Visible to the public Biblio

Filters: Author is Barber, K. Suzanne  [Clear All Filters]
2020-04-03
Liau, David, Zaeem, Razieh Nokhbeh, Barber, K. Suzanne.  2019.  Evaluation Framework for Future Privacy Protection Systems: A Dynamic Identity Ecosystem Approach. 2019 17th International Conference on Privacy, Security and Trust (PST). :1—3.
In this paper, we leverage previous work in the Identity Ecosystem, a Bayesian network mathematical representation of a person's identity, to create a framework to evaluate identity protection systems. Information dynamic is considered and a protection game is formed given that the owner and the attacker both gain some level of control over the status of other PII within the dynamic Identity Ecosystem. We present a policy iteration algorithm to solve the optimal policy for the game and discuss its convergence. Finally, an evaluation and comparison of identity protection strategies is provided given that an optimal policy is used against different protection policies. This study is aimed to understand the evolutionary process of identity theft and provide a framework for evaluating different identity protection strategies and future privacy protection system.
2020-01-21
Rana, Rima, Zaeem, Razieh Nokhbeh, Barber, K. Suzanne.  2019.  An Assessment of Blockchain Identity Solutions: Minimizing Risk and Liability of Authentication. 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI). :26–33.
Personally Identifiable Information (PII) is often used to perform authentication and acts as a gateway to personal and organizational information. One weak link in the architecture of identity management services is sufficient to cause exposure and risk identity. Recently, we have witnessed a shift in identity management solutions with the growth of blockchain. Blockchain-the decentralized ledger system-provides a unique answer addressing security and privacy with its embedded immutability. In a blockchain-based identity solution, the user is given the control of his/her identity by storing personal information on his/her device and having the choice of identity verification document used later to create blockchain attestations. Yet, the blockchain technology alone is not enough to produce a better identity solution. The user cannot make informed decisions as to which identity verification document to choose if he/she is not presented with tangible guidelines. In the absence of scientifically created practical guidelines, these solutions and the choices they offer may become overwhelming and even defeat the purpose of providing a more secure identity solution.We analyze different PII options given to users for authentication on current blockchain-based solutions. Based on our Identity Ecosystem model, we evaluate these options and their risk and liability of exposure. Powered by real world data of about 6,000 identity theft and fraud stories, our model recommends some authentication choices and discourages others. Our work paves the way for a truly effective identity solution based on blockchain by helping users make informed decisions and motivating blockchain identity solution providers to introduce better options to their users.
2019-05-20
Chang, Kai Chih, Zaeem, Razieh Nokhbeh, Barber, K. Suzanne.  2018.  Enhancing and Evaluating Identity Privacy and Authentication Strength by Utilizing the Identity Ecosystem. Proceedings of the 2018 Workshop on Privacy in the Electronic Society. :114–120.
This paper presents a novel research model of identity and the use of this model to answer some interesting research questions. Information travels in the cyber world, not only bringing us convenience and prosperity but also jeopardy. Protecting this information has been a commonly discussed issue in recent years. One type of this information is Personally Identifiable Information (PII), often used to perform personal authentication. People often give PIIs to organizations, e.g., when applying for a new job or filling out a new application on a website. While the use of such PII might be necessary for authentication, giving PII increases the risk of its exposure to criminals. We introduce two innovative approaches based on our model of identity to help evaluate and find an optimal set of PIIs that satisfy authentication purposes but minimize risk of exposure. Our model paves the way for more informed selection of PIIs by organizations that collect them as well as by users who offer PIIs to these organizations.