These entities are process, pipes,
sockets and files. The labels of pipes, sockets and files
are immutable whereas the labels of process have
changeable labels. FlowK maintains a map between
entity identifiers and their labels and privileges. When a
process changes its security context, then it will not be
allowed to read or write file it created or could access
previously. Since pipes are created by processes they
inherit the labels of the processes. Information flow
policy is applied according to the direction of flow when
a process interacts with sockets. FlowK is simple and
straightforward way to implement information flow
control.
• Information can only flow within same group.
• After execution of IFS assignment statement and input
statement, join operation adjusts the security level of
destination.
• After invocation statement, the security level is same
as that of returned statement.
• For output statement, highly sensitive data should be
hidden from non-authorized users. The security level
of output is associated with the information to protect
it.
CloudIFC is a SaaS level of information flow control.
Since it is embedded at service level, it may induce
runtime overhead.
2.4 Chinese Wall Policy
Chinese wall policy was proposed by Brewer and Nash
and often called as BN Chinese policy. The objects and
subjects are used to prevent information flows that
results in Conflict Of Interest problem. The simple
diagram which elaborates Chinese Wall Policy is given
below.
2.2 FlowK2
FlowK2 is an extension of FlowK. FlowK supports ‘Big
Data’ along with the implementation of Information flow
control. The major challenge in IFC is to represent data
in labels which should be concise and should implement
policy efficiently. This method proposes the use of two
component tags that represent the concern of data and a
specifier for an item of that kind. One of the advantages
is that current single tag system can be easily expressed
in this way. The tag is decomposed in two components
concern and specifier (t=<c,s>). The information flow
rules are changed as follows:
• A flow from A to B is allowed there is a super type of
secrecy tag of A in B and there is super type of
integrity of B in A.
• Addition of special privilege to allow removal of
labels.
• The policy of conflict of interest is expanded to
include constraints applied on whole tags, concerns
and specifiers.
• For compatibility with single tag, the single tag will
become a specifier with a null concern.
• The highest level is conflict of interest class where the
general categorization of the objects is being done.
• The level below it consists of the datasets of individual
companies and all objects of the same company are
present together.
• The last level is individual level where each object of
information is being associated with a simple company
terminal
Chinese Wall Policy works on following two rules
Thus implementation of two-dimensional tags in IFC
will be helpful in data analysis in distributed system.
2.3 CloudIFC
Here, the information flow between the variables is
controlled. The IFC lies with the variables because
different variables contain data with different sensitivity.
Since the control granularity lies with the variables, the
variable is given a security level number and is
associated with a group. According to the group, the
sensitive variables are placed in the lattice and then are
ordered according to security level. CloudIFC includes
security levels and various components like set of
variables, input device, output device and files. IFS are
statements that will cause information flow. CloudIFC
consists of set of rules which are defined as:
• Two objects belonging to specific security group
belong to the same conflict of interest class.
• If two objects belong to different conflict of interest
classes they belong to different security group.
• If the conflict of interest is new or if the previous
object O` is accessed then the current object O can be
read.
• The data from the company dataset of O can be read
by the writer only if the above mentioned condition is
satisfied.
2.5 Rule Based IFC
Cloud based infrastructure is valuable to customers since
it allows the dynamicity from multiple terminals. The
existing web services models are focused onto the
protection of the individual services. It is necessary to
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a. Enumeration.
ensure proper information flow control when dealing
with multiple services from different domains. The
service chain systems determine whether sensitive
information should be directly or indirectly passed to
subsequent services. The basic criteria for the service
chain can be defined below.
All the possible combination are enumerated from
Si+1 to Sj-1 by Si and determine the next service
where the sensitive information is passed. Large
Overhead of enumerating all the services is one of the
drawbacks of this approach
Output of the service Si can be computed using the input
as well as Si backend databases. i.eSiOutF = SiInF +
SiInL
•
•
•
2.5.2 Service Access Control Models
a. Action Based Access
where SiOutF is the output of the service
SiInF is the input of the service
SiInL is backend databases of Si
In this method when a client is granted priviledges,
priviledges to the data and other resources is also
granted and can be accessed from the action. Action
based Control Model provides resource protection too.
The input data that Si received from Si-1 may result into
some changes into the backend databases of Si and the
itsupto Si if it wishes to retain those changes or discard
them. These decisions are based on certain control
policies and are discussed below.
2.5.1
b. Resource Based Access
This method does not allow indirect access imposed in
service chain. The resource based access can be
extended to web servies. It is mandatory to determine
the ownership of Resource r if it needs to accessed and
can only be granted if the request satisfy all the
necessary condition of the ownership set.
Approaches to Flow of service chaining
In this part we discuss about the approaches taken by the
service chaining in determining the next services to
process. The information will always flow based on the
decision made during this phase.
3. Comparative Study
The above mentioned approaches have been compared in
Table I. The parameters for comparison are approach,
level at which the method works. In approach the
different ways of controlling information flow are looked
at. In Level of operation, the level of cloud computing at
which the IFC method works is described.
a. Direct Access.
In this method the service Si ignores the computation
effect of the chain which might have services in the
order <Si, Si+1, . . . . Sj> and treats if Sj is directly
accessing the sensitive information of Si. This method
imposes many restrictive flow control constraints.
Comparative study of IFC methods
Name
Approach
Level of
Operation
Advantages
Disadvantages
FlowK
Concept of
Trusted Processes
and labels
Kernel level at
Operating System
of Server
Simple and easy
to implement
Single component
tag is not flexible
FlowK2
2D Component
Tags
Kernel level at
Operating System
of Server
Helps in data
analysis
More tags
increases
complexity
CloudIFC
Control
information flow
between
variables
Saas
Security level
numbers depict
sensitivity of
information
Overhead
increases
Rule Based IFC
Information Flow
takes place
SaaS
Allows secure
transfer of the
Huge amount of
overhead
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according to
specified rules
Chinese Wall
Policy
Only access given
to objects of
different groups
4. CONCLUSION
Various methods of Information Flow Control have been
developed in the recent decade. This paper looks at some
of the techniques and their attributes. Here, one can
notice that CloudIFC works on service level and assigns
security level numbers to each variable and thus can
provide greater security. On the other hand, FlowK
executes IFC in a much simpler way whereas FlowK2
couples IFC with data analysis that can provide security
along with large scale data processing. In this manner all
the methods have been compared and depending on the
requirements of the cloud provider and the customer
.
sensitive
information
Iaas
Works on Iaas
that’s why very
secure and robust
Complexity is high
since it deals with
individual objects
“Information Technology Interfaces”, 2008. ITI 2008,
pages 31-40, 2008.
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“Information Flow Control for Secure Cloud
Computing” 76 IEEE TRANSACTIONS ON
NETWORK AND SERVICE MANAGEMENT, VOL.
11, NO. 1, MARCH 2014.
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Model
for
the
Cloud”,
http://www.cri.ensmp.fr/classement/doc/A-602.pdf
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