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You searched for +publisher:"University of Arkansas" +contributor:("Brajendra Panda"). Showing records 1 – 2 of 2 total matches.

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University of Arkansas

1. Wu, Yongkai. Achieving Causal Fairness in Machine Learning.

Degree: PhD, 2020, University of Arkansas

Fairness is a social norm and a legal requirement in today's society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative task to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also subject to fairness requirements. In the literature, machine learning researchers have proposed association-based fairness notions, e.g., statistical parity, disparate impact, equality of opportunity, etc., and developed respective discrimination mitigation approaches. However, these works did not consider that fairness should be treated as a causal relationship. Although it is well known that association does not imply causation, the gap between association and causation is not paid sufficient attention by the fairness researchers and stakeholders. The goal of this dissertation is to study fairness in machine learning, define appropriate fairness notions, and develop novel discrimination mitigation approaches from a causal perspective. Based on Pearl's structural causal model, we propose to formulate discrimination as causal effects of the sensitive attribute on the decision. We consider different types of causal effects to cope with different situations, including the path-specific effect for direct/indirect discrimination, the counterfactual effect for group/individual discrimination, and the path-specific counterfactual effect for general cases. In the attempt to measure discrimination, the unidentifiable situations pose an inevitable barrier to the accurate causal inference. To address this challenge, we propose novel bounding methods to accurately estimate the strength of unidentifiable fairness notions, including path-specific fairness, counterfactual fairness, and path-specific counterfactual fairness. Based on the estimation of fairness, we develop novel and efficient algorithms for learning fair classification models. Besides classification, we also investigate the discrimination issues in other machine learning scenarios, such as ranked data analysis. Advisors/Committee Members: Xintao Wu, Qinghua Li, Brajendra Panda.

Subjects/Keywords: Algorithmic Bias; Causal Inference; Fairness; Machine Learning; Artificial Intelligence and Robotics; Theory and Algorithms

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wu, Y. (2020). Achieving Causal Fairness in Machine Learning. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/3632

Chicago Manual of Style (16th Edition):

Wu, Yongkai. “Achieving Causal Fairness in Machine Learning.” 2020. Doctoral Dissertation, University of Arkansas. Accessed November 29, 2020. https://scholarworks.uark.edu/etd/3632.

MLA Handbook (7th Edition):

Wu, Yongkai. “Achieving Causal Fairness in Machine Learning.” 2020. Web. 29 Nov 2020.

Vancouver:

Wu Y. Achieving Causal Fairness in Machine Learning. [Internet] [Doctoral dissertation]. University of Arkansas; 2020. [cited 2020 Nov 29]. Available from: https://scholarworks.uark.edu/etd/3632.

Council of Science Editors:

Wu Y. Achieving Causal Fairness in Machine Learning. [Doctoral Dissertation]. University of Arkansas; 2020. Available from: https://scholarworks.uark.edu/etd/3632


University of Arkansas

2. Almakdi, Sultan Ahmed A. Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud.

Degree: PhD, 2020, University of Arkansas

Recently, database users have begun to use cloud database services to outsource their databases. The reason for this is the high computation speed and the huge storage capacity that cloud owners provide at low prices. However, despite the attractiveness of the cloud computing environment to database users, privacy issues remain a cause for concern for database owners since data access is out of their control. Encryption is the only way of assuaging users’ fears surrounding data privacy, but executing Structured Query Language (SQL) queries over encrypted data is a challenging task, especially if the data are encrypted by a randomized encryption algorithm. Many researchers have addressed the privacy issues by encrypting the data using deterministic, onion layer, or homomorphic encryption. Nevertheless, even with these systems, the encrypted data can still be subjected to attack. In this research, we first propose an indexing scheme to encode the original table’s tuples into bit vectors (BVs) prior to the encryption. The resulting index is then used to narrow the range of retrieved encrypted records from the cloud to a small set of records that are candidates for the user’s query. Based on the indexing scheme, we then design three different models to execute SQL queries over the encrypted data. The data are encrypted by a single randomized encryption algorithm, namely the Advanced Encryption Standard AES-CBC. In each proposed scheme, we use a different (secure) method for storing and maintaining the index values (BVs) (i.e., either at user’s side or at the cloud server), and we extend each system to support most of relational algebra operators, such as select, join, etc. Implementation and evaluation of the proposed systems reveals that they are practical and efficient at reducing both the computation and space overhead when compared with state-of-the-art systems like CryptDB. Advisors/Committee Members: Brajendra Panda, Susan Gauch, Miaoqing Huang.

Subjects/Keywords: Cloud Databases; Cloud Security; Database Security; Encrypted Databases; Outsourced Databases; Query Processing; Databases and Information Systems; Information Security

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Almakdi, S. A. A. (2020). Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/3578

Chicago Manual of Style (16th Edition):

Almakdi, Sultan Ahmed A. “Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud.” 2020. Doctoral Dissertation, University of Arkansas. Accessed November 29, 2020. https://scholarworks.uark.edu/etd/3578.

MLA Handbook (7th Edition):

Almakdi, Sultan Ahmed A. “Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud.” 2020. Web. 29 Nov 2020.

Vancouver:

Almakdi SAA. Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud. [Internet] [Doctoral dissertation]. University of Arkansas; 2020. [cited 2020 Nov 29]. Available from: https://scholarworks.uark.edu/etd/3578.

Council of Science Editors:

Almakdi SAA. Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud. [Doctoral Dissertation]. University of Arkansas; 2020. Available from: https://scholarworks.uark.edu/etd/3578

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