Assured Cloud Computing
and Information Sharing
Dr. Bhavani Thuraisingham
The University of Texas at Dallas (UTD)
April 2012
Team Members
• Sponsor: Air Force Office of Scientific Research
• The University of Texas at Dallas
– Faculty: Dr. Murat Kantarcioglu; Dr. Latifur Khan; Dr. Kevin
Hamlen; Dr. Zhiqiang Lin, Dr. Kamil Sarac
• Sub-contractors
– Prof. Elisa Bertino (Purdue)
– Ms. Anita Miller, Dr. Bob Johnson (North Texas Fusion Center)
• Collaborators
– Dr. Steve Barker, Kings College, U of London (EOARD)
– Dr. Barbara Carminati; Dr. Elena Ferrari, U of Insubria (EOARD)
– Prof. Peng Liu, Penn State
– Prof. Ting Yu, NC State
Outline
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Objectives
Layered Framework
Data Security Issues for Clouds
Our Research
– FY11
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Cloud-based Assured Information Sharing Demonstration
RDF-based Policy Engine on the Cloud
Secure Query Processing in Hybrid Cloud
CloudMask: Purdue University
Stream-based Malware Detection on the Cloud
Hypervisor (e.g., Xen) Integrity Issues and Forensics in the Cloud
Preliminary Investigation of Identity Management
– FY10
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Secure Querying and Storing Relational Data with HIVE
Secure Querying and Storing RDF in Hadoop with SPARQL
XACML Implementation for Hadoop
Amazon.com Web Services and Security
Accountability and Access Control (Joint with Purdue)
Acknowledgement: Research Funded by Air Force Office of Scientific Research
Objectives
• Cloud computing is an example of computing in which dynamically scalable
and often virtualized resources are provided as a service over the Internet.
Users need not have knowledge of, expertise in, or control over the
technology infrastructure in the "cloud" that supports them.
• Our research on Cloud Computing is based on Hadoop, MapReduce, Xen
• Apache Hadoop is a Java software framework that supports data intensive
distributed applications under a free license. It enables applications to work
with thousands of nodes and petabytes of data. Hadoop was inspired by
Google's MapReduce and Google File System (GFS) papers.
• XEN is a Virtual Machine Monitor developed at the University of Cambridge,
England
• Our goal is to build a secure cloud infrastructure for assured information
sharing applications
Information Operations Across Infospheres:
Assured Information Sharing
Objectives
 Develop a Framework for Secure and Timely Data Sharing
across Infospheres
 Investigate Access Control and Usage Control policies for
Secure Data Sharing
 Develop innovative techniques for extracting information
from trustworthy, semi-trustworthy and untrustworthy
partners
 Budget FY06-8: AFOSR $300K, State Match. $150K
Scientific/Technical Approach
 Conduct experiments as to how much information is lost
as a result of enforcing security policies in the case of
trustworthy partners
 Develop more sophisticated policies based on role-based
and usage control based access control models
 Develop techniques based on game theoretical strategies
to handle partners who are semi-trustworthy
 Develop data mining techniques to carry out defensive
and offensive information operations
Data/Policy for Coalition
Publish Data/Policy
Publish Data/Policy
Publish Data/Policy
Component
Data/Policy for
Agency A
Component
Data/Policy for
Agency C
Component
Data/Policy for
Agency B
Accomplishments
 Developed an experimental system for determining
