Defending Sybil Attack in Peer2Peer Networks

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Defending Sybil Attack in Peer2Peer Networks
Distributed Search Techniques
Md. Tanvir Al Amin 04 09 05 2064 Shah Md. Rifat
Ahsan 10 09 05 2060 Adviser Dr. Reaz Ahmed
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Sybil Attack

• A fundamental problem in distributed systems.
• Single user assumes 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
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Sybil attack

• Present in both Application level and P2P
  Networking
• Attacker creates many fake/sybil identities
• Many cases of real world attacks Digg, Youtube
• Several research works shown how easy it was to
  subvert DHT like Chord or Kademlia using Sybil
  Attack

Automated sybil attack on Youtube for 147!
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Defending against Sybil attacks

• Traditional solutions rely on central trusted
  authorities
• Runs counter to open membership policies of OSNs
• Recent proposals leverage social networks
• Lots of research activity recently
• Each optimized under assumptions about the graph
  structure
• Each evaluated on different datasets

SybilGuard SIGCOMM06 SybilLimit
Oakland08 Ostra NSDI08 SumUp
NSDI09 SybilInfer NDSS09 Whanau
NSDI10 MobID INFOCOM10
All schemes analyze the graph structure to
isolate Sybils
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Defending against Sybil attacks

• Recent proposals leverage social networks
• Key Insight Social links are hard to acquire in
  abundance
• Look for small cuts in the graph
• Conversely, look for communities around known
  trusted nodes
• Dunbars Number
• Power law node degrees

Links difficult to create
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How Do Social Networks look like
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SybilGuard Defending Against Sybil Attacksvia
Social Networks

• Sybilguard is a system for detecting Sybil nodes
  in social graphs.
• Features of Sybil Guard
• SybilGuard enables an honest node to identify
  other nodes
• Verifier node V can verify if suspect node S is
  malicious
• Guaranteed bound on number of sybil groups
• Guaranteed bound on size of sybil groups
• Completely decentralize
• Key Insight
• 1. Use a social network to limit Sybils
• 2.Social links are hard to acquire in abundance
• 3.Look for small cuts in the graph

DBLP Network
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Dunbars number

• Limits the of stable social relationships a
  user can have
• To less than a couple of hundred
• Linked to size of neo-cortex region of the brain
• Observed throughout history since hunter-gatherer
  societies
• Roughly reported to be 150
• Also observed repeatedly in studies of OSN user
  activity
• Users might have a large number of contacts
• But, regularly interact with less than a couple
  of hundred of them

9
Power-law node degrees
U.S. highways
U.S. Airlines
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Path lengths and diameter

• all major networks have short path length from
  4.25 5.88
• six degrees of separation

Facebook, 4.2 million for Octorber 2007, 6.12 from
http//blog.paulwalk.net/2007/10/08/no-degrees-of-
separation/
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Implications of Path lengths and diameter
The small diameter and path lengths of social
networks are likely to impact the design of
techniques for finding paths in such networks
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Link degree correlations

• high-degree nodes tend to connect to other
  high-degree nodes ? OR
• high-degree nodes tend to connect to low-degree
  nodes ?
• In real society the former theory is true.
• By virtue of two metrics the scale-free metric
  and the assortativity.
• Suggests that there exists a tightly-connected
  core of the high-degree nodes which connect to
  each other, with the lower-degree nodes on the
  fringes of the network.
• The next question How big the core is

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Implications of Link degree correlationsSpread
of Information
A Measurement-driven Analysis of Information
Propagation in the Flickr Social Network WWW
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Densely connected core

• the graphs have a densely connected core
  comprising of between 1 and 10 of the highest
  degree nodes such that removing this core
  completely disconnects the graph.

Sub logarithmic growth
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Densely connected core

• the graphs have a densely connected core
  comprising of between 1 and 10 of the highest
  degree nodes such that removing this core
  completely disconnects the graph.

