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Reputation Network Analysis for Email Filtering

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E-mail Scoring mechanism based on a social network augmented with reputation ratings ... Social Networks. Proposed by Boykin and Roychowdhury. Social network ... – PowerPoint PPT presentation

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Title: Reputation Network Analysis for Email Filtering


1
Reputation Network Analysis for Email Filtering
  • Ravi Emani
  • Ramesh Ravindran

2
Describes about
  • E-mail Scoring mechanism based on a social
    network augmented with reputation ratings
  • Algorithm for inferring reputation ratings
  • Integration into a mail application TrustMail

3
Preventing Spam
  • Trying to prevent spam from even reaching the
    users mailbox
  • Methods
  • - Whitelist filters
  • - Social Networks
  • - Connecting Users

4
Whitelist Filters
  • Messages accepted according to a list of approved
    addresses created by the user
  • Advantages
  • - No spam in users inbox
  • - Filters the spam into a low-priority
    folder
  • Disadvantages
  • -Extra burden on the user
  • -Filters even the valid emails

5
Social Networks
  • Proposed by Boykin and Roychowdhury
  • Social network created from the messages received
    by the user
  • Messages identified as spam, valid or unknown
    based on clustering thresholds and structural
    properties like the propensity for local
    clustering.
  • Classifies about 50 of users email into spam or
    other valid categories

6
Optimization
  • Extension of whitelisting and social network
    based filtering
  • Uses a network that connects users
  • A score of reputation or trust is assigned by
    the users to the people they know
  • Results in a large reputation network connecting
    thousands of users
  • Messages sorted by the score shown next to the
    messages in the inbox

7
Optimization
  • Overcomes the problem of the whitelists
  • More reliable than the whitelists even though the
    user takes the burden for creating an initial set
    of reputation ratings
  • Less work comparatively

8
Creating the Reputation Network
  • Uses a Distributed, web based social network
  • Reputation rating inferred from one user to
    another
  • Individuals are connected to each person they
    rated
  • Results in a large interconnected network of users

9
How is it related to Semantic Web?
  • The only requirement is that the individuals
    should assert their reputation ratings for one
    another in the network
  • Individuals will be controlling their own data
  • Data is maintained in a distributed fashion
  • Data can be stored anywhere and integrated
    through a common foundation

10
Role of Semantic Web...
  • Semantic web, along with its component languages
    RDF, RDFS, OWL utilize web architecture
  • Supports distributed data management
  • Users create ontologies with classes and
    properties and hence instances
  • The instances of the classes help in describing
    the data on the web

11
FOAF Project
  • Friend-Of-A-Friend project developed on Semantic
    Web
  • An ontological vocabulary for describing people
    and their relationships
  • Extended by providing a mechanism describing the
    reputation relationships
  • Allows people to rate the reputation or
    trustworthiness of another person

12
Fig The reputation network developed as part of
the semantic web trust project at
http//trust.mindswap.org.
13
Algorithms for Inferring Reputation between
Individuals
  • Recommendations are made to one person(source)
    about the reputation of another person(sink)
  • Trust and reputation literature contains many
    different metrics
  • These metrics are categorized according to the
    perspective used for making calculations

14
Perspective in Reputation Inference Algorithms
  • Global metrics calculate a single value for each
    entity in the network
  • Local metrics calculate a reputation rating for
    an individual in the network
  • In global system an entity will always have the
    same inferred rating
  • In local system an entity could be rated
    differently depending on the node the inference
    is made for

15
Perspective in Reputation Inference Algorithms
  • Global metrics can be highly effective in
    situations where the experiences of users are
    similar
  • Local metrics can be appropriate where users
    opinions vary about the same topic

E
9
1
D
C
10
10
A
B
16
Accurate Metrics for Inferring Reputation
  • The inferred rating from the source to the sink
    is given by a weighted average of the neighbors
    reputation ratings of the sink.
  • Reputation rating t from source i to sink s
    is written as tis
  • No inference needed if source is directly
    connected to the sink
  • If not, the reputation rating is calculated by
    weighted average of the reputation ratings
    returned for the sink by each of its n neighbors.

17
  • getRating(source, sink)
  • mark source as seen
  • if source has no rating for sink
  • denom 0
  • num 0
  • for each j in neighbors(source)
  • if j has not been seen
  • denom
  • j2sink in(rating(source,j),ge
    tRating(j,sink))
  • num rating(source,j) j2sink
  • mark j unseen
  • rating(source,sink) num/denom
  • return rating(source,sink)

18
Accurate metrics for Inferring Reputation
The concise representation of how tis is weighted
is shown as follows
The condition in this formula ensures that the
source will never trust the sink more than any
intermediate node
19
Reputation Metric Evaluation
  • To determine the accuracy of this metric
  • Reputation rating tij is recorded for each
    neighbor j by iterating through each individual
    i in the network
  • Later the connection from i to j is removed and
    the reputation rating tij is recorded
  • The accuracy is measured as tij-tij

20
TrustMail A Prototype
  • Message Scoring System
  • Adds reputation ratings to the folder views of a
    message
  • Helps sort messages accordingly by the user after
    he sees the reputation ratings
  • Highlights the important and relevant messages

21
Conclusion and Future Work
  • Our algorithm infers reputation relationships in
    a network
  • Benefit - Valid emails from unknown people can
    receive high scores because of the connections
    within the social network
  • Future work involves the refinement of the
    algorithm for inferring reputation ratings

22
Conclusion and Future work
  • May involve developing and studying the TrustMail
    interface
  • The number of ratings received will change with
    the size of a network
  • Important issues to be considered
  • -Techniques combining best with reputation
    filtering
  • - Percentage of messages accurately scored

23
References
  • Boykin, P. O. Roychowdhury, V. Personal email
    networks an effective anti-spam tool
    http//www.arxiv.org/abs/cond-mat/0402143,
    (2004).
  • http//sites.wiwiss.fu-berlin.de/suhl/bizer/SWTSGu
    ide/
  • RDFWeb FOAF The Friend of a Friend
    Vocabulary, http//xmlns.com/foaf/0.1/
  • Golbeck, Jennifer, Bijan Parsia, James Hendler,
    Trust Networks on the Semantic Web,
  • Richardson, Matthew, Rakesh Agrawal, Pedro
    Domingos. Trust Management for the Semantic
    Web, Proceedings of the Second International
    Semantic Web Conference, Sanibel Island, Florida,
    2003.
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