privacy preserving log file processing in mobile network environment

1 © Nokia Solutions and Networks 2014

Privacy Preserving Log File Processing in Mobile Network EnvironmentShankar Lal16-06-2015


Presentation outline•Introduction and background review•Cases of privacy breach•Statistical analysis over Network trace•Continuous fields anonymisation through Differential Privacy•Discrete fields anonymisation through ℓ-diversity•IP address anonymisation•Future work and conclusion


Introduction and Objectives of this work


Background review

•Data Privacy

•Need of privacy in user data

•Sharing of network trace data

•Tradeoff between data utility and data privacy


Privacy Laws

•PII (Personally Identifying Information) US privacy law

•Personal Data EU Data protection Directive


IP address as Personal Data

•Arguments on both sides

•EU consider it personal (UK is exception)

•US consider it non-personal


Cases of privacy breach from anonymised data sets


There's No Such Thing As An Anonymized Dataset


Netflix anonymous data set and user privacy breach


AOL anonymous data set of user queries


Identification of medical record of former governor of Massachusetts

William WeldFormer governor of Massachusetts


Statistical analysis over Network traces


Sample of a Network Log file


Statistical Analysis on network trace I


Statistical Analysis on network trace II

Most used protocols

Most used packet lengths

Source and destination IP class count

IP class packet length distribution


Functional dependencies between fields


Why packet length and timestamp fields are sensitive?

Certain security incidents have fixed packet length

• Slammer worm 404 bytes • Nachi worm 92 bytes

Timestamp along with IP address reveals communication existed between parties.


Privatizing network trace


Privacy Enhancing Technologies (PETs)•Hashing•Encryption•Randomization and Tokenization•k-anonymity

New Inclusions:•Differential Privacy•ℓ-diversity


k-anonymityMain idea: Generalization Suppression Perturbation


Example on network data set

Sample Data set 2-anonymous data set


Differential Privacy: Anonymisation of continuous fields


Differential PrivacyDifferential privacy algorithm states that probability that dataset D1 produces output C is very close to the probability ofdata set D2 producing same output.

Laplace noise calculation: Scale parameter b = Δ f/ ϵ

Mean μ =0

Δ f =sensitivity of the functionϵ= Privacy parameter

Probability density plots of Laplace distributions


Noise addition through Differential Privacy

Original Distribution ϵ =0.01ϵ =0.1

Packet length field


Noise addition through Differential Privacy

ϵ =0.01ϵ =0.1Original Distribution

Timestamp field


Comparison between original and noisy data

Packet Length Time stamp


ℓ-diversity: Anonymisation of discrete fields


ℓ-diversityA q-block is ℓ-diverse if contains at least ℓ “well-represented” values for the sensitive attribute (in other words, diversity in the sensitive attributes).


Example on network data set

Sample Data set 3-diverse Data set


ℓ-diversity technique

<Change information classification in footer>


Equivalence class creation

Equivalence class name

Protocol name

Protocol name

Protocol name

Protocol name

Transport Protocols

TCP UDP * *

ManagementProtocols

DNS ICMP DHCP ARP

Security Protocols

TLS SSL SSH HTTPS

Mobile Networks Protocols

SSMP GTP GTPv2 UCP

Other Protocols

* * * *


5-diverse data set



Other Noise addition techniques


Zero Mean noise addition


Noise addition by summing LSBs technique

Example: 1414 1414+9= 1423

LSBs


IP address Anonymisation


Anonymising IP addresses

Method: 1. Last octet Obfuscation Method: 2. Transformation to IP class

Goal is to anonymise IP addresses but also preserve network topology information


Final Anonymised data set


Anonymised network trace



Conclusion and Future Work


Conclusion

•Preserving user privacy in a network trace.•Analyzing Functional dependencies between the fields.•Packet length and timestamp anonymisation by Differential Privacy and ℓ-diversity technique.•Deciding the best values of privacy parameter ε •IP addresses anonymisation by last octet obfuscation method

.


Future work

•Framework for calculating best value of epsilon

•Re-identification testing

•Feature extraction/Clustering

•Anomaly detection/Malware Analysis



Thank you

Questions?

privacy preserving log file processing in mobile network environment

Data & Analytics