UNDERSTANDING CAREER ASPIRATIONS AMONG YOUNG WOMEN: IMPROVING INSTRUMENTATION

The purpose of this study was to improve an instrument used to assess career aspirations (the Career Aspiration Scale) so the revised measure can be used with confidence by counseling psychologists in research and practice. Three studies were conducted with a total of 583 undergraduate and graduate women. Results of these studies provided support for the reliability and validity of the Career Aspiration Scale-Revised when used with undergraduate and graduate women. Results from confirmatory factor analyses indicated that the three-factor solution had good model fit, thus supporting a revised measure with three subscales assessing achievement, leadership, and educational aspirations. Suggestions for future research and practice using the Career Aspiration Scale- Revised are provided.

Language-based Techniques for Practical and Trustworthy Secure Multi-party Computations

Secure Multi-party Computation (MPC) enables a set of parties to collaboratively compute, using cryptographic protocols, a function over their private data in a way that the participants do not see each other's data, they only see the final output. Typical MPC examples include statistical computations over joint private data, private set intersection, and auctions. While these applications are examples of monolithic MPC, richer MPC applications move between "normal" (i.e., per-party local) and "secure" (i.e., joint, multi-party secure) modes repeatedly, resulting overall in mixed-mode computations. For example, we might use MPC to implement the role of the dealer in a game of mental poker -- the game will be divided into rounds of local decision-making (e.g. bidding) and joint interaction (e.g. dealing). Mixed-mode computations are also used to improve performance over monolithic secure computations. Starting with the Fairplay project, several MPC frameworks have been proposed in the last decade to help programmers write MPC applications in a high-level language, while the toolchain manages the low-level details. However, these frameworks are either not expressive enough to allow writing mixed-mode applications or lack formal specification, and reasoning capabilities, thereby diminishing the parties' trust in such tools, and the programs written using them. Furthermore, none of the frameworks provides a verified toolchain to run the MPC programs, leaving the potential of security holes that can compromise the privacy of parties' data. This dissertation presents language-based techniques to make MPC more practical and trustworthy. First, it presents the design and implementation of a new MPC Domain Specific Language, called Wysteria, for writing rich mixed-mode MPC applications. Wysteria provides several benefits over previous languages, including a conceptual single thread of control, generic support for more than two parties, high-level abstractions for secret shares, and a fully formalized type system and operational semantics. Using Wysteria, we have implemented several MPC applications, including, for the first time, a card dealing application. The dissertation next presents Wys*, an embedding of Wysteria in F*, a full-featured verification oriented programming language. Wys* improves on Wysteria along three lines: (a) It enables programmers to formally verify the correctness and security properties of their programs. As far as we know, Wys* is the first language to provide verification capabilities for MPC programs. (b) It provides a partially verified toolchain to run MPC programs, and finally (c) It enables the MPC programs to use, with no extra effort, standard language constructs from the host language F*, thereby making it more usable and scalable. Finally, the dissertation develops static analyses that help optimize monolithic MPC programs into mixed-mode MPC programs, while providing similar privacy guarantees as the monolithic versions.

BLACK, MALE, AND HIGH-ACHIEVING: AN EXAMINATION OF A RISK FACTOR AND CULTURAL RESOURCES FOR BLACK MALE ACADEMIC ACHIEVEMENT

Black boys are confronted with unique educational circumstances. They are often misdiagnosed and misplaced into special education programs (Bush-Daniels, 2008; Patton, 1998; Terman et al., 1996). Additionally, they are less likely to be enrolled in gifted and talented programs, even if their former achievements reflect their aptitude to succeed (Black Alliance for Educational Options, n.d.; Moore & Flowers, 2012). Given these statistics, a considerable emphasis has been placed on the causes and the consequences of low/under achievement for this population. As a result, the experiences of Black males who are achieving have been greatly neglected. Moreover, little is known about the factors that facilitate academic achievement among high-achieving Black boys. In an effort to bring the heterogenic nature of schooling experiences for Black boys to light, the present study examined the influence risk and protective factors had on the academic experiences of high-achieving Black boys. Grounded in the risk and resilience framework and the Integrative Model for the Study of Minority Youth Development, this study explored whether the high-achieving Black high school boys in this sample (n =88) reported experiencing discrimination (i.e. academic-based) and how this academic-based discrimination related to their 1) academic performance (i.e. GPA), 2) perceptions of math ability, and 3) race-based academic self-concept. In addition to exploring how academic-based discrimination was linked to academic achievement, this study examined how cultural resources such as racial socialization messages and racial identity related to academic achievement. Specifically, cultural socialization, preparation for bias, egalitarianism, private regard and public regard were evaluated alongside the three academic outcomes under study. Finally, the study explored whether aspects of racial socialization or racial identity buffered the effects of discrimination on any of the outcomes. Interestingly, the race/ethnicity of the student mattered for how students perceived their math ability. The risk factor academic-based discrimination was linked to academic performance. Cultural resources cultural socialization, preparation for bias, and private regard were linked to various academic outcomes of interest. There was only one significant moderating effect: a high private regard buffered the relationship between academic-based discrimination and race-based academic self-concept. Limitations and implications of this study are discussed.