Sub logarithmic growth
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Implications of densely connected core

• Network contains dense core of users
• Core necessary for connectivity of 90 of users
• Most short paths pass through core
• Could be used for quickly disseminating
  information
• So 10 at core
• What about remaining nodes (90 at fringe)

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What does the structure look like
the networks contain a densely connected core of
high-degree nodes and that this core links
small groups of strongly clustered, low-degree
nodes at the fringes of the network.
octopus
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Mixing time

• Random walk choose each hop randomly
• Mixing time hops until uniform probability
• Fast mixing network mixing time O(log n)

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Sampling by random walks

• A random walk has o(1) chance of escaping
• True when g bounded by o(n/log n)
• Of r walks, (1-o(1))r O(r) end nodes are good!
• Cant distinguish good from bad nodes in set

Honest region
Sybil region
escaping paths
non-escaping path
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Creating Social Link Is Hard
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Social links maintained over Internet
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Social network

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Social network
Honest region
Attack edges

A malicious user fools an honest user Creates an
attack edge
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Sybil resilience group attachment theory

• Sybil schemes find bond groups around a trusted
  node
• But, these are only a fraction of all honest
  nodes
• Bond groups are hard for Sybils to infiltrate
• Not the case with identity groups

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SybilGuard

• Yu, Kaminsky, Gibbons, Flaxman, Sigcomm 2006

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Problem Formulation and Objective

• Social network
• n honest human users
• 1 malicious users multiple sybil identities
• SybilGuard enables an honest node to identify
  other nodes
• Verifier node V can verify if suspect node S is
  malicious

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SybilGuard

• Guaranteed bound on number of sybil groups
• Divides n nodes into m equivalence classes
• A group is sybil if it contains 1 sybil nodes
• Guaranteed bound on size of sybil groups
• In a group, at most w sybil nodes
• Completely decentralized
• An honest node accepts honest nodes with high
  probability
• Rejects malicious nodes with high probability
• Accepts bounded number of sybil nodes

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Random Routes

• Foundation of SybilGuard different from random
  walk
• Random route begins at a random edge of a node
• At every node
• For an incoming edge i, there is a unique
  outgoing edge j
• Thus, input to output is one-to-one mapped
• A node A with d neighbors uniformly randomly
  chooses a permutation x1,x2, . . . ,xd among
  all permutations of 1,2, . . . ,d.
• If a random route comes from the ith edge, A uses
  edge xi as the next hop.

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SybilGuard Algorithm

• Attack Model
• n honest users One identity/node each
• Malicious users Multiple identities each (sybil
  nodes)
• node A verify node B
• A computes d random routes (length w)
• B computes d random routes (length w)
• If d/2 random routes intersects, accept S
• Else reject S
• If few attack edges, then a sybil nodes random
  route is less likely to reach honest region
• And vice-versa

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Main Assumptions of SybilGuard
Attack edges
Honest Nodes
Sybil Nodes
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Properties of Random Routes

• Convergence
• Once two routes merge, they will remain merged
• Routes are back-traceable
• There can be only one route with length w that
  traverses e along the given direction at its ith
  hop
• If two random routes ever share an edge in the
  same direction, then one of them must start in
  the middle of the other
• Cycles can exist, but with low probability
• Prob. (diameter k cycle) 1/d(k-2)

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Sybilguard Algorithm
Steps 2 Choose a verifier (A) and a suspect
(B). A and B send out random walks of a certain
length (2). Look for intersections. A knows B is
not a Sybil because multiple paths intersect and
they do so at different nodes.

• Step 1
• Bootstrap the network.
• All users exchange signed keys.
• Key exchange implies that both parties are human
  and trustworthy.

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SybilGuard Algorithm, cont.
B
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SybilGuard Caveats

• Bootstrapping requires human interaction.
• Assumes short random walks lie mostly in the
  honest region
• Results in poor threshold to colluding attackers.
• In a million node network ,each attack edge
  accepts nearly 2000 sybil nodes.
• In million node network , SybilGuard cannot bound
  the number of sybils at all if there are gt 15,000
  attack edges .

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SybilLimitA Near-Optimal Social Network Defense
Against Sybil Attacks
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SybilLimitA Near-Optimal Social Network Defense
Against Sybil Attacks

• Motivation To mitigate the problems of
  SybilGuard.
• Basic insight Social network (same as
  SybilGuard)
• SybilLimit Novelity
• 1. use many random routes but shorter ones.
• 2. intersect edges not nodes
• 3. limit how often each edge is used.

