Publications

Individuals declare their identities to online network providers with credentials such as usernames, passwords, and email addresses. To obtain these credentials from providers, users enroll by providing identity attributes, or collections of personal identifiable information (PII), such as phone numbers. Credentials vary in trustworthiness, and thus, so do identities. In search of better methods for increasing trustworthiness, we present a computational model of identity attributes described as an Identity Ecosystem to determine which are most vulnerable to malicious users. Using existing data from the U.S. Army and Department of Defense, wecmodel relationships between attributes as transition probabilities and analyze the long-run probability of all connected attributes being affected by one compromised attribute. This approach allows the provider to determine how best to weight relationships between attributes and thereby become more secure. Copyright is held by the owner/author(s). Publication rights licensed to ACM.

Link to publication

CITATION:
Soeder, B., & Barber, K. S. . Trustworthiness of identity attributes. In Proceedings of the 7th International Conference on Security of Information and Networks, (SIN 2014), vol. 2014-September, pp. 4-8, 2014.

Online communities such as question and answer (QA) systems are growing rapidly and we increasingly rely on them for valuable information and entertainment. However, finding meaningful rewards to motivate participation from the most qualified users, or experts, presents researchers with two main challenges: identifying these users and (2) rewarding their participation. Using an interdisciplinary theoretical framework, we illustrate possibilities for identifying and motivating the most valuable contributors to online communities. We suggest that access to peer-generated content can directly motivate people to apply their own expertise, thereby generating more content. Survey data from 380 participants suggests that users strongly prefer a novel class of incentives—reciprocal systemic rewards—to traditional achievement-based rewards. Overall, this research presents important considerations for many different types of online communities, including social networking and news aggregation sites.

CITATION:
DeAngelis, D. and K.S. Barber, “Systematic Reciprocal Rewards: Motivating Expert Participation in Online Communities with a Novel Class of Incentives,” International Journal of Agent Technologies and Systems (IJATS), Vol. 6(2), pp. 30-50, 2014

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.

Link to publication

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

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.

Link to publication

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

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.

Link to publication

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