This document proposes a novel infrastructure for data sanitization in cloud computing. It discusses the need for data sanitization to comply with personal data protection acts and address the issue of data remanence in cloud storage. The proposed mechanism includes a trust model, data sanitization scheme, and monitoring framework. It implements various data overwriting methods and evaluates their performance. Experimental results demonstrate the feasibility of the infrastructure and monitoring of data sanitization in cloud computing. Future work is outlined to further improve performance evaluation and address additional cloud security issues.

1. A Novel Infrastructure for
Data Sanitization
in Cloud Computing
Dr. Cheng-Yuan Ku
Department of Information Management,
National Chung Cheng University, Taiwan,
R.O.C.
Date ： Dec. 25, 2013

2. C. V.
• NCTU, Control Engineering, B.S. in 1987
• Northwestern University, EECS M.S. in 1993
• Northwestern University, EECS Ph.D. in 1995
• Purdue University, Visiting Professor in 2009
• Specialties: Computer and Communication
Network, Information Security, Information
Security Management, E- and M-commerce,
Cloud Computing Security

3. Outline
1. Introduction1. Introduction
2. Related Technology and Works2. Related Technology and Works
3. Proposed Mechanism3. Proposed Mechanism
4. Experimental Results and Future
Work
4. Experimental Results and Future
Work
1. Introduction1. Introduction

4. Background
 Cloud computing service (Mell & Grance, 2011):
 IaaS , PaaS , SaaS
 Cloud Security (Subashini & Kavitha, 2011):
 Data security
 Personal Data Protection Act in Taiwan (Chang,
2012):
 Collecting, processing and using personal data
 A party will be fined up to NT 200 million for violation.
 Government agencies and non-governmental organizations
must provide evidence for handling personal data with due care
in the court. It is not the customer’s responsibility.

5. Motivations
 Data Remanence ：
 Comply with PDPA ：
• What is data remanence ?
– Data sanitization (Kissel et al., 2006)
– One of the most important security issues for cloud computing
• Solutions for cloud computing
– To provide evidence
– To audit data security

6. Outline
1. Introduction1. Introduction
2. Related Works2. Related Works
3. Proposed Mechanism3. Proposed Mechanism
4. Experimental Results and Future
Work
4. Experimental Results and Future
Work
2. Related Technology and Works2. Related Technology and Works

7. Cloud Computing Operating System
VMware vSphere architecture
Source ： Modified from the VMware (2011)
• Windows Azure
• Google Apps
• VMware vSphere
• Amazon WebService

8. Big Data Platform-Hadoop
Hadoop cluster operating
Source ： White (2012)
• Hadoop Distributed File System (HDFS)
• MapReduce

9. Public-Key Infrastructure
Public-key infrastructure model
Source ： Stallings (2012)

10. Monitoring mode (CSA, 2011)
• Database Activity Monitoring (DAM)
• File Activity Monitoring (FAM)

11. Monitoring Approach for Cloud
McAfee database activity monitoring architecture
Source ： McAfee (2012)

12. Data Security Lifecycle
Source ： Modified from the CSA (2011)

13. Data Sanitization (1/2)
 Definition (Kissel, Scholl, Skolochenko, & Li, 2006) ：
• The data sanitization refers to removing remnant
data from storage media.
• Type
– Clearing ： Overwriting
– Purging ： Degaussing
– Destroying ： Disintegration, incineration,
pulverizing, shredding, and melting.

14. Data Sanitization (2/2)
 Overwriting methods ：
• Gutmann
• Schneier
• US DoD 5220-22.M
• VSITR
Overwrite Algorithm
Pass 1-35:
Writes a random character
• Gutmann
Source ： Gutmann (1996)
• Schneier Pass 1: Writes a one
Pass 2: Writes a zero
Pass 3: Writes a random character
Pass 4: Writes a random character
Pass 5: Writes a random character
Pass 6: Writes a random character
Pass 7: Writes a random character
Source ： Schneier (2004)
• US DoD 5220-22.M
Pass 1: Writes a zero and verifies
Pass 2: Writes a one and verifies
Pass 3: Writes a random character
and verifies the write
Source ： DoD and CIA (1995)
• VSITR
Pass 1: Writes a zero
Pass 2: Writes a one
Pass 3: Writes a zero
Pass 4: Writes a one
Pass 5: Writes a zero
Pass 6: Writes a one
Pass 7: Writes a random character
Source ： Hintemann and Faßnacht (2008)

15. Outline
1. Introduction1. Introduction
2. Related Technology and Works2. Related Technology and Works
3. Proposed Mechanism3. Proposed Mechanism
4. Experimental Results and Future
Work
4. Experimental Results and Future
Work
3. Proposed Mechanism3. Proposed Mechanism

16. Mechanism process
Trust ModelTrust Model
Data Sanitization SchemeData Sanitization Scheme
Monitoring Framework DesignMonitoring Framework Design

17. Trust Model

18. Concerns of data sanitization
1) We must know where the data are stored and which
data should be cleared.
2) We need to monitor the clearing process.

19. Design of Monitoring Framework

20. Monitoring Center
• Monitoring Data

21. Data Sanitization Scheme (1/2)
 Data Sanitization Process

22. Data Sanitization Scheme (2/2)
 Data Sanitization by Overwriting
• Customer interface and procedure
– Interface provides customer two choices whether
the data sanitization should be monitored or not.
– Select the number of overwrites, and confirm the
service.
– Customers choose whether the recovery test
report is necessary.

23. Outline
1. Introduction1. Introduction
2. Related Technology and Works2. Related Technology and Works
3. Proposed Mechanism3. Proposed Mechanism
4. Current Status and Future Work4. Current Status and Future Work
4. Experimental Results and Future
Work
4. Experimental Results and Future
Work

24. Implementation and performance evaluation
• Overwriting program
– To propose an efficient overwriting scheme in cloud
• Monitoring center
– Provide big data to the monitoring center to test
• Monitoring agent
– DAM (Database activity monitoring) captures the
metadata packet
– FAM (File activity monitoring) captures the log files

25. Gutmann sanitization performance
Gutmann sanitization performance

26. Schneier sanitization performance
Schneier sanitization performance

27. US DoD 5220-22.M sanitization performance
US DoD 5220-22.M sanitization performance

28. VSITR sanitization performance
VSITR sanitization performance

29. Performance Analysis of Data Sanitization
Sanitization method Time CPU load
Gutmann 31 min 12
Schneier 6 min 2
US DoD 5220-22.M 3 min 1
VSITR 6 min 2

30. System Simulation and Implementation

31. System Simulation and Implementation

32. Cloud Environment - DNS & iSCSI

33. System Establishment - Conversion Interface

34. Data Security Lifecycle Report

35. Future Work
• Further Performance Evaluation
• Cloud Data Lifecycle Auditing
Criteria for ISACA
• Other Cloud Security Issues

36. Thank you for your attention