The Social Hourglass: Enabling Socially-aware Applications and Services

1. The Social Hourglass: Enabling Socially-aware Applications and Services Adriana Iamnitchi University of South Florida anda@cse.usf.edu

2. Much Social Information Available • Connects people through relationships • Object centric: use of same objects • Person centric: declared relationships or co-participation in events, groups, etc.

3. Mining Social Data • Spam filtering • Sybil identification • Personalized search • Target marketing • Medical emergency notifications • …

4. Current Approach: Vertically Integrated Socially-aware Applications Data Source Data Source Data Source Application Application

5. Challenges with Current Approach • Application-limited collection and use of social information • High bootstrap cost • Limited (potentially inaccurate) information. E.g., Information from online social networks • Hidden incentives to have many “friends” • All relationships equal • Symmetric relationships • Newer proposals to merge different sources of social (and sensor) information for one app • Specifically targeting context awareness

6. Motivating Application: CallCensor

7. Motivating Application: Sofa Surfer

8. Motivating Application: Data Placement

9. Proposal: An Infrastructure for Social Computing Sofa Surfer Roommate Finder CallCensor …

10. Objective An infrastructure that: • Can fuse information from various sources • Allow user to control own information • What is collected • Where it is stored • Who can access it • Provide social knowledge to a variety of applications: • Social inferences (may be non-trivial)

11. Outline • Motivation • The Social Hourglass architecture • Social Sensors (work in progress) • Personal Aggregator (some ideas) • Social Knowledge Service: Prometheus (Kourtellis et al, Middleware 2010) • Data Management • API for social inferences • Experimental evaluation (on PlanetLab) • Summary

12. The Social Hourglass Architecture Applications Social Inference API Social Data Management Personal Aggregators Social Sensors Social Signals

13. Social Sensors Consume existing social signals • Location • Collocation • Schedule (e.g., Google calendar) • Mobile phone activity (calls, sms) • Online social network interactions • Email • Personal relations (family) • Shared content • Shared interest (e.g., CiteULike) • …

14. Social Sensors • Report on behalf of ego: • Alter, the person ego is interacting with • An activity tag: e.g., “outdoors”, “dining” • Based on content, location, predefined labels, etc. • A weight: e.g., 0.15 • Run on ego’s mobile devices, desktop, or on web • Processes user interactions • To reduce noise • To distinguish between routine and meaningful interactions

15. Social Sensors: Challenges • Identifying activity tags: • Mine text for keywords (emails, sms, blogs, etc) • Reverse geo-coding to find where (co)located • Predefined labels or dictionary and ontologies • Quantifying interactions (assigning weights): • Frequency, duration, time in-between interactions • Familiar strangers versus active social interaction

16. Work in Progress: Social Sensor for Gaming Interactions • Variability in playing habits • Variability in playing skills • Time patterns

17. Aggregators • Act as the user’s personal assistant • Runs on trusted device (cell phone) • Responsible for • Managing passwords for various applications • Personalization • Identity management

18. The Social Hourglass Architecture Applications Social Inference API Social Data Management Personal Aggregators Social Sensors Social Signals

19. Social Graph

20. Prometheus • Peer-to-peer architecture • Users contribute resources (peers) • Fundamental change from typical peer-to-peer networks: not every user has its peer • Input: Social information collected from different social sensors (reported via aggregators) • Output: Social information made available to applications and services • Information made available subject to user policies

21. Distributed Social Graph

22. Prometheus Architecture

23. Architecture Details • Users have a unique user ID • Select trusted peer group based on offline social trust with peer owners • A user’s trusted peers communicate via Scribe • Only the user’s trusted peers can decrypt user’s social data and thus perform social inference functions

24. Social Data Protection • 2 sets of public/private keys • User’s • User’s trusted peer group • Social sensors submit data encrypted with the group’s public key and signed with the user’s private key • Access to user’s private key only on user’s devices • Data stored in the Pastry overlay • Only trusted peers can decrypt and authenticate data

25. Social Inference Functions The social graph management service exports an API that implement social inferences

26. API for Applications: Social Inference Functions • 5 basic social inference functions: • relation_test (ego, alter, ɑ, w) • top_relations (ego, ɑ, n) • neighborhood (ego, ɑ, w, radius) • proximity (ego, ɑ, w, radius, distance) • social_strength (ego, alter) • More complex functions can be built

27. Social Strength • Quantifies strength between ego and alter • Result normalized to consider overall activity • Search all paths of maximum 2 social hops • One approach to quantify social strength. Others are certainly possible.

