Defending Against Sybil Attacks via Social Networks

1
Defending Against Sybil Attacks via Social
Networks

• Haifeng Yu
• School of Computing
• National University of Singapore

2
Acknowledgments

• Talk based on three papers
• SIGCOMM06, ToN08 (SybilGuard)
• IEEE SP08 (SybilLimit)
• Available on my homepage google my name
• Co-authors
• Phillip B. Gibbons
• Michael Kaminsky
• Feng Xiao
• Abie Flaxman

3
Background Sybil Attack
honest

• Sybil attack Single user pretends many
  fake/sybil identities
• I.e., Creating multiple accounts
• Already observed in real-world p2p systems
• Sybil identities can become a large fraction of
  all identities

malicious
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Background Sybil Attack

• Enables malicious users to easily out-vote
  honest users
• Byzantine consensus exceed the 1/3 threshold
• Majority voting cast more than one vote
• DHT control a large portion of the ring
• Recommendation systems manipulate the
  recommendations

5
Background Defending Against Sybil Attack

• Using trusted central authority to tie identities
  to human beings not always desirable
• Much harder without a trusted central authority
  Douceur02
• Resource challenges not sufficient
• IP address-based approach not sufficient
• Widely considered as real challenging
• Over 40 papers acknowledging the problem of sybil
  attack, without having a distributed solution

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SybilGuard / SybilLimit Basic Insight
Leveraging Social Networks
SybilGuard / SybilLimit is the first to use
social networks for thwarting sybil attacks with
provable guarantees.

• Nodes identities
• Undirected edges strong mutual trust
• E.g., colleagues, relatives in real-world
• Not online friends!

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SybilGuard / SybilLimit Basic Insight

• n honest users One identity/node each
• Malicious users Multiple identities each (sybil
  nodes)

honest nodes
attack edges
malicious users
Observation Adversary cannot create extra edges
between honest nodes and sybil nodes
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SybilGuard/SybilLimit Basic Insight

• Dis-proportionally small cut disconnecting a
  large number of identities

But cannot search brute-force
attack edges
honest nodes
sybil nodes
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SybilGuard / SybilLimit End Guarantees

• Completely decentralized
• Enables any given verifier node to decide whether
  to accept any given suspect node
• Accept Provide service to / receive service from
• Ideally Accept and only accept honest nodes
  unfortunately not possible
• SybilGuard / SybilLimit provably
• Bound of accepted sybil nodes (w.h.p.)
• Accept all honest nodes except a small ? fraction
  (w.h.p.)

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Example Application Scenarios
If of sybil nodes accepted Then applications can do
lt n majority voting
lt n/2 byzantine consensus
lt n/c for some constant c secure DHT Awerbuch06, Castro02, Fiat05

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SybilGuard vs. SybilLimit
sybil nodes accepted (smaller is better) per
attack edge
total number of attack edges SybilGuard SIGCOMM06 SybilLimit Oakland08


between
unbounded
and
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Outline

• Motivation, basic insight, and end guarantees
• SybilLimit design
• Will focus on intuition
• Evaluation results on real-world social networks

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Cryptographic Keys

• Each edge in social network corresponds to a
  symmetric edge key
• Established out of band
• Each node (honest or sybil) has a locally
  generated public/private key pair
• Identity V accepts S V accepts Ss public
  key KS
• When running SybilLimit, every suspect S is
  allowed to register KS on some other nodes

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SybilLimit Strawman Design Step 1
K registered keys of sybil nodes

• Ensure that sybil nodes (collectively) register
  only on limited number of honest nodes
• Still provide enough registration opportunities
  for honest nodes

K registered keys of honest nodes
K
K
K
K
K
K
sybil region
honest region
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SybilLimit Strawman Design Step 2
K registered keys of sybil nodes

• Accept S iff KS is register on sufficiently many
  honest nodes
• Without knowing where the honest region is !
• Circular design? We can break this circle

K registered keys of honest nodes
K
K
K
K
K
K
K
K
K
K
K
K
K
K
K
K
sybil region
honest region
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Three Interrelated Key Techniques

• Technique 1 Use the tails of random routes for
  registration
• Will achieve Step 1
• SybilGuard novelty Random routes
• SybilLimit novelty The use of tails
• SybilLimit novelty The use of multiple
  independent instances of shorter random routes

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Three Interrelated Key Techniques

• Technique 2 Use intersection condition and
  balance condition to verify suspects
• Will break the circular design and achieve Step 2
• SybilGuard novelty Intersection on nodes
• SybilLimit novelty Intersection on edges
• SybilLimit novelty Balance condition
• Technique 3 Use benchmarking technique to
  estimate unknown parameters
• Breaks another seemingly circular design
• SybilLimit novelty Benchmarking technique

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Random Route Convergence
f
a
e
b
d
a ? d
d ? e
c
randomized routing table
b ? a
e ? d
c ? b
f ? f
d ? c
Using routing table gives Convergence Property
Routes merge if crossing the same edge
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Securely Registering Public Keys
record KA under name C?D
A
i 1 KA
i 2 KA
i 3 KA
i 3 KA
To register KA, A initiates a random route
(assuming w 3)

• All random routes in SybilLimit are of length w
• All nodes know w
• Nodes communicate via authenticated channels

