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 –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

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

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

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?

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

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

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

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!

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

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 SybilGuard [SIGCOMM’06] SybilLimit [Oakland’08] Ostra [NSDI’08] SumUp [NSDI’09] SybilInfer [NDSS’09] Whanau [NSDI’10] MobID [INFOCOM’10] All schemes analyse the graph structure to isolate Sybils

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

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

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?