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 user’s mailbox Methods: - Whitelist filters - Whitelist filters - Social Networks - Social Networks - Connecting Users - Connecting Users

4. Whitelist Filters Messages accepted according to a list of approved addresses created by the user Advantages - No spam in user’s inbox - No spam in user’s inbox - Filters the spam into a low-priority folder - Filters the spam into a low-priority folderDisadvantages -Extra burden on the user -Extra burden on the user -Filters even the valid emails -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 user’s 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 user’s opinions vary about the same topic A DC B E 10 1 9

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 ‘t is ’ 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),getRating(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 t is 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 t ij 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 t ij ` is recorded The accuracy is measured as |t ij -t ij `|

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 - 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/SWTSGuide/ RDFWeb: FOAF: ‘The Friend of a Friend Vocabulary’, http://xmlns.com/foaf/0.1/ 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.