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Identity Registration

• Each node (honest or sybil) has a locally
  generated public/private key pair
• Identity V accepts S means V
  accepts Ss public key KS
• NO assumption/need PKI
• Every suspect S registers KS on some other nodes

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Registration Goals
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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Acceptance Criteria
K registered keys of sybil nodes
K registered keys of honest nodes

• Accept S only if KS is register on sufficiently
  many 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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Key Idea

• Take random walks of w
  hops
• Honest nodes likely to remain in honest region
• Sybil nodes must cross an attack edge to reach
  honest region

• Register key at last hop of walk

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Verification Procedure
S
V
3.common tail E?F
4 messages involved
V accepts S Tails intersect key
registered
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Sybil nodes accepted
Attack edges SybilGuard SybilLimit



between
unbounded
and
unbounded
unbounded
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SybilInfer How to Win the Zombie Wars!

• Prateek Mittal, George Danezis (MSRC
  Intern) (MSR Cambridge)

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SybilInfer

• Work from UIUC and Microsoft Research
• A centralized algorithm
• Uses the fast mixing properties of social network
  to design a Bayesian Classifier
• Classify nodes

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Formal Model

• Assign probabilities of cuts being honest
• Using Bayes Theorem, we have that
• Next Challenge Model

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Formal Model
X
X
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Sybil proof DHT
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Distributed Hash Table

• Interface PUT(key, value), GET(key)?value
• Route to peer responsible for key

GET( sip//alice_at_foo )
PUT( sip//alice_at_foo, 18.26.4.9 )
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DHTs are subject to the Sybil attack

• Attacker creates many pseudonyms
• Disrupts routing or stabilization

IDt
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The Sybil attack on open DHTs
Brute-force attack
Clustering attack
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Sybil Proof DHT

• How to build a sybil resilient DHT ?

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Works from MIT PDOS Group

• Parallel and Distributed Operating Systems
• Quest to build Sybil Proof DHT
• Sybil-resistant DHT routing 2005
• A Sybil proof One hop DHT SocialNets 2008
• Whanau NSDI 2010

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A Sybil proof one hop DHT

• Motivation
• SybilGuard/SybilLimitNot a DHT, but a general
  Sybil defense
• Honest node accepts at most O(g log n) Sybils
• Features
• DHTs are subject to the Sybil attack
• Social networks provide useful information
• Created a Sybil-resistant one-hop DHT
• Resistant to g o(n/log n) attack edges
• Table sizes and routing BW O(vn log n)
• Uses O(1) messages to route

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Basic one-hop DHT design

• Construct finger table by r random walks
• Route to t by asking all fingers about t
• If r O(vn log n), some finger knows t WHP
• Adversary cannot interfere with routing

s
ts IP address
r
forwarded message from s
r
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Properties of this solution

• Finger table size r O( )
• Bandwidth to construct O(r log n) bits
• Bandwidth to query O(r) messages
• Probability of failure 1/poly(n)

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Whanau A Sybil-Proof Distributed Hash Table

• Chris Lesniewski-Laas M. Frans Kaashoek NSDI 2010

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Contribution

• Whanau an efficient Sybil-proof DHT protocol
• GET cost O(1) messages, one RTT latency
• Cost to build routing tables O(vN log N)
  storage/bandwidth per node (for N keys)
• Oblivious to number of Sybils!
• Proof of correctness
• PlanetLab implementation
• Large-scale simulations vs. powerful attack

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Social network
Honest region
Attack edges

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Random walks
c.f. SybilLimit Yu et al 2008
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Building tables using random walks
c.f. SybilLimit Yu et al 2008

• What have we accomplished?
• Small fraction (e.g. lt 50) of bad nodes in
  routing tables
• Bad fraction is independent of number of Sybil
  nodes

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key value


Put(key, value)
Put Queue
key
Setup
Lookup
value
Social Network
Routing Tables
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Routing table structure

• O(vn) fingers and O(vn) keys stored per node
• Fingers have random IDs, cover all keys WHP
• Lookup query closest finger to target key

Aardvark
Zyzzyva
Finger tables (ID, address)
Key tables (key,value)
Kelvin
Keynes
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From social network to routing tables

• Finger table randomly sample O(vn) nodes
• Most samples are honest

ID IP address
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Honest nodes pick IDs uniformly
Plenty of fingers near key
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Sybil ID clustering attack
Many bad fingers near key
Hypothetical scenario 50 Sybil IDs, 50 honest
IDs
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Honest layered IDs mimic Sybil IDs
Layer 0
Layer 1
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Every range is balanced in some layer
Layer 0
Layer 1
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Two layers is not quite enough
Layer 0
Layer 1
Ratio 1 honest 10 Sybils
Ratio 10 honest 100 Sybils
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Log n parallel layers is enough
Layer 0
Layer 1
Layer 2
Layer L

