1. A S URVEY OF TRUST MANAGEMENT AND ITS APPLICATIONS S UPERVISED BY : D R. Y AN W ANG Ravendra Singh Student-id: 41446461 1

2. WHAT IS TRUST?  Trust can mean having belief or confidence in the honesty, goodness, skill or safety of a person, organisation or a thing.  In simple terms, Trust can mean to have confidence or faith in a person or a piece of information.  Trust is inferred differently in different contexts, for example on social networks, peer-to-peer networks or for e-commerce transactions. 2

3. P ROBLEM S PECIFICATION  Too much information is available over the internet in terms of selection of goods and services.  Very difficult to ascertain the trustworthiness or the reliability of the available information.  How is trust inferred and applied in terms of ratings or feedback on online social networks?  How do reputation-based and trust based filtering methodologies work in peer-to-peer networks?  Comparison of the methodologies to highlight their merits and drawbacks  Suggest future studies 3

4. A IMS AND SIGNIFICANCE  Advent of online social and peer-to-peer networks has led to the emergence of a trust based approach to recommendations.  Different recommender systems using trust inference algorithms have been formulated.  The methodologies used have to be studied and compared for their effective deployment in the real world applications. 4

5. A IMS AND SIGNIFICANCE (C ONTD.)  In peer-to-peer networks, there is a widespread prevalence of malicious peers.  The malicious peers provide fake resources with the same name as a real resource peer which the user may be looking for.  Similarity and trust based filtering algorithm mechanisms have been formulated to check the menace and provide authenticity to the trustworthy resources. 5

6. S COPE OF WORK AND SOURCES USED Study and compare different trust inferring methodologies and it’s applications in context of Social networks:  Inferring trust using TidalTrust as a trust inference algorithm (Golbeck and Hendler 2006)  Trust-based recommendation system on a social network using collaborative filtering method (Walter, Battiston et al. 2008)  Random Walk model for combining trust-based and item based recommendation (Mohsen and Martin 2009) 6

7. S COPE OF WORK AND SOURCES USED ( CONTD.) Study and compare different trust inferring methodologies and it’s applications in context of P2P networks:  A Similarity-based recommendation filtering algorithm for establishing reputation based trust (Li, Jing et al. 2005)  Trust based search for unstructured peer-to-peer networks (Mashayekhi, Habibi et al. 2008) 7

8. A PPROACH TAKEN TO SOLVE THE PROBLEM  Study and compared the algorithms and the methodologies used in different models.  Highlighted the merits and shortcomings found in the used methodologies.  Proposed issues for future research in certain areas. 8

9. O UTCOMES OF THE PROJECT The first methodology studied in context of social networks points out that TidalTrust has been used as the trust inference algorithm.  It uses collaborative filtering only for calculating the similarity between users in the network and recommendation items that are liked by users with similar tastes.  It works on the premise that recommendations to suggest user’s interest in an item shall be generated.  It measures how much the item relates to the user’s preference.  It uses the concept of making predictions on a compact and strong trust neighbourhood.  Trust ratings within the social network have been taken as the basis for similarity related calculations. 9

10. O UTCOMES OF THE PROJECT (C ONTD.) 10 The second methodology studied in context of social networks pertains to a trust-based recommendation system using collaborative filtering  It works on the premise that agents use their social network to reach information and trust relationships to filter the information.  It looks into how the dynamics of trust among different agents affect the system’s performance when comparing the methodology with a frequency based recommendation system.  It functions in an automated and distributed manner and has the ability to filter information for people based on their social network and trust relationships.  The model is found as robust and reliable against random, selfish and malicious agents on the social network.

11. O UTCOMES OF THE PROJECT (C ONTD.) 11 The third methodology based on Random walk model studied in context of social networks combines trust-based and item-based recommendations.  It considers the ratings of similar items along with the ratings of the target items.  Taking the similar items’ rating is done to avoid considering the ratings of far neighbours in the network.  The methodology works on the premise that the reliability of far neighbours in the chain of trust-based relationships becomes weak and cannot be relied upon.  The model computes confidence in its prediction of recommendations which is not done by other models.

12. O UTCOMES OF THE PROJECT (C ONTD.) 12 The first methodology is based on similarity based recommendation filtering algorithm.  It takes a community based reputations approach for estimating the trustworthiness of peers.  A simplified algorithm method is used to compute the similarity between peers.  The algorithm proves to be robust to thwart attempts from group of peers who co-operate deliberately amongst themselves to subvert the system.  The algorithm tags the community of malicious peers and biases the downloads preventing inauthentic downloads.

13. O UTCOMES OF THE PROJECT (C ONTD.) 13 The second t methodology on P2P networks is based on trust based search model  It combines the search and trust systems to reduce the costs of executing them separately.  In the evaluation scheme, it does not calculate and store the global reputation values.  It obtains an estimate of global reputation values.

14. 14 CONCLUSION

15. 15 Q UESTIONS ?