Recent Internet applications, such as online social networks and user-generated content sharing, produce an unprecedented amount of social information, which is further augmented by location or collocation data collected from mobile phones. Unfortunately, this wealth of social information is fragmented across many different proprietary applications. Combined, it could provide a more accurate representation of the social world, and it could enable a whole new set of socially-aware applications. We introduce Prometheus, a peer-to-peer service that collects and man- ages social information from multiple sources and implements a set of social inference functions while enforcing user-defined access control poli- cies. Prometheus is socially-aware: it allows users to select peers that manage their social information based on social trust and exploits naturally- formed social groups for improved performance. We tested our Prometheus prototype on PlanetLab and built a mobile social application to test the performance of its social inference functions under real-time constraints. We showed that the social-based mapping of users onto peers improves the service response time and high service availability is achieved with low overhead. Prometheus: User-Controlled P2P Social Data Management for Socially-aware Applications. Nicolas Kourtellis, Joshua Finnis, Paul Anderson, Jeremy Blackburn, Cristian Borcea, Adriana Iamnitchi. ACM/IFIP/USENIX 11th International Middleware Conference, 2010

1. Prometheus: User-Controlled P2P
Social Data Management
for Socially-aware Applications
Nicolas Kourtellis, Joshua Finnis,
Paul Anderson, Jeremy Blackburn,
Cristian Borcea*, Adriana Iamnitchi
Department of Computer Science and Engineering, USF
*Department of Computer Science, NJIT
ACM/IFIP/USENIX 11th International Middleware Conference, 2010

2. 2
Social and Socially-aware Applications
Applications may contain user profiles, social networks,
history of social interactions, location, collocation

3. 3
Problems with Current Social
Information Management
 Application specific:
 Need to input data for each new application
 Cannot benefit from information
aggregation across applications
 Typically, data are owned by applications:
users don't have control over their data
 Hidden incentives to have many "friends":
social information not accurate

4. 4
Our Solution: Prometheus
 P2P social data management service:
 Receives data from social sensors that collect
application-specific social information
 Represents social data as decentralized social graph
 Exposes API to share social information with
applications according to user access control policies
SOCIAL
SENSORS
SOCIALLY-
AWARE APPS
CallCensor
Foursquare`
`` `
`
`
`
PROMETHEUS
Loopt

5. 5
Outline
 Motivation
 Social Graph Management
 API and Access Control
 Prototype Implementation
 Evaluation over PlanetLab
 Summary
 Future Work

6. 6
How is the Social Graph Populated?
 Social sensors report edge information to
Prometheus:
<ego, alter, activity, weight>
 Applications installed by user on personal devices
 Aggregate & analyze history of user's interactions with
other users
 Two types of social ties:
 Object-centric: use of similar resources
 Examples: tagging communities on Delicious,
repeatedly being parts of the same BitTorrent swarms
 People-centric: pair-wise or group relationships
 Examples: friends on Facebook, same company name
on LinkedIn, collocation from mobile phones

7. 7
Social Graph Representation
 Multi-edged, directed, weighted, labeled graph
 Each edge → a reported social activity
 Weight → interaction intensity
 Directionality reflects reality
 Allows for fine-grain privacy
 Prevents social data manipulation

8. 8
Decentralized Graph Storage
 Each user has a set of trusted peers in the P2P network
 Peers it owns & peers owned by trusted users
 Each user’s sub-graph stored on all its trusted peers
 Improved availability in face of P2P churn
 P2P multicast used to synchronize information among
trusted peers
User
ID
Owns
Peer
Trust
Peer
A --- 1,2
B 1 1,2
C 2 1,2,3
D 3 2,3,4,5
E 4 3,4
F 5 3,5
ALL PEERS
A
C F
EB
D
<music,0.15>
<m
usic,0.25>
<football,0.3>
<m
usic,0.1>
<football,0.2>
<m
usic,0.1>
<hiking,0.25>
<football,0.3>
<m
usic,0.3>
<football,0.3>
<music,0.2>
<football,0.3>
<m
usic,0.25>
<hiking,0.3>
<football,0.3>
<m
usic,0.1>
<hiking,0.2>
PEER 1
A
C
B
D
<music,0.15>
<m
usic,0.25>
<football, 0.3>
<m
usic, 0.1>
<football, 0.2>
<m
usic, 0.1>
<hiking,0.25>
<football,0.3>
<music,0.3>
<football,0.2>
<football,0.3>
<music,0.2>
<football,0.2>
<football,0.1>
E

9. 9
Encrypted P2P Storage
 Sensor data stored encrypted in P2P network
 Improves availability and protects privacy
 Sensors encrypt data with trusted group public key &
sign with user private key
 Trusted peers retrieve user data, decrypt it, & create
social graph
Group
Public Key
Private Key
User
Public Key
Private Key

10. 10
Outline
 Motivation
 Social Graph Management
 API and Access Control
 Prototype Implementation
 Evaluation over PlanetLab
 Summary
 Future Work

