OSN Research As If Sociology Mattered Krishna P. Gummadi Networked Systems Research Group MPI-SWS.
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Transcript of OSN Research As If Sociology Mattered Krishna P. Gummadi Networked Systems Research Group MPI-SWS.
![Page 1: OSN Research As If Sociology Mattered Krishna P. Gummadi Networked Systems Research Group MPI-SWS.](https://reader030.fdocuments.us/reader030/viewer/2022033108/56649e535503460f94b49725/html5/thumbnails/1.jpg)
OSN Research As If Sociology Mattered
Krishna P. Gummadi
Networked Systems Research GroupMPI-SWS
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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
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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
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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
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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?
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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
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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
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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
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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!Automated sybil attack on Youtube for $147!
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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 createLinks difficult to create
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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
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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
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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
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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?