• log n layered IDs for each node
• Lookup steps
• Pick a random layer
• Pick a finger to query
• GOTO 1 until success or timeout

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From Social relations to Routing Tables
key value


Put(key, value)
Put Queue
key
Setup
Lookup
value
Social Network
Routing Tables
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Problems

• Whanaus goal is to create a Sybil proof DHT
• Which ensures delivery
• Whanau uses the idea of random walk in fast
  mixing graphs
• Whanau has changed the basic structure of DHT
• Tables contain O(vn log n) entries !!
• The DHT has become a one hop DHT
• But O(vn) entries are insane !!
• Think of a DHT with 100000000 users
• How to handle churn ??

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OuR IDEA of a sybil proof p2p Application
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Problems of present solutions

• SybilGuard and SybilLimit results in lots of
  false positives
• The system should try to capture Sybil-like
  behavior.
• Though sybil-like behavior is also not an
  indicator, but together with the other evidence,
  it should work stronger.
• Whanau changes the basic structure of DHT to a
  multi-layer, ID unordered, one-hop one.
• If one need to alter the structure of some DHT,
  it effectively means that the its structure has a
  inherent design flaw inside, which makes it
  vulnerable.

74
Problems of the present solutions

• As a design methodology, open systems are often
  uncontrollable.
• Systems with feedback stabilize easily. There
  should be a feedback mechanism via ratings which
  is  not present in any of the protocols.
• In whanau, a node have to save O(sqrt(n) log n)
  nodes the finger table.
• 108 members in a DHT is possible in the upcoming
  era of distributed systems, and far more members
  will be a commonl case when IPv6 will become
  general. Having a table size growing at this rate
  doesnt scale well.

75
Our Idea

• Primarily, we dont want to change the basic
  structure of a DHT to apply security patches in
  it.
• For a P2P application, our idea is to divide the
  responsibility in three layers.
• Network-Access Layer
• DHT Layer
• Application Layer

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Security

• Security is imposed via four mechanism
• Admission control at Application layer
• Social trust, Friendship, controlled at
  Application layer
• Application Object (Files or other shared items)
  rating and Rating behavior recording (determined
  at application layer)
• Routing behavior rating, Query reply rating and
  rating behavior recording (determined at DHT
  Layer)

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Security

• A new node can not perform any DHT lookup, or can
  not perform in any DHT level ratings.
• May or may not perform Application object
  ratings, depending on application policy
• May not have full permissions capabilities at
  application (depending on the application
  policy).  
• Can not make social relationships with another
  new or immature node

78

• Explanation of the lookup process for immature
  nodes
• Any friend of an immature node should work as
  the proxy for it (only for DHT lookup process).
  Content object / Shared files will be exchanged
  directly, but as the immatured nodes can not have
  acess to DHT.
• All lookup for them will be done by its matured
  friends. It may want to load balance the queries
  among the friends. And the content / files shared
  by the child, will also be represented by their
  parents / friends. Any lookup of that content
  should return the IP address of a friend /
  parent.

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• However, the friend / parent will then provide
  the IP address of the child. Then the file
  transfer can work directly. However, based on the
  behavior of its provided content, first class
  citizens / DHT members will rate it. If the new
  node gets bad rating, its probability of becoming
  a DHT member will be less.

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Our idea of a sybil proof dht
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Our Idea

• We are given a social graph
• Each node knows about their friends in the social
  graph
• Same assumptions about SybilGuard or Whanau
• Fast mixing graphs
• Small cut around attack edges
• o(n/log n) attack edges at most

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Our Motivation for DHT

• Isnt it possible to keep the basic routing
  features of a DHT while making it sybil
  resilient?
• O(log n) table size
• Lookup should take O(log n)
• We should use social information to build the DHT

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Bootstrapping the DHT

• Here comes the fundamental question
• How to convert a given social graph into a DHT
• So that the socially connected nodes are near
• Socially far nodes are far in the DHT
• Sybil nodes require significant amount of social
  engineering to be strongly connected members of a
  social group

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A new type of DHT

• We want to build a DHT
• Where distance between two nodes in the DHT-Space
  is related to their social-distance
• i.e, two friends in the social graph are expected
  to be one-hop distant in the DHT-Space
• Most of the queries will be through friends
• Hence, the probability of reaching a Sybil node
  is less
• We use the idea of Plexus
• A novel DHT routing based on linear block codes
• Plexus A Scalable Peer-to-Peer Protocol Enabling
  Efficient Subset Search Reaz Ahmed and Raouf
  Boutaba
• ACM/ IEEE TON Feb 2009

