Download Balancing Risk and Utility in Flow Trace Anonymization

Balancing Risk and Utility in
Flow Trace Anonymization
Martin Burkhart, ETH Zurich
[email protected]
Joint work with Daniela Brauckhoff, Elisa Boschi, Martin May
Motivation
 Sharing of traffic measurements is crucial

Only a limited set of sources available

Reproducibility of results

Dynamics / variability of traffic

Get the big picture (e.g. Internet Storm Center)

Keep up with globalized attacks (e.g. botnets)
 More and more traces are collected but not shared

Data protection legislation

Security concerns

Competitive advantage
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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State-Of-The-Art: Anonymization
 Black Marking
 Truncation

E.g. last bits of IP addresses
 Permutation

Random

(Partial) Prefix-preserving IP address permutation
 Enumeration

E.g. Timestamps: keep the logical order of events
 Categorization
 Randomization (data mining community)
 K-Anonymity (data mining community)
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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The Tradeoff in Anonymization
 It‘s a trade-off


Risk(t)
RU-Maps
Algorithm X
X
t=0.4
X
t=0.2
t: Anony. Strength
X
t=0.1
X Prefix Pres.
X t=0.7

X-Axis: Utility(t)

Y-Axis: Risk(t)
X Random Perm.
Sweet
Spot
Utility(t)
 Not quantitatively studied, lack of metrics
 Strongly dependent on the application / attacker model
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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A Case Study: IP Address Truncation
 Techniques that permute IP addresses 1:1 are reversible

Characteristic object sizes/frequencies, behavioral profiling, fingerprint
active ports, exploit prefix structure
 Apply IP address truncation and evaluate the risk and utility
dimensions
 Lower risk:
Hosts are aggregated to subnets
IP address
8 bits trunc.
16 bits trunc.
123.45.67.89
123.45.67.0
123.45.0.0
123.45.67.123
123.45.67.0
123.45.0.0
123.45.12.34
123.45.12.0
123.45.0.0
 Lower utility:
Resolution of entities is reduced
 Quantifying the tradeoff: How bad is it in numbers?
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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Internal vs. External Prefixes
Unique Count (log)
Factor 3
Factor 53
 Asymmetry in prefixes

external

Internal (AS 559)
 Is this reflected in

Risk reduction?

Utility reduction?
x=8
Prefix length (32-x)
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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Measuring Utility of Truncated Data
 Specific application: anomaly detection
 Compare detection quality of scans and (D)DoS attacks
in original and truncated data
 Two IP-based metrics

Unique address count

Address entropy
 3 weeks of NetFlow data

~ 43 billion flows

SWITCH network
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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Measuring Detection Quality
 Ground truth: Manual identification of scans/(D)DoS attacks
 Run a Kalman filter on metric timeseries
 Utility measured by AUC (area under the ROC curve)
Vary
threshold
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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Utility of Truncated Data
 Internal metrics degrade faster than external metrics
 Counts degrade faster than Entropy
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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Approximating Risk of Host Identification
 In general: Truncation of x bits leads to
 2^(32-x) prefixes with 2^x addresses per prefix
 But: only a fraction (A) of potential addresses is usually
active
129.130.80.
1, 2, 3, ...
10, 11, 12,
...
240, 241, ...
254, 255
e.g. A = 10%
 Hence, On average A*2^x addresses per prefix
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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Risk of Truncated Data
1
risk ( x)  x
2 A
Ain  10.5% (total: 2.2 million)
Aout  0.08% (total: 4.3 billion)
 Risk for external addresses is higher due to sparcity!
 Constant offset: log 2 (
Martin Burkhart, ETH Zurich
Ain
)7
Aext
Balancing Risk and Utility in Flow Trace Anonymization
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The Risk-Utility Tradeoff
No truncation
4 bits
8 bits
12 bits
16 bits
best tradeoff
Metric
Martin Burkhart, ETH Zurich
x
Utility
Risk
internal entropy 8
0.94
0.035
internal entropy 12
0.87
0.002
external entropy 16
0.97
0.02
Balancing Risk and Utility in Flow Trace Anonymization
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Conclusion
 We made a quantitative evaluation of the risk-utility
tradeoff in anonymization
 Entropy is much more resistant to truncation than unique
counts
 Risk and utility degrade faster for internal addresses
 For detection of scans and (D)DoS attacks, it is possible
to get a good tradeoff with high utility and low risk
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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Thank You for the Attention
Martin Burkhart, ETH Zurich
Balancing Risk and Utility in Flow Trace Anonymization
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