A Survey of TRUST MANAGEMENT AND ITS APPLICATIONS Supervised by: Dr. Yan Wang

1. A Survey of TRUST MANAGEMENT AND ITS APPLICATIONSSupervised by: Dr. Yan Wang Ravendra Singh Student-id: 41446461

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.

3. Problem Specification • 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

4. Aims 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.

5. Aims and significance (Contd.) • 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.

6. Scope 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)

7. Scope 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)

8. Approach 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.

9. Outcomes 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.

10. Outcomes of the project (Contd.) 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. Outcomes of the project (Contd.) • 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. Outcomes of the project (Contd.) • 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. Outcomes of the project (Contd.) • 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. CONCLUSION

15. Questions?