information loss due to security policy enforcement
 Developed a strategy for applying game theory for semitrustworthy partners; simulation results
 Developed data mining techniques for conducting
defensive operations for untrustworthy partners
Challenges
 Handling dynamically changing trust levels; Scalability
Incentive Issues in Assured Information Sharing
DoD MURI Project 2008 - 2013, AFOSR
Motivation
• Misaligned incentives could be a significant problem in Information Security
– Software bugs vs. software companies’ incentives
• Incentive issues in information sharing have been explored to some extent
– Incentive issues in file sharing p2p networks
• Assured information sharing creates new challenges
– Security considerations vs. utility
Technical Approach
• Verify that the other participants do not lie about their data
– If the data is revealed as it is
• Trust but verify (Our initial results: DKE ’08 paper)
– If the data is not revealed (e.g., SMC techniques are used)
• Non-cooperative computing
• Mechanism design
• SMC with rational adversaries
Layered Framework
Policies
XACML
QoS
User Interface
Resource
Allocation
HIVE/SPARQL/Query
Hadoop/MapReduc/Storage
XEN/Linux/VMM
Risks/
Costs
Cloud
Monitors
Secure Virtual
Network Monitor
Figure2. Layered Framework for Assured Cloud
7/28/2017
7
Secure Query Processing with
Hadoop/MapReduce
• We have studied clouds based on Hadoop
• Query rewriting and optimization techniques designed and
implemented for two types of data
• (i) Relational data: Secure query processing with HIVE
• (ii) RDF data: Secure query processing with SPARQL
• Demonstrated with XACML policies
• Joint demonstration with Kings College and University of Insubria
– First demo (2011): Each party submits their data and policies
– Our cloud will manage the data and policies
– Second demo (2012): Multiple clouds
Fine-grained Access Control with Hive
System Architecture
 Table/View definition and loading,
 Users can create tables as well as
load data into tables. Further, they
can also upload XACML policies
for the table they are creating.
Users can also create XACML
policies for tables/views.
 Users can define views only if
they have permissions for all
tables specified in the query used
to create the view. They can also
either specify or create XACML
policies for the views they are
defining.
 CollaborateCom 2010
SPARQL Query Optimizer for Secure
RDF Data Processing
New Data
Web Interface
Answer
Query
Data Preprocessor
MapReduce Framework
Parser
N-Triples Converter
Query Validator &
Rewriter
Prefix Generator
Predicate Based
Splitter
Predicate Object
Based Splitter
Server
Backend
XACML PDP
Query Rewriter By
Policy
Plan Generator
Plan Executor
To build an
efficient storage
mechanism using
Hadoop for large
amounts of data
(e.g. a billion
triples); build an
efficient query
mechanism for
data stored in
Hadoop; Integrate
with Jena
Developed a query
optimizer and
query rewriting
techniques for
RDF Data with
XACML policies
and implemented
on top of JENA
IEEE Transactions
on Knowledge and
Data Engineering,
2011
Demonstration: Concept of Operation
Agency 1
Agency 2
Agency n
…
User Interface Layer
Relational Data
Fine-grained Access Control
with Hive
RDF Data
SPARQL Query Optimizer
for Secure RDF Data
Processing
RDF-Based Policy Engine
Technology
By UTDallas
Interface to the Semantic Web
Inference Engine/
Rules Processor
e.g., Pellet
Policies
Ontologies
Rules
In RDF
JENA RDF Engine
RDF Documents
RDF-based Policy Engine on the Cloud
Query
Result

