Vulnerability in
Socially-informed Peer-to-Peer
Systems
Jeremy Blackburn
Nicolas Kourtellis
Adriana Iamnitchi
University of South Florida
Social and Socially-aware
Applications
Internet Applications
Mobile Applications
Applications may contain user profiles, social networks,
history of social interactions, location, collocation 2
Problems with Current Social
Information Management
• Application specific:
– Need to input data for each new application
– Cannot benefit from information aggregation
across applications
• Typically, data are owned by applications:
users don't have control over their data
• Hidden incentives to have many "friends":
social information not accurate
3
Our Previous Work: Prometheus
A peer-to-peer social data management service that:
•
Receives data from social sensors that collect application-specific
social information
•
Represents social data as decentralized social graph stored on
trusted peers
•
Exposes API to share social information with applications according to
user access control policies
Prometheus: User-Controlled Peer-to-Peer Social Data Management for
Socially-Aware Applications, N. Kourtellis et al, Middleware 2010
4
Prometheus: A P2P Social
Data Management Service
5
Social and Peer Networks in
Prometheus
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Social and Peer Topology
7
Applicable to Other Systems
• Socially-informed search
• Contextually-aware information dissemination
• Socially-based augmentation of risk analysis
in a money-lending peer-to-peer system
(such as prosper.com)
Unifying characteristics:
• Socially-informed routing of messages
between nodes in the peer-to-peer network
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Questions
• What is the vulnerability of such a network?
• What design decisions should be
considered?
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Outline
• Background
• Model
• Vulnerability to:
– Malicious users
– Malicious peers
• Experimental Evaluation
– Setup
– Results
– Lessons
• Summary
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Malicious Users
• Directed graph limits vulnerability
• Even if reciprocal edge created, label and weight
requirement limit effects
• Lessons for writing social inference functions that use
the social graph representation
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Malicious Peers
• Several attack mechanisms that are difficult
to prevent:
– Modifying results sent back to other peers
– Dropping/changing/creating fake requests
• We focus on the results sent back by a peer
– Question: how much damage can a peer do in
terms of the fraction of requests it can manipulate?
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Experimental Setup
• Social networks:
– Synthetic social graph
– Real networks (results not presented in the paper)
• Worst case scenario:
– Networks have reciprocal edges
– No weight or edge label restriction
– Requests flood neighborhood of radius K
• Mapping users on peers:
– Social: map communities to peers
– Random
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Socially-informed P2P Topologies
P2P topology formed by the 25 highest social bandwidth
connections between peers
Social mapping
Random mapping
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Synthetic Social Network
• 1000 users, 100 peers
• Communities identified
with Girvan-Newman
algorithm
• Lessons:
– Social mapping more
resilient
– Replication level
irrelevant for
vulnerability
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Mappings Users to Peers in
Real Social Networks
• Used a recursive version of the Louvain
algorithm for fast community detection
– Much more scalable than GN
• For the random mapping:
– Keep community size same as social
– Reshuffle the community members
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Communities in Real Networks
Social
Network
Number
of Users
gnutella04
Number of Communities
with average size S (in users)
S=10
S=50
S=100
10,876
1,088
218
109
gnutella31
62,561
6,256
1,246
619
enron
33,696
3,370
674
337
epinions
75,877
7,564
1,485
727
slashdot
82,168
8,207
1,607
794
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Lesson 1: Network Size Matters
Malicious nodes influence a larger percentage of the
network in smaller networks
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Lesson 2: Social Network
Topology Matters
Size is not an accurate predictor of vulnerability:
• epinions networks are smaller than slashdot networks
• yet vulnerability in epinions is lower
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Lesson 3: Grouping Matters
Gnutella04-social
Gnutella04-random
Enron-social
Enron-random
Gnutella31-social
Gnutella31-random
Epinions-social
Epinions-random
Slashdot-social
Slashdot-random
Social user grouping
always less
vulnerable than
random grouping
fraction of requests influenced
1
0.1
0.01
0.001
0.0001
10-2
50-2 100-2 10-3 50-3 100-3
Users per Peer - Hops
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Lesson 4: Size of Group Matters
More users on peer
means more
influence on
requests
(random or social)
fraction of requests influenced
• 50 users/peer, 674 peers
in enron
• 100 users/peer, 619 peers
in gnutella31
• yet enron more vulnerable
Gnutella04-social
Gnutella04-random
Enron-social
Enron-random
Gnutella31-social
Gnutella31-random
Epinions-social
Epinions-random
Slashdot-social
Slashdot-random
1
0.1
0.01
0.001
0.0001
10-2
50-2 100-2 10-3 50-3 100-3
Users per Peer - Hops
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Lessons
• Mapping of users onto peers influences
system vulnerability
– Socially-aware mappings more resilient
• Replication does not significantly affect
vulnerability
• Malicious peers can be more effective in
small networks
• Size of network is not an accurate predictor of
vulnerability
• Hub peers are most damaging
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Summary
• A study on the vulnerability of a sociallyinformed peer-to-peer network to malicious
attacks
• Problem motivated by our previous work but
of more general applicability
• Socially-aware design is tricky:
– Social mapping increases resilience
– Yet peer hubs (an outcome of social mapping)
decrease resilience
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