28. Lessons from Experiments on PlanetLab • Social-based mapping of users onto peers leads to significant performance gains: • More than 15% of requests finish faster • An order of magnitude fewer messages • Reasonable latency • Code significantly improved since publication in Middleware 2010

29. Experimental Results: Neighborhood Requests 10 users per peer 50 users per peer Prometheus: User-Controlled P2P Social Data Management for Socially-Aware Applications, Nicolas Kourtellis, Joshua Finnis, Paul Anderson, Jeremy Blackburn, CristianBorcea, Adriana Iamnitchi. 11th International Middleware Conference, Bangalore, India, November 2010.

30. Real Social Traces: NJIT Social Graph 100 randomly selected students from NJIT given Bluetooth-enabled phones that report their collocation • Data recorded • Collocation with two thresholds (45 and 90 minutes) • Facebook friendships • Sparse graph (commuters)

31. CallCensor • CallCensor implemented on Android • Cell phone silenced, rings or vibrates depending on the social context and relationship with caller • Relationship with caller: • Social strength > threshold: allow call • Caller directly connected by work • Caller connected by work and ≤ 2 hops away • Real social data from 100 users stored on 3 nodes from PlanetLab • Real time performance constraints

32. Lessons from CallCensor Experiments

33. Resilience to (Social) Attacks • Vulnerability to malicious users mitigated by directed, multi-edged, weighted social graph • Vulnerability to malicious peers related to social graph distribution • Peers gain the properties of the social graph they represent

34. Summary • The social hourglass architecture • Prometheus: a decentralized service that enables socially-aware applications and services by collecting, managing and exposing social knowledge, subject to user-specified privacy policies. • Unique contributions: • Social graph representation • Aggregated social data • Social inference functions • Socially-aware design

35. Much Work to Be Done • Developing social sensors • Aggregator: • proof of concept implementation • Performance • Evaluating benefits of social knowledge in system design • Socially-aware applications • Query language for social inferences • Privacy protection

36. More Information • The Social Hourglass: an Infrastructure for Socially-aware Applications and Services, Iamnitchi et al., IEEE Internet Computing, May/June 2012 • Prometheus: User-Controlled P2P Social Data Management for Socially-Aware Applications, Kourtellis et al., Middleware 2010 • Vulnerability in Socially-Informed Peer-to-Peer System, Jeremy Blackburn, Nicolas Kourtellis, and Adriana Iamnitchi. Fourth Workshop on Social Network Systems (SNS 2011) http://www.cse.usf.edu/~anda anda@cse.usf.edu

37. Acknowledgements • My team of talented graduate students and alumni: • US National Science Foundation grants CNS-0831785 and CNS-0952420

38. Thank you!

39. Neighborhood Inference

40. Social Strength Inference

41. A Distributed System 42

42. Or a Distributed System 43

43. An Example: Interest Sharing “No 24 in B minor, BWV 869” “Les Bonbons” “Yellow Submarine” “Les Bonbons” “Yellow Submarine” “Wood Is a Pleasant Thing to Think About” “Wood Is a Pleasant Thing to Think About” The interest-sharing graph GmT(V, E): • V is set of users active during interval T • An edge in E connects users who share at least m file requests within T

44. Food web LANL coauthors Film actors Power grid Web Internet Word co-occurrences Small Worlds D. J. Watts and S. H. Strogatz, Collective dynamics of small-world networks. Nature, 393:440-442, 1998 R. Albert and A.-L. Barabási, Statistical mechanics of complex networks, R. Modern Physics 74, 47 (2002).

45. 300s, 1file 1800s, 10file 7200s, 50files 3600s, 50files 1800s, 100files Web Interest-Sharing Graphs

46. DØ Interest-Sharing Graphs 28 days, 1 file 7days, 1file

47. KaZaA Interest-Sharing Graphs 2 hours 1 file 28 days 1 file 1 day 2 files 4h 2 files 12h 4 files 7day, 1file

48. Proactive Information Dissemination D0 Web Kazaa