Determine how access is granted to a resource as
well as how a document is shared

User specify policy: e.g., Access Control, Redaction,
Released Policy

Parse a high-level policy to a low-level
representation

Support Graph operations and visualization. Policy
executed as graph operations

Execute policies as SPARQL queries over large
RDF graphs on Hadoop

Support for policies over Traditional data and its
provenance

IFIP Data and Applications Security, 2010, ACM
SACMAT 2011
User Interface Layer
High Level Specification
Policy
Translator
Policy Parser Layer
Access Control/ Redaction
Policy (Traditional Mechanism)
Policy / Graph
Transformation Rules
Regular Expression-Query
Translator
Provenance Controller
Data Controller
XML
DB
Policy
Transformation
Layer
...
RDF
DB
RDF
A testbed for evaluating different policy sets over
different data representation. Also supporting
provenance as directed graph and viewing policy
outcomes graphically
Integration with
Assured Information Sharing:
Agency 1
Agency 2
Agency n
…
User Interface Layer
SPARQL Query
RDF Data
and Policies
Policy Translation and
Transformation Layer
RDF Data Preprocessor
MapReduce Framework for
Query Processing
Hadoop HDFS
Result
Secure Storage and Query Processing in a
Hybrid Cloud: Problem Motivation
• The use of hybrid clouds is an emerging trend in cloud computing
– Ability to exploit public resources for high throughput
– Yet, better able to control costs and data privacy
• Several key challenges
– Data Design: how to store data in a hybrid cloud?
• Solution must account for data representation used
(unencrypted/encrypted), public cloud monetary costs and
query workload characteristics
– Query Processing: how to execute a query over a hybrid cloud?
• Solution must provide query rewrite rules that ensure the
correctness of a generated query plan over the hybrid cloud
Research Results
•
Data Design: A user submits data, a query workload, monetary and confidentiality
constraints
– Solve the data partitioning problem in four phases
– Partition the data into several public (Ppu) and private (Ppr) components
– For each partition, Ppu & Ppr, obtain their associated statistics
– Estimate the execution cost of given query workload based on a user’s choice of
confidentiality level as well as the statistics associated with the partition
– Select the best partition as the one that minimizes query workload execution cost without
violating monetary and confidentiality constraints
•
Query Processing: A user submits a query Q
• Solve the query processing problem in four phases
– Query Rearrangement: Use query rewrite rules to transform an original query Q into public
(Qpu) and private (Qpr) query(ies)
– Public Cloud Execution: Execute Qpu on public cloud
– Private Cloud Execution: Execute Qpr on private cloud
– Post-Processing: Combine the results of the execution of Qpu and Qpr into the final result
Hypervisor integrity and forensics
in the Cloud
Applications
Linux
forensics
Solaris
XP
MacOS
OS
integrity
Virtualization Layer (Xen, vSphere)
Hardware Layer
 Secure control flow of hypervisor code
Hypervisor
Cloud integrity &
forensics
 Integrity via in-lined reference monitor
 Forensics data extraction in the cloud
 Multiple VMs
 De-mapping (isolate) each VM memory from physical memory
Cloud-based Malware Detection
Dr. Mehedy
Stream of known malware or
benign executables
Buffer
Unknown
executable
Feature
extraction and
selection using
Cloud
Feature
extraction
Malware
Remove
Training &
Model update
Ensemble of
Classification
models
Classify
Benign
Class
Keep
Cloud-based Malware Detection
• ACM Transactions on Management Information Systems
• Binary feature extraction involves
– Enumerating binary n-grams from the binaries and selecting the best n-grams
based on information gain
– For a training data with 3,500 executables, number of distinct 6-grams can
exceed 200 millions
– In a single machine, this may take hours, depending on available computing
resources – not acceptable for training from a stream of binaries
– We use Cloud to overcome this bottleneck
• A Cloud Map-reduce framework is used
– to extract and select features from each chunk
– A 10-node cloud cluster is 10 times faster than a single node
– Very effective in a dynamic framework, where malware characteristics
change rapidly
Key Features of CloudMask System:
Elisa Bertino Purdue University and
Murat Kantarcioglu, UT Dallas
Fine-grained attribute-based privacy-preserving access control
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•
•
•
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Fine-grained access control: different parts of the data can
be covered by different access control policies
Attribute-based access control: access control policies are
expressed in terms of identity attributes of subjects
accessing the data
Privacy-preserving: the cloud does not learn anything about
the contents of the data and the values of the identity
attributes of users
System Developed is CloudMask
Joint Paper at CollobarateCom 2011
Identity Management
Considerations in a Cloud (with Penn State and NC State)
• Trust model that handles
– (i) Various trust relationships, (ii) access control policies based on roles and
attributes, iii) real-time provisioning, (iv) authorization, and (v) auditing and
accountability.
• Several technologies have to be examined to develop the trust
model
– Service-oriented technologies; standards such as SAML and XACML; and
identity management technologies such as OpenID.
• Does one size fit all?
– Can we develop a trust model that will be applicable to all types of clouds
such as private clouds, public clouds and hybrid clouds Identity architecture
has to be integrated into the cloud architecture.
Directions
• Secure VMM (Virtual Machine Monitor) and VNM (Virtual
Network Monitor)
– Exploring XEN VMM and examining security issues
– Developing automated techniques for VMM
introspection
– Will examine VMM issues January 2012
• Integrate Secure Storage Algorithms into Hadoop (FY
2012)
• Identity Management (FY 2012)
• Technology Transfer through Knowledge and Security
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