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SybilLimit: A Near-Optimal Social Network 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
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Background: 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 honest malicious launch sybil attack Haifeng Yu, National University of Singapore
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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 [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
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SybilGuard / SybilLimit Basic Insight: Leveraging Social Networks
SybilGuard [SIGCOMM’06] / SybilLimit [Oakland’08]: The first to leverage 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 ! Haifeng Yu, National University of Singapore
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Haifeng Yu, National University of Singapore
Attack Model n honest users: One identity/node each Malicious users: Multiple identities each (sybil nodes) honest nodes sybil nodes sybil nodes may collude – the adversary attack edges malicious users Observation: Adversary cannot create extra edges between honest nodes and sybil nodes Haifeng Yu, National University of Singapore
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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 Haifeng Yu, National University of Singapore
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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.) Haifeng Yu, National University of Singapore
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Example 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
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SybilLimit Contribution 1: “Pushing the Limit”
# sybil nodes accepted (smaller is better) per attack edge total number of attack edges SybilGuard [SIGCOMM’06] SybilLimit [Oakland’08] ~2000 ~10 between unbounded say: times improvement 2. SybilGuard’s guarantee is fundamental in its design and achieving these improvements needs simultaneously address multiple challenges and We also prove that SybilLimit is away from optimal Haifeng Yu, National University of Singapore
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Haifeng Yu, National University of Singapore
Outline 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
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Identity 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
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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 K K honest region sybil region Haifeng Yu, National University of Singapore
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SybilLimit: Strawman Design – Step 2
K: registered keys of sybil nodes 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: registered keys of honest nodes K K K K K K K K K K K K K K K K honest region sybil region Haifeng Yu, National University of Singapore
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Three 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
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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 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
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Three 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
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Random Route: Convergence
f e b d Random 1 to 1 mapping between incoming edge and outgoing edge 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 Haifeng Yu, National University of Singapore
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Registering 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” edge “CD” is the tail of A’s random route w = 3 A B C D D records KA under name “CD” Haifeng Yu, National University of Singapore
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Tails of Sybil Suspects
Imagine that every sybil suspect initiates a random route from itself tainted tail sybil nodes honest nodes total 1 tainted tail Haifeng Yu, National University of Singapore
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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 Convergence: At most w tainted tails per attack edge Regardless of whether sybil nodes follow the protocol Haifeng Yu, National University of Singapore
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Back 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… K: registered keys of sybil nodes K: registered keys of honest nodes K K K honest region K K K K Step 1 achieved ! Haifeng Yu, National University of Singapore
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Haifeng Yu, National University of Singapore
Outline 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
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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 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
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Haifeng Yu, National University of Singapore
Simulation Setup Crawled online social networks used in experiments # nodes # edges Friendster 0.9M 7.8M Livejournal 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 Haifeng Yu, National University of Singapore
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Brief 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
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Haifeng Yu, National University of Singapore
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 Haifeng Yu, National University of Singapore
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