Chulalongkor n University Wongyos Keardsri Department of Computer Engineering Faculty of Engineering, Chulalongkorn University Bangkok, Thailand E-mail: [email protected]An IP Address Anonymization An IP Address Anonymization Scheme Based on Privacy Scheme Based on Privacy Levels Levels Ph.D. Seminar, August 5, 2011
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Chulalongkorn University
Wongyos Keardsri
Department of Computer EngineeringFaculty of Engineering, Chulalongkorn UniversityBangkok, ThailandE-mail: [email protected]
An IP Address An IP Address Anonymization Scheme Anonymization Scheme
Based on Privacy LevelsBased on Privacy Levels
Ph.D. Seminar, August 5, 2011
Chulalongkorn University
Ph.D. Seminar, August 5, 2011
Wongyos Keardsri2
OutlineOutline
• Introduction• Literature Reviews• Anonymization Scheme• Privacy Levels• Anonymization Factors
• Privacy Tree Structures• Network Analysis Functions• Computer Law
• Rule-Based Combination• Results and Discussions• Conclusion
Chulalongkorn University
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• Network Traffic Analysis Packet Sniffer
IP: 161.200.92.41
IP: 161.200.92.30
IP: 161.200.92.59
IP: 161.200.92.62
IP: 161.200.92.45
Capture packetsCapture packets
Analyze packetsAnalyze packets
Anonymize packetsAnonymize packets
IntroductionIntroduction
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• IP Address AnonymizationTo reform the original IP address to the anonymized IP address
P2P (1) Non-anonymization(2) n-Right anonymizationTCP Session History
DNS Full anonymization
System Diagnosis and Anomaly Detection
Intrusion detection Full anonymization
Fault detection
Log analysis
Social network analysis
Behavior analysis
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Network Analysis Functions (Cont)
• Network Analysis Functions Details (Cont)
Group of Functions Functions Privacy Levels
System Report and Display
Network traffic map Full anonymization
Web application report (1) Full anonymization(2) Randomly full anonymization
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• Thailand Computer Crimes Act B.E. 2550
Computer Law
Section Privacy Levels
18(2) (1) n-Right Anonymization (Related with network part)(2) Full Anonymization (Related with person, network and host parts)
18(3) Follow by Privacy Tree Structure
18(4) Follow by Privacy Tree Structure and Network Analysis Function
18(5) Full Anonymization
18(6) Full Anonymization
26-1 Non-anonymization
26-2 n-Right Anonymization
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• Rule-Based Method Represent the conditions of 3 factors into the rules Consider and combine each rule to select final privacy
levels
Rule-Based Combination
• Example of Rule-Based Method
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• Example of the Results of IP Address Anonymization Based on Privacy Levels with 3 Factors
Results and Discussions
• Scenarios: CU Network administrators are a competent official to request packet data from CU-Engineering for analyzing the web site (HTTP) usages
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===================================================================A is referenced IP address of organization which analyzes BB is referenced IP address of organization which is analyzed by A===================================================================Enter Network Address A : 161.200.0.0Enter Mask Address A : 255.255.0.0Enter Network Address B : 161.200.93.0Enter Mask Address B : 255.255.254.0Enter Network Function (NF) : 10Enter Network Function (NF) : 0Enter Law Section : 1Enter Law Section : 0Network Bit of A : 10100001110010000000000000000000Mask Bit of A : 11111111111111110000000000000000Network Bit of B : 10100001110010000101110100000000Mask Bit of B : 11111111111111111111111000000000Privacy Tree Structure (PTS) : (4) Proper Subtree (B in A)Privacy Levels of PTS : (3) n-Right AnonymizationPrivacy Levels of NF : (1) Non-anonymizationPrivacy Levels of LAW : (3) n-Right Anonymization===================================================================Privacy Levels of 3 Factors : (3) n-Right Anonymization===================================================================
Results and Discussions (Cont)
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• Example of Results Given subnet mask is
255.255.0.0 11111111.11111111.00000000.00000000 Given key is 11101010010011010010110110010010
• Using Non-anonymization161.200.92.35 10100001.11001000.01011100.00100011
• Advantage of Our Anonymization Scheme• Applicable to an administrator who analyzes packet
data in different functions• Benefits any organizations in exchanging network data• Appropriates for heavy packet tracers and sniffers
Results and Discussions (Cont)
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• This research proposes 5 privacy levels Non-anonymization n-Left anonymization n-Right anonymization Full anonymization Randomly full anonymization
• This research applies these privacy levels to prefix-preserving IP address anonymization, specifically to Crypto-PAn
ConclusionConclusion
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• Presenting 3 anonymization factors which are used to consider and select appropriate privacy level Privacy tree structure Network analysis functions Computer law
• Combining the anonymization factors by using rule-based method