SybilLimit: A Near-Optimal Social SybilLimit: A Near-Optimal Social Network Defense Against Sybil AttacksNetwork Defense Against Sybil Attacks

Haifeng Yu National University of Singapore

Phillip B. Gibbons Intel Research Pittsburgh

Michael Kaminsky Intel Research Pittsburgh

Feng Xiao National University of Singapore

Haifeng Yu, National University of Singapore 2

Background: Sybil AttackBackground: Sybil Attack

Sybil attack: Single user pretends many fake/sybil identities Already observed in real-world

p2p systems

Sybil identities can become a large fraction of all identities “Out-vote” honest users in

collaborative tasks

launchsybilattack

honest

malicious

Haifeng Yu, National University of Singapore 3

Background: Defending Against Sybil AttackBackground: Defending Against Sybil Attack Using trusted central authority to tie identities to

human beings – not always desirable

Much harder without a trusted central authority [Douceur’02] 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

Haifeng Yu, National University of Singapore 4

SybilGuard / SybilLimit Basic Insight: SybilGuard / SybilLimit Basic Insight: Leveraging Social NetworksLeveraging Social Networks

Nodes = identities

Undirected edges = strong mutual trust E.g., colleagues, relatives in

real-world

Not online friends !

SybilGuard [SIGCOMM’06] / SybilLimit [Oakland’08]:

The first to leverage social networks for thwarting sybil attacks with provable guarantees.

Haifeng Yu, National University of Singapore 5

Attack ModelAttack Model

malicioususers

honestnodes

Observation: Adversary cannot create extra edges between honest nodes and sybil nodes

attack edges

n honest users: One identity/node each

Malicious users: Multiple identities each (sybil nodes)

sybil nodes

sybil nodes may collude – the adversary

Haifeng Yu, National University of Singapore 6

SybilGuard/SybilLimit Basic InsightSybilGuard/SybilLimit Basic Insight

honest nodes sybil nodes

Dis-proportionally small cut disconnecting a large number of identities

But cannot search brute-force…attack

edges

Haifeng Yu, National University of Singapore 7

SybilGuard / SybilLimit End GuaranteesSybilGuard / 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.)

Haifeng Yu, National University of Singapore 8

Example Application ScenariosExample Application Scenarios

If # of sybil nodes accepted

Then applications can do

< n/2 byzantine consensus

< n majority voting

< n/c for some constant c secure DHT [Awerbuch’06, Castro’02,

Fiat’05]

… …

Haifeng Yu, National University of Singapore 9

total number of attack edges

SybilGuard [SIGCOMM’06]

SybilLimit [Oakland’08]

nnOg log/ )log( nn )(log n

)(log nunbounded

# sybil nodes accepted (smaller is better) per attack edge

nn log/ nnO log/

g between

and

g

~2000 ~10

~10

SybilLimit Contribution 1: “Pushing the Limit” SybilLimit Contribution 1: “Pushing the Limit”

We also prove that SybilLimit is away from optimal)(log nO

Haifeng Yu, National University of Singapore 10

OutlineOutline

Motivation, basic insight, and end guarantees

SybilLimit Contribution 1: “Pushing the Limit” The near-optimal SybilLimit design

SybilLimit Contribution 2: Validation on Real-World Social Networks

Haifeng Yu, National University of Singapore 11

Identity Registration in SybilLimitIdentity Registration in SybilLimit

Each node (honest or sybil) has a locally generated public/private key pair

“Identity”: V accepts S = V accepts S’s public key KS

We do not assume/need PKI

In SybilLimit, every suspect S “registers” KS on

some other nodes

Haifeng Yu, National University of Singapore 12

SybilLimit: Strawman Design – Step 1SybilLimit: Strawman Design – Step 1

Ensure that sybil nodes (collectively) register only on limited number of honest nodes

Still provide enough “registration opportunities” for honest nodes

sybil regionhonest region

K: registered keys of sybil nodes

K K

K

KK

K

K K

K

K

K

K

K

KK K

K: registered keys of honest nodes

Haifeng Yu, National University of Singapore 13

SybilLimit: Strawman Design – Step 2SybilLimit: Strawman Design – Step 2

Accept S only if KS is

register on sufficiently many honest nodes

Without knowing where the honest region is !

