Center Project Reports

A Model for Calculating User-Identity Trustworthiness in Online Transactions, B. Soeder and K. Suzanne Barber, 13th Annual Conference on Privacy, Security and Trust (PST), pp. 177-185, 2015.

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Online transactions require a fundamental relationship between users and resource providers (e.g., retailers, banks, social media networks) built on trust; both users and providers must believe the person or organization they are interacting with is who they say they are. Yet with each passing year, major data breaches and other identity-related cybercrimes become a daily way of life, and existing methods of user identity authentication are lacking. Furthermore, much research on identity trustworthiness focuses on the user’s perspective, whereas resource providers receive less attention. Therefore, the current research investigated how providers can increase the likelihood their users’ identities are trustworthy. Leveraging concepts from existing research, the user-provider trust relationship is modeled with different transaction contexts and attributes of identity. The model was analyzed for two aspects of user-identity trustworthiness—reliability and authenticity—with a significant set of actual user identities obtained from the U.S. Department of Homeland Security. Overall, this research finds that resource providers can significantly increase confidence in user-identity trustworthiness by simply collecting a limited amount of user-identity attributes.

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CITATION
Soeder, B. and K.S. Barber, 13th Annual Conference on Privacy, Security and Trust (PST), pp. 177-185, 2015.

Tournament-Based Reputation Models for Aggregating Relative Preferences, Budalakoti, S., and K. Suzanne Barber, Proceedings of the 2013 International Conference on Social Computing (SOCIALCOM), pp. 983-986, 2013

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Online social networks often rely on reputation values to signal trustworthiness of a participant in the network. Reputation models have traditionally focused on scenarios where raters provide an absolute value rating to those they interact with. This paper investigates an alternate scenario, where a rater provides relative preference information among multiple alternatives, indicating which participant it prefers from a given set of participants. To model such scenarios, we propose an alternative to the traditional referral graph model, in the form of tournament models. We investigate two tournament-based trust models: the power ranking model and the fair bets model. We find that tournament models outperform standard referral network models, in predicting which participant will prove to be most preferred in future transactions. We also find that, in online communities where self-selection norms are not strongly enforced, so that a participant may participate in a transaction even when it knows it is unlikely to provide a satisfactory result, the fair bets model outperforms other models by a wide margin in identifying trustworthy participants.

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CITATION:
Budalakoti, S., and K.S. Barber, Proceedings of the 2013 International Conference on Social Computing (SOCIALCOM), pp. 983-986, 2013.

Modeling Virtual Footprints, R. Kadaba, S. Budalakoti, D. DeAngelis, and K. Suzanne Barber, International Journal of Agent Technologies and Systems, IGI Publishers, Volume 3, Issue 2, 2011.

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Entities interacting on the web establish their identity by creating virtual personas. These entities, or agents, can be human users or software-based. This research models identity using the Entity-Persona Model, a semantically annotated social network inferred from the persistent traces of interaction between personas on the web. A Persona Mapping Algorithm is proposed which compares the local views of personas in their social network referred to as their Virtual Signatures, for structural and semantic similarity. The semantics of the Entity-Persona Model are modeled by a vector space model of the text associated with the personas in the network, which allows comparison of their Virtual Signatures. This enables all the publicly accessible personas of an entity to be identified on the scale of the web. This research enables an agent to identify a single entity using multiple personas on different networks, provided that multiple personas exhibit characteristic behavior. The agent is able to increase the trustworthiness of on-line interactions by establishing the identity of entities operating under multiple personas. Consequently, reputation measures based on on-line interactions with multiple personas can be aggregated and resolved to the true singular identity.

CITATION:
Kadaba, R., S. Budalakoti, D. DeAngelis, and K. S. Barber, International Journal of Agent Technologies and Systems, IGI Publishers, Volume 3, Issue 2, pp. 1-17, 2011.

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Expertise Modeling and Recommendation in Online Question and Answer Forums, S. Budalakoti, D. Deangelis, K.Suzanne Barber, Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE - IEEE International Conference on Social Computing, SocialCom 2009, vol. 4, pp. 481-488. 2009

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Question and answer forums provide a method of connecting users and resources that can leverage both the static and dynamic (live) capabilities of a network of human users. We present a recommender for selecting the most appropriate responders given a question. The goal of this work is to encourage expert participation in QA forums by reducing the time investment needed by an expert to find a suitable question, decrease responder load, and to increase questioner confidence in the responses of others. The two primary contributions of this work are: 1. a generative model for characterizing the production of content in an online question and answer forum and 2. a decision theoretic framework for recommending expert participants while maintaining questioner satisfaction and distributing responder load. We have also developed two new metrics tailored to QA forums: responder load and questioner satisfaction. These metrics are used to evaluate the performance of our recommender system on datasets harvested from Yahoo! Answers. Experiments across several topic domains demonstrate our system’s ability to predict responder identities and suggest new responders that are likely to have the appropriate expertise.

CITATION:
Budalakoti, S., D. Deangelis, K.S. Barber, Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE - IEEE International Conference on Social Computing, SocialCom 2009, vol. 4, pp. 481-488. 2009

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