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The Social Hourglass: Enabling Socially-aware Applications
and Services
Adriana IamnitchiUniversity of South Florida
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.
Mining Social Data• Spam filtering• Sybil identification• Personalized search• Target marketing• Medical emergency notifications• …
Current Approach: Vertically Integrated Socially-aware
Applications
Data Source
ApplicationApplication
Data Source
Data Source
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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
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Motivating Application: CallCensor
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Motivating Application: Sofa Surfer
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Motivating Application: Data Placement
Proposal: An Infrastructure for Social Computing
Sofa SurferRoommate Finder
CallCensor
…
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ObjectiveAn 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)
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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
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The Social Hourglass Architecture
Applications
Social Inference API
Social Data ManagementPersonal Aggregators
Social Sensors
Social Signals
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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)• …
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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
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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
Work in Progress: Social Sensor for Gaming Interactions
• Variability in playing habits• Variability in playing skills• Time patterns
Aggregators• Act as the user’s personal assistant• Runs on trusted device (cell phone)• Responsible for
– Managing passwords for various applications
– Personalization– Identity management
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The Social Hourglass Architecture
Applications
Social Inference API
Social Data ManagementPersonal Aggregators
Social Sensors
Social Signals
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Social Graph
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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
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Distributed Social Graph
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Prometheus Architecture
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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
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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
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Social Inference FunctionsThe social graph management service exports an API
that implement social inferences
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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
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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.
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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
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Experimental Results: Neighborhood Requests
10 users per peer 50 users per peerPrometheus: 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.
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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)
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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
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Lessons from CallCensor Experiments
• 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
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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
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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
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/[email protected]
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Acknowledgements• My team of talented graduate
students and alumni:
• US National Science Foundation grants CNS-0831785 and CNS-0952420
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Thank you!
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Neighborhood Inference
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Social Strength Inference
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A Distributed System
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Or a Distributed System
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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
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Small Worlds
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, 1998R. Albert and A.-L. Barabási, Statistical mechanics of complex networks, R. Modern Physics 74, 47 (2002).
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Web Interest-Sharing Graphs
7200s, 50files
3600s, 50files
1800s, 100files
1800s, 10file
300s, 1file
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DØ Interest-Sharing Graphs
7days, 1file
28 days,1 file
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KaZaA Interest-Sharing Graphs
7day, 1file
28 days1 file
2 hours1 file
1 day2 files
4h2 files
12h4 files
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Proactive Information Dissemination
D0
WebKazaa