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Tails of Sybil Suspects

• Imagine that every sybil suspect initiates a
  random route from itself

sybil nodes
honest nodes
total 1 tainted tail
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Counting The Number of Tainted Tails
attack edge
honest nodes
sybil nodes

• Claim There are at most w tainted tails per
  attack edge
• Proof By the Convergence property
• Regardless of whether sybil nodes follow the
  protocol

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Back to the Strawman Design Step 1

• of K s ? g?w
• Independent of sybil nodes
• of K s ? n g?w
• From backtrace-ability property of random
  routes
• See paper

K registered keys of sybil nodes K registered
keys of honest nodes
K
K
K
K
honest region
K
K
K
Step 1 achieved !
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Independent Instances

• SybilLimit uses independent instances
  of the registration protocol
• m of edges in the honest region
• Number of Ks
• Number of Ks
• Goal Accept S iff KS is registered on
  tails in the honest region
• Sybil suspects accepted
• Honest suspects accepted

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Three Techniques

• Technique 1 Use novel random routes to register
  public keys
• Will achieve Step 1
• Technique 2 Use intersection condition and
  balance condition to verify suspects
• Challenge SybilLimit does not know which region
  is the honest region
• Technique 3 Use benchmarking technique to
  estimate unknown parameters

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The Intersection Condition

• Verifier V obtains tails by doing
  random routes of length w
• Using different instances see paper
• Some tails are in the sybil region ignore for
  now
• S satisfies intersection condition if
• Ss and Vs tails intersect
• Ss public key is registered with the
  intersecting tail

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Intersection Condition Verification Procedure
S
V
3.common tail E?F
4 messages involved
S satisfies intersection condition
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Leveraging Known Random Walk Theory

• (Approximate) Theorem
• If w is roughly the mixing time of the social
  network, then all tails (Vs and Ss) are roughly
  uniformly random edges
• If social networks have mixing
  time, then

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Leveraging a Sharp Distribution

• Assuming V has tails in the honest
  region

Intersection prob p
1.0
Birthday paradox
This is why SybilLimit does edge intersection
0
of Ss tails in honest region
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Back to the Strawman Design Step 2
K registered keys of sybil nodes

• Accept S iff KS is register on sufficiently many
  honest nodes
• Sufficiently many
• Intersection occurs iff S has tails
  in the honest region

K registered keys of honest nodes
K
K
K
K
K
K
K
K
K
K
K
K
K
K
K
K
sybil region
honest region
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Omitted Challenges

• Some of Vs tails are in the sybil region
• We do not know which tails are in the sybil
  region
• Balance condition hardest part to prove in
  SybilLimit
• Adversary has many strategies to allocated the
  tainted tails
• Tainted tails are not uniformly random
• See paper for details

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Three Interrelated Key Techniques

• Technique 1 Random routes
• Technique 2 Intersection condition and balance
  condition
• Technique 3 Novel and counter-intuitive
  benchmarking technique
• Avoids another seemingly circular design
• See paper
• Claims on near-optimality See paper

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Performance Aspects

• Random routes are performed only once
• Re-do only when social network changes
  infrequently
• Can be done incrementally
• Doing random routes is not time-critical
• Only delays a new suspect being accepted
• Churn is a non-problem
• Verification involves O(1) messages
• See paper

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Outline

• Motivation, basic insight, and end guarantees
• SybilLimit design
• Evaluation results on real-world social networks

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Validation on Real-World Social Networks

• SybilGuard / SybilLimit assumption Honest nodes
  are not behind disproportionally small cuts
• Rigorously Social networks (without sybil nodes)
  have small mixing time
• Mixing time affects sybil nodes accepted
• Synthetic social networks proof in SIGCOMM06
• Real-world social networks?
• Social communities, social groups, .

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Simulation Setup
Crawled online social networks used in experiments
nodes edges
Friendster 0.9M 7.8M
Livejournal 0.9M 8.7M
DBLP 0.1M 0.6M

• We experiment with
• Different number and placement of attack edges
• Different graph sizes -- full size to 100-node
  sub-graphs
• Sybil attackers use the optimal strategy

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Brief Summary of Simulation Results

• In all cases we experimented with
• Average honest verifier accepts 95 of all
  honest suspects
• Average honest suspect is accepted by 95 of all
  honest verifiers
• sybil nodes accepted
• 10 per attack edge for Friendster and
  LiveJournal
• 15 per attack edge for DBLP

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Other Social Networks?

• Other social networks likely to have small mixing
  time too (DBLP as a worst-case)
• What if the mixing time is large?
• Graceful degradation of SybilLimits guarantees
  -- Accept more sybil nodes

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Conclusions

• Sybil attack
• Widely considered as a real and challenging
  problem
• SybilLimit Fully decentralized defense protocol
  based on social networks
• Provable near-optimal guarantees
• Experimental validation on real-world social
  networks
• Future work Implement SybilLimit with real apps

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Post Doc Opening

• NUS Ranked 31st globally by Newsweek
• E.g., we have 11 SIGMOD papers in 2008
• I have post doc opening in distributed systems
  and distributed algorithms
• Minimum 1 year, renewable up to multiple years
• 2 years funding already committed
• Main job duty Publish in top venues
• Help you to build up track record for career
  after post doc
• Salary Comparable (if not better) than US post
  docs
• Singapore living cost and tax are lower than US
• Contact me to inquire or apply google my name