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Plexus Index Clustering
Linear code, C ltn,k,dgt Cluster head ?
Codeword Generator matrix based routing
C set of cluster heads
lt7, 4, 3gt Hamming code
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Linear Binary Code

• C ltn, k, dgt linear binary code
• n number of bits in a codeword
• k dimension ? 2k codeword in code
• d minimum distance between any pair of codeword
• e.g., G24?24, 12, 8?
• Generator Matrix G,

• 2k codeword can be formed by applying XOR to any
  combination of these k codewords.

87
Plexus Routing Table

• In a complete network each peer is responsible
  for a codeword
• Peer with codeword X maintains links to k1 peers
  with IDs computed as
• Xi X ? gi 1?? i ? k
• Xk1 X ? g1 ? g2 ? ? gk
• Xk1 is used for
• Replication
• Reducing routing cost

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Plexus Routing

• Observation C is closed under ? operation

X21X2?g1 X23X2?g3 X2kX2?gk
X231X23?g1 X235X23?g5 X23kX23?gk
X1X?g1 X2X?g2 XkX?gk
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Strengths of Plexus Routing

• Hamming distance based clustering indexing
• Maximum routing hops (within a subnet)
• ½ K in normal condition
• ½ K 2 in presence of failure.
• Disjoint routing paths
• Source X destination Y
• X?Y is disjoint from X?YK1
• Alternate routing paths
• Suitable for Multicasting
• Improved fault resilience
• Improved load balancing

90
Social Network to Plexus

• Now, the problem reduces to assigning appropriate
  linear block codes to the nodes
• How to do that ?

91
Naïve Idea

• All nodes u know their friends F1(u). All nodes u
  send F1(u) to all of their friends.
• At this point, Every node u, in addition to
  F1(u), can calculate its "mutual friend list" for
  each of its friends. For any two friends u, v  
• Their mutual friend set is
• Every node u, can also calculate F2(u), its exact
  two-hop distant friend list.

92
Naïve Idea

• Each node u, sorts their friends according to an
  "influence metric. For each friend v of a node
  u, Influence(u,v)   Influence of v on u I (u,
  v)
• it is highly probable that a sybil node will have
  very low influence on an honest node via attack
  edge due to very small number of mutual friends.
• However not only sybils, but also a common friend
  of two groups will have low influence on both
  group (however, this case is not handled in any
  algorithms)

93
Naïve Idea

• Each node u, calculates I(u,v) and I(v,u) for all
  its friends. There are 2deg(u) such quantities.
• C(u) Those nodes for which u has more influence
  on v than v has on u
• P(u) Those nodes for which v has more influence
  on u than u has on v.
• and R(u) Those nodes for which u and v both has
  same influence on each other

94
Naïve Idea

• max C(u) x The friend, on which u is
  maximum influential.
• However, it doesnt mean x doesnt have a friend
  more influential than u. It means, u does not
  have a friend on which it has more influence than
  it has on x.
• max P(u) y The friend which has the
  highest influence on u.
• It also doesnt mean y doesnt have friends on
  which it has more influence than it has on u.
• max R(u) z The friends which has same
  influence on u as u has on them.

95
Naïve Idea

• lx   I(x,u)  Iy  I(u,y) ,  Iz I(u,z)
  I(z,u)MI Ix, Iy, IzIf Max MI   Ix u
  is an influencial nodeIy u is an
  influenced nodeIz u is an neutral node

96
Naïve Idea

• Action D If u is influenceD, it decides not
  to generate any ID, and decides to take command
  from y. It sends a message to y that it has come
  into his control.Action L If u is an
  influentiaL node, it decides to generate ID for u
  and some of F1(u) and F2(u)Action N If u is
  Neutral, then decides Action L or Action D by a
  uniform bernoulli trial.

97

• Now, u generates ID for itself, for those of
  Gang(u).
• It will try to keep friend IDs as close as
  possible, also those of Gang(u) which are friends
  themselves will get close ID as  possible.
• u will inform all of Gang(u) all the ids
  generated by it.
• Members of Gang(u) will take care of id
  generation of their neighbors

98

• But how to handle collision ?
• Some gossip protocols needed !!

99
Naïve Idea

• Thus Ids will be assigned in the code space
  according to their Social Groups