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

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 5

6. Motivating Application: CallCensor 6

7. Motivating Application: Sofa Surfer 7

8. Motivating Application: Data Placement 8

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) 10

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 11

12. 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) … 13

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 14

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 15

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

19. Social Graph 19

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 20

21. Distributed Social Graph 21

22. Prometheus: A P2P Social Data Management Service Collects social information from multiple sources (social sensors) Maintains this information in a social graph Offers a set of basic social inference functions 22

23. Prometheus Architecture 23

24. 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

25. 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

26. Social Inference Functions The social graph management service exports an API that implement social inferences 26

27. 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

28. 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

29. 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

30. Experimental Results: Neighborhood Requests 30 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, Cristian Borcea, Adriana Iamnitchi. 11th International Middleware Conference, Bangalore, India, November 2010.

31. 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

32. 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

33. Lessons from CallCensor Experiments 33

34. 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 Resilience to (Social) Attacks

35. 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

36. 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

37. 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

38. Acknowledgements My team of talented graduate students and alumni: US National Science Foundation grants CNS- 0831785 and CNS-0952420 38

39. Thank you! 39

40. Neighborhood Inference 40

41. Social Strength Inference 41

42. 42 A Distributed System 42

43. 43 Or a Distributed System 43

44. An Example: Interest Sharing 44 “ 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 G m T (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

45. Small Worlds 45 Word co-occurrences Film actors LANL coauthors Internet Web Food web Power grid 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).

46. Web Interest-Sharing Graphs 46 7200s, 50files 3600s, 50files 1800s, 100files 1800s, 10file 300s, 1file

47. DØ Interest-Sharing Graphs 47 7days, 1file 28 days, 1 file

48. KaZaA Interest-Sharing Graphs 48 7day, 1file 28 days 1 file 2 hours 1 file 1 day 2 files 4h 2 files 12h 4 files

49. Proactive Information Dissemination 49 D0 Web Kazaa