When Encryption is Not Enough Privacy Attacks in Content- Centric - - PowerPoint PPT Presentation

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When Encryption is Not Enough Privacy Attacks in Content- Centric - - PowerPoint PPT Presentation

Dec 06, 2023 16 likes •389 views

When Encryption is Not Enough Privacy Attacks in Content- Centric Networking ACM ICN 2017 1 Privacy with IP GET /a/b/c C S RESPONSE: <data> ACM ICN 2017 2 Privacy with IP secure channel GET /a/b/c C S RESPONSE: <data>

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SLIDE 1

When Encryption is Not Enough Privacy Attacks in Content- Centric Networking

ACM ICN 2017 1

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SLIDE 2

Privacy with IP

2

C S

GET /a/b/c RESPONSE: <data>

ACM ICN 2017

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SLIDE 3

Privacy with IP

3

C S

GET /a/b/c RESPONSE: <data> secure channel

What’s revealed?

  • Source and destination addresses and port #
  • Timing
  • Packet sizes

ACM ICN 2017

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SLIDE 4

Privacy with CCN

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Interest: /a/b/c Content: <data>

ACM ICN 2017

C P

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SLIDE 5

Privacy with CCN

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Interest: /a/b/c Content: <data> encrypted content?

What’s revealed?

  • Consumer and producer locations
  • Timing
  • Packet sizes
  • Producer identity
  • Interest name (and equality)
  • …

ACM ICN 2017

C P

encrypted name?

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SLIDE 6

Motivating Question

6 ACM ICN 2017

What can an adversary do with interest equality alone?

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SLIDE 7

Database Security

ACM ICN 2017 7

C

DB

SELECT * FROM SECRET_TABLE WHERE NAME = <secret> [secret record]

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SLIDE 8

Database Security

ACM ICN 2017 8

C

DB

SELECT * FROM SECRET_TABLE WHERE NAME = <secret> [secret record]

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SLIDE 9

Eavesdropping Attack

ACM ICN 2017 9

C

DB

C C

NAME = 0x1234…

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SLIDE 10

Eavesdropping Attack

ACM ICN 2017 10

0x1234… 0x4356… 0x4356… 0x1234… 0x1234… 0x1234… 0x1234… 0x9981… 0x9981… 0x9271… 0x3233… …

C

DB

C C

NAME = 0x1234…

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SLIDE 11

Empirical Frequency Counts

11 1 2 3 4 5 6 0x1234… 0x9981… 0x4536 0x9271 0x3233 Count

0x1234… 0x4356… 0x4356… 0x1234… 0x1234… 0x1234… 0x1234… 0x9981… 0x9981… 0x9271… 0x3233… …

ACM ICN 2017

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SLIDE 12

Auxiliary Popularity Info

12 0.1 0.2 0.3 0.4 0.5 0.6 Item 1 Item 2 Item 3 Item 4 Item 5 Popularity ACM ICN 2017

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SLIDE 13

Frequency Analysis Attack

13 0.1 0.2 0.3 0.4 0.5 0.6 Item 1 Item 2 Item 3 Item 4 Item 5 Popularity 2 4 6 0x1234… 0x9981… 0x4536 0x9271 0x3233 Count ACM ICN 2017

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SLIDE 14

Frequency Analysis Attack

14 0.1 0.2 0.3 0.4 0.5 0.6 Item 1 Item 2 Item 3 Item 4 Item 5 Popularity 2 4 6 0x1234… 0x9981… 0x4536 0x9271 0x3233 Count ACM ICN 2017

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SLIDE 15

CCN as a Content Database

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C

Network

Get <secret> [Secret Content]

ACM ICN 2017

P C

Application data Encrypted data items Request for encrypted content

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SLIDE 16

CCN as a Content Database

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C

Get <secret> [Secret Content]

P

Request for encrypted content

ACM ICN 2017

C ∈ C P ∈ P

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SLIDE 17

Relevant Distributions

  • Real popularity distribution
  • Auxiliary information distribution
  • Empirical frequency distribution

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DA

A(P)

DE(C) DR(P)

ACM ICN 2017

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SLIDE 18

Global Eavesdropping Adversary

  • Nefarious ISPs, nation states, etc.
  • Questions:

– To what extent does auxiliary information accuracy matter? – To what extent does universe size matter?

18 ACM ICN 2017

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SLIDE 19

Topology

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Consumer Edge Router Core Router

ACM ICN 2017

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SLIDE 20

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Different Auxiliary and Popularity Information

ACM ICN 2017

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SLIDE 21

Matching Auxiliary and Popularity Information

21 ACM ICN 2017

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SLIDE 22

Takeaway

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∆(DA

A(P), DR(P)) ≈ 0.0

∆(DE(C), DA

A(P)) ≈ 0.0

ACM ICN 2017

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SLIDE 23

Auxiliary Information Gap

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0.0 0.2 0.4 0.6 0.8 1.0 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 2000 4000 6000 8000 10000

Statistical Distance Length of Simulation [s] Match Ratio

  • ACM ICN 2017
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SLIDE 24

Content Universe Size

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200 400 600 800 1000 0.0 0.2 0.4 0.6 0.8 1.0 2000 4000 6000 8000 10000

Sample Size Length of Simulation [s] Match Ratio

  • ACM ICN 2017
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SLIDE 25

Takeaway

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Auxiliary information accuracy is not as important as sample size

ACM ICN 2017

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SLIDE 26

Distributed Adversary

  • Access point, enterprise network

middlebox, compromised transit router, etc.

  • Questions:

– Where does the adversary have the best chance at succeeding? – To what extent does caching dampen attack efficacy? – Can content replication (across different producers) help?

26 ACM ICN 2017

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SLIDE 27

Edge vs Inner Router

27 ACM ICN 2017

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SLIDE 28

Cache Presence

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SLIDE 29

Replication

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SLIDE 30

Probing for Popularity

  • What does

do if it has no popularity information?

30 ACM ICN 2017

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SLIDE 31

Probing for Popularity

  • What does

do if it has no popularity information?

  • Exploit caches to learn popularity

– Assumes plaintext and ciphertext equivalents are fetched with equal distributions

31 ACM ICN 2017

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SLIDE 32

Probing Algorithm

32 ACM ICN 2017

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SLIDE 33

Probe Results (S = 50)

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SLIDE 34

Probe Results (S = 100)

34 ACM ICN 2017

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SLIDE 35

Summary

  • Caching both helps and hurts privacy
  • Content replication helps bypass

adversaries

  • Preventing namespace enumeration is key

to mitigating the attack

35 ACM ICN 2017

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SLIDE 36

Future Work

  • Expand simulator and widen experiments
  • Analytically quantify the attack match

percentage given distributions, network topologies, and cache hit probabilities

  • Study attack on CDNs today

36 ACM ICN 2017

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SLIDE 37

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/this/is/the/end/version=0x00/chunk=0x01/PID=0x02 Questions?

ACM ICN 2017