Vulnerability in Socially-informed Peer-to-Peer Systems

1. Vulnerability in Socially-informed Peer-to-Peer Systems Jeremy Blackburn Nicolas Kourtellis Adriana Iamnitchi University of South Florida

2. Social and Socially-aware Applications Internet Applications Mobile Applications Applications may contain user profiles, social networks, history of social interactions, location, collocation

3. 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

4. Our Previous Work: Prometheus A peer-to-peer social data management servicethat: 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

5. Prometheus: A P2P Social Data Management Service

6. Social and Peer Networks in Prometheus

7. Social and Peer Topology

8. 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

9. Questions • What is the vulnerability of such a network? • What design decisions should be considered?

10. Outline • Background • Model • Vulnerability to: • Malicious users • Malicious peers • Experimental Evaluation • Setup • Results • Lessons • Summary

11. 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

12. 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?

13. 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

14. Socially-informed P2P Topologies P2P topology formed by the 25 highest social bandwidth connections between peers Social mapping Random mapping

15. Synthetic Social Network • 1000 users, 100 peers • Communities identified with Girvan-Newman algorithm • Lessons: • Social mapping more resilient • Replication level irrelevant for vulnerability

16. 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

17. Communities in Real Networks

18. Lesson 1: Network Size Matters Malicious nodes influence a larger percentage of the network in smaller networks

19. 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

20. Lesson 3: Grouping Matters Social user grouping always less vulnerable than random grouping

21. Lesson 4: Size of Group Matters • 50 users/peer, 674 peers in enron • 100 users/peer, 619 peers in gnutella31 • yet enron more vulnerable More users on peer means more influence on requests (random or social)

22. 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

23. Summary • A study on the vulnerability of a socially-informed 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 increasesresilience • Yet peer hubs (an outcome of social mapping) decrease resilience