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

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

3. 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. 4 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

5. 5 Prometheus: A P2P Social Data Management Service

6. 6 Social and Peer Networks in Prometheus

7. 7 Social and Peer Topology

8. 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. 9 Questions What is the vulnerability of such a network? What design decisions should be considered?

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

11. 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. 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. 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. 14 Socially-informed P2P Topologies P2P topology formed by the 25 highest social bandwidth connections between peers Social mappingRandom mapping

15. 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. 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. 17 Communities in Real Networks Social Network Number of Users Number of Communities with average size S (in users) S=10S=50S=100 gnutella0410,8761,088218109 gnutella3162,5616,2561,246619 enron33,6963,370674337 epinions75,8777,5641,485727 slashdot82,1688,2071,607794

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

19. 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. 20 Lesson 3: Grouping Matters Social user grouping always less vulnerable than random grouping

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

22. 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. 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 increases resilience –Yet peer hubs (an outcome of social mapping) decrease resilience