Circular design? We can break this circle…

K K

K

KK

K

K K

K

K

K

K

K

KK K

sybil regionhonest region

K: registered keys of sybil nodes

K: registered keys of honest nodes

Haifeng Yu, National University of Singapore 14

Three Interrelated Key TechniquesThree Interrelated Key Techniques

Technique 1: Use the tails of random routes for registration Will achieve Step 1

Random routes are from SybilGuard

Novelty: The use of tails

Novelty: The use of multiple independent instances of shorter random routes

Haifeng Yu, National University of Singapore 15

Three Interrelated Key TechniquesThree Interrelated Key Techniques Technique 2: Use intersection condition and

balance condition to verify suspects Will break the circular design and achieve Step 2

SybilGuard also has intersection condition

Novelty: Intersection on edges

Novelty: SybilGuard has no balance condition

Technique 3: Use benchmarking technique to estimate unknown parameters Breaks another seemingly circular design…

Novelty: SybilGuard has no such technique

Haifeng Yu, National University of Singapore 16

Three Interrelated Key TechniquesThree Interrelated Key Techniques

Technique 1: Use the tails of random routes for registration Will achieve Step 1

Random routes are from SybilGuard

Novelty: The use of tails

Novelty: The use of multiple independent instances of shorter random routes

Haifeng Yu, National University of Singapore 17

Random 1 to 1 mapping between incoming edge and outgoing edge

Random Route: ConvergenceRandom Route: Convergence

a db ac bd c

d ee df f

a

b

c

d e

f

randomized

routing table

Using routing table gives Convergence Property:

Routes merge if crossing the same edge

Haifeng Yu, National University of Singapore 18

Registering Public Keys with TailsRegistering Public Keys with Tails Every node initiates a “secure” random route of length w from itself

See paper for discussion on w

See paper for how to make it “secure”

A B C D

edge “CD” is the tail of A’s random routew = 3

D records KA

under name “CD”

Haifeng Yu, National University of Singapore 19

Tails of Sybil SuspectsTails of Sybil Suspects Imagine that every sybil suspect initiates a

random route from itself

total 1 tainted tail

honestnodes

sybilnodes

tainted tail

Haifeng Yu, National University of Singapore 20

Counting The Number of Tainted TailsCounting The Number of Tainted Tails

Claim: There are at most w tainted tails per attack edge Convergence: At most w tainted tails per attack edge

Regardless of whether sybil nodes follow the protocol

honestnodes

sybilnodes

attack edge

Haifeng Yu, National University of Singapore 21

Back to the Strawman Design Step 1Back to the Strawman Design Step 1

# of K ’s gw Independent of # sybil

nodes

# of K ’s n – gw From “backtrace-ability”

property of random routes

See paper…

honest region

K

K

K

K

K

K

KStep 1 achieved !

K: registered keys of sybil nodes

K: registered keys of honest nodes

Haifeng Yu, National University of Singapore 22

OutlineOutline SybilLimit Contribution 1: “Pushing the Limit”

Independent instances, intersection condition, balance condition, benchmarking technique

Avoids multiple seemingly circular designs (hardest part…)

Also see paper for Performance overheads…

Near-optimality …

SybilLimit Contribution 2: Validation on Real-World Social Networks

Haifeng Yu, National University of Singapore 23

Validation on Real-World Social NetworksValidation 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 and # honest nodes accepted

Synthetic social networks – proof in [SIGCOMM’06]

Real-world social networks? Social communities, social groups, ….

Haifeng Yu, National University of Singapore 24

Simulation SetupSimulation Setup

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

# nodes # edges

Friendster 0.9M 7.8M

Livejournal 0.9M 8.7M

DBLP 0.1M 0.6M

Crawled online social networks used in experiments

Haifeng Yu, National University of Singapore 25

Brief Summary of Simulation ResultsBrief Summary of Simulation Results

In all cases we experimented with:

Fraction of honest nodes accepted: ~95%

# sybil nodes accepted: ~10 per attack edge for Friendster and LiveJournal

~15 per attack edge for DBLP

Haifeng Yu, National University of Singapore 26

ConclusionsConclusions

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