11. 11
Prometheus Application Interface
 Five social inference functions:
 Boolean relation_test (ego, alter, ɑ, w)
 User-List top_relations (ego, ɑ, k)
 User-List neighborhood (ego, ɑ, w, radius)
 User-List proximity (ego, ɑ, w, radius, distance)
 Double social_strength (ego, alter)
 Ego & alter don’t have to be directly connected
 Normalized result: consider ego’s overall activity
 Search all 2-hop paths

12. 12
Application Example: CallCensor
 Socially-aware incoming call filtering
 Ring/vibrate/silence phone based on current social
context and relationship with caller
 Invokes
 proximity() to determine current social context
 social_strength() to determine relationship with caller

13. 13
Request Execution: social_strength()
1st hop
2nd hop1st hop
1. Application sends request to a peer
2. Peer forwards request to trusted peer
3. Trusted peer enforces ACPs
4. Trusted peer sends secondary requests
5. Trusted peers enforce ACPs & reply
6. Primary peer combines results
7. Primary peer replies to application
through contacted peer with final result

14. 14
Access Control Policies
 User specifies ACPs upon registration
 ACPs stored on user’s trusted peer group
 Update them at any time
 Changes propagated through multicast mechanism
 Applied for each inference request
 Control relations, labels, weights & locations
Example: Alice’s ACPs
relations: hops-2
hiking-label: lbl-hiking
work-label: lbl-work
general-label: ---
weights: ---
location: hops-1
blacklist: user-Eve

15. 15
Outline
 Motivation
 Social Graph Management
 API and Access Control
 Prototype Implementation
 Evaluation over PlanetLab
 Summary
 Future Work

16. 16
Prototype Implementation
 FreePastry Java implementation with support for
 DHT (Pastry)
 P2P storage (Past)
 Multicast (Scribe)
 Social graph management implemented in Python

17. 17
Evaluation over PlanetLab
 Goals:
1. Assess performance under realistic network
conditions (peers distributed around the world)
2. Assess performance at large scale using realistic
workloads with large number of users
3. Assess the effect of socially-aware mapping of
users onto trusted peers on system’s performance
4. Validate Prometheus with socially-aware
application under real-time constraints (CallCensor)
 Metric: end-to-end response time

18. 18
Large-Scale Evaluation Setup
 100 PCs around the globe
 RTT~200-300ms
 1000 users: synthetic social graph
 Random vs. socially-aware trusted peer assignment
 10 & 30 users assigned per peer
 Workloads for:
 Social sensor inputs based on Facebook study
 Neighborhood requests based on Twitter study
 Social strength requests based on BitTorrent study
 Applied a timeout of 15 seconds to fulfill a 1-hop
request in PlanetLab

19. 19
Neighborhood Request Results
 Socially-aware assignment of users onto peers results in faster
response time
 Message overhead reduced by an order of magnitude
 Replication for improved availability does not induce high overhead
0
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0.7
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0 10,000 20,000 30,000
CDF
End-to-end response time (msecs)
Neighborhood Requests (10 users/peer)
random - 1hop
random - 2hop
random - 3hop
social - 1hop
social - 2hop
social - 3hop
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0.5
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0 10000 20000 30000
CDF
End-to-end response time (msecs)
Neighborhood Requests (30 users/peer)
random - 1hop
random - 2hop
random - 3hop
social - 1hop
social - 2hop
social - 3hop

20. 20
Social Strength Request Results
 Similar performance with 2-hop Neighborhood Requests
 Search all 2-hop paths from source to destination
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0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 10000 20000
CDF
Average end-to-end response time (msecs)
Social Strength Requests
random - 10 users per peer
random - 30 users per peer
social - 10 users per peer
social - 30 users per peer

21. 21
CallCensor Evaluation Setup
 CallCensor implemented and
tested on Nexus Android phone
 100 users: real social graph
 Volunteer students from NJIT
 Two social sensors
 Collocation from Bluetooth
 45 & 90 minutes threshold
 Friendship from Facebook
 3 USA PlanetLab peers
 Socially-aware trusted peer
assignment

22. 22
CallCensor Results
 Met real-time performance constraint: response arrives before
call forwarded automatically to voicemail

23. 23
Summary
 Users of Prometheus:
 Decide what personal social data are collected by
installing/configuring social sensors
 Cooperate to store and manage their social data in
a decentralized fashion
 Own and control access to their data
 Prometheus enables:
 Socially-aware applications that utilize social data
collected from multiple sources
 Accurate social world representation through multi-
edged, labeled, directed and weighted graph
 Improved performance through socially-aware P2P
system design

24. 24
Future Work
 Improve Prometheus performance
 Network optimizations
 Caching of inference request results
 Develop new social sensors
 Develop new socially-aware applications &
services
 Study tolerance to malicious attacks
 Exposure of social information to
intermediate peers during request execution
 Manipulation of social connections to alter
the structure of the social graph

25. 25
Thank you!
This work was supported by NSF Grants:
CNS 0952420, CNS 0831785, CNS 0831753
http://www.cse.usf.edu/dsg/mobius
nkourtel@mail.usf.edu