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OSN Research As If Sociology Mattered

OSN Research As If Sociology Mattered. Krishna P. Gummadi Networked Systems Research Group MPI-SWS. OSN research today. Computational sociology : A natural sciences approach Gather and analyze OSN data to study problems in sociology

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OSN Research As If Sociology Mattered

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  1. OSN Research As If Sociology Mattered Krishna P. Gummadi Networked Systems Research Group MPI-SWS

  2. OSN research today • Computational sociology: A natural sciences approach • Gather and analyze OSN data to study problems in sociology • Sociologists today use pretty sophisticated computing tools • Social computing: An engineering approach • Build systems that support / leverage human social interactions • But, we tend to treat human behavior as annoying noise • rather than leverage insights from sociology

  3. This talk • Argues that insights from sociology can help design better systems • Example 1: Dunbar’s number • The case for decentralized content sharing in OSNs • Example 2: Group attachment theory • How social network-based Sybil defenses do or don’t work

  4. Example 1: Dunbar’s number • Limits the # of stable social relationships a user can have • To less than a couple of hundred • Linked to size of neo-cortex region of the brain • Observed throughout history since hunter-gatherer societies • Also observed repeatedly in studies of OSN user activity • Users might have a large number of contacts • But, regularly interact with less than a couple of hundred of them

  5. User generated content sharing over OSNs • A very popular activity over Facebook • UGC like pictures, videos, and wall posts • Facebook is building massive datacenters to support UGC • Uses Akamai to deliver it • But, most of Facebook’s UGC is of personal nature • Pictures and videos of family and social events • Content popularity would be limited by Dunbar’s number! • Do we really need datacenters & CDNs to share this UGC?

  6. Why not share personal UGC from homes? • Advantage: Regain control over personal data sharing • Better control over what you share & whom you share • Concerns: • Can we get good performance? • Yes, due to Dunbar’s limit on popularity • Can we get good availability? • Yes, using always-on and always-connected gateways • They are inexpensive: cheap and low-power

  7. Example 2: Group attachment theory • Explains how humans join and relate to groups • Common-bond based groups • Membership based on inter-personal ties, e.g., family or kinship • Necessarily small, but tightly-knit and cohesive • Common-identity based groups • Membership based on self- or shared- interest • Could be larger, but become less cohesive with scale

  8. OSN graphs and groups • Most OSN graphs include all manners of links • Can extract bond groups from graph structure • By looking for highly clustered communities of nodes • But, not identity groups • Loosely-knit, they merge into the rest of the network • Result: A size limit on detectable graph communities

  9. Sybil attack • A fundamental problem in distributed systems • Attacker creates many fake/sybil identities • Many cases of real world attacks : Digg, Youtube Automated sybil attack on Youtube for $147!

  10. Defending against Sybil attacks • Traditional solutions rely on central trusted authorities • Runs counter to open membership policies of OSNs • Recent proposals leverage social networks • Key Insight: Social links are hard to acquire in abundance • Look for small cuts in the graph • Conversely, look for communities around known trusted nodes Links difficult to create

  11. Lots of research activity recently • Each optimized under assumptions about the graph structure • E.g., graphs are fast-mixing • Each evaluated on different datasets • Comparative evaluations yield inconsistent results All schemes analyse the graph structure to isolate Sybils SybilGuard [SIGCOMM’06] SybilLimit [Oakland’08] Ostra [NSDI’08] SumUp [NSDI’09] SybilInfer [NDSS’09] Whanau [NSDI’10] MobID [INFOCOM’10]

  12. Sybil resilience & group attachment theory • Sybil schemes find bond groups around a trusted node • But, these are only a fraction of all honest nodes • Bond groups are hard for Sybils to infiltrate • Not the case with identity groups

  13. Implications • Graph structure can identify nodes that are non-Sybils • But, it cannot identify nodes that are Sybils • Most nodes cannot be classified into either categories • Does this imply Sybil schemes are useless? • No, they can be used conservatively to find content from people you trust

  14. Summary • OSN system designers should look to leverage insights from sociology • Presented two examples where some very basic knowledge of sociology proved useful • Lots more ways to leverage sociology in the future • Can we leverage strength of ties to set privacy policies or prioritizing updates from friends?

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