Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

Displaying 1 - 2 of 2
Filtering by

Clear all filters

161988-Thumbnail Image.png

Formalizing Safety, Perception, and Mission Requirements for Testing and Planning in Autonomous Vehicles

Description
Autonomous Vehicles (AV) are inevitable entities in future mobility systems thatdemand safety and adaptability as two critical factors in replacing/assisting human drivers. Safety arises in defining, standardizing, quantifying, and monitoring requirements for all autonomous components. Adaptability, on the other hand, involves efficient handling of uncertainty and inconsistencies in models and data. First, I…

Autonomous Vehicles (AV) are inevitable entities in future mobility systems thatdemand safety and adaptability as two critical factors in replacing/assisting human drivers. Safety arises in defining, standardizing, quantifying, and monitoring requirements for all autonomous components. Adaptability, on the other hand, involves efficient handling of uncertainty and inconsistencies in models and data. First, I address safety by presenting a search-based test-case generation framework that can be used in training and testing deep-learning components of AV. Next, to address adaptability, I propose a framework based on multi-valued linear temporal logic syntax and semantics that allows autonomous agents to perform model-checking on systems with uncertainties. The search-based test-case generation framework provides safety assurance guarantees through formalizing and monitoring Responsibility Sensitive Safety (RSS) rules. I use the RSS rules in signal temporal logic as qualification specifications for monitoring and screening the quality of generated test-drive scenarios. Furthermore, to extend the existing temporal-based formal languages’ expressivity, I propose a new spatio-temporal perception logic that enables formalizing qualification specifications for perception systems. All-in-one, my test-generation framework can be used for reasoning about the quality of perception, prediction, and decision-making components in AV. Finally, my efforts resulted in publicly available software. One is an offline monitoring algorithm based on the proposed logic to reason about the quality of perception systems. The other is an optimal planner (model checker) that accepts mission specifications and model descriptions in the form of multi-valued logic and multi-valued sets, respectively. My monitoring framework is distributed with the publicly available S-TaLiRo and Sim-ATAV tools.
ContributorsHekmatnejad, Mohammad (Author) / Fainekos, Georgios (Thesis advisor) / Deshmukh, Jyotirmoy V (Committee member) / Karam, Lina (Committee member) / Pedrielli, Giulia (Committee member) / Shrivastava, Aviral (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
187820-Thumbnail Image.png

Differential Privacy Protection via Inexact Data Cloning

Description
With the advent of new advanced analysis tools and access to related published data, it is getting more difficult for data owners to suppress private information from published data while still providing useful information. This dual problem of providing useful, accurate information and protecting it at the same time has…

With the advent of new advanced analysis tools and access to related published data, it is getting more difficult for data owners to suppress private information from published data while still providing useful information. This dual problem of providing useful, accurate information and protecting it at the same time has been challenging, especially in healthcare. The data owners lack an automated resource that provides layers of protection on a published dataset with validated statistical values for usability. Differential privacy (DP) has gained a lot of attention in the past few years as a solution to the above-mentioned dual problem. DP is defined as a statistical anonymity model that can protect the data from adversarial observation while still providing intended usage. This dissertation introduces a novel DP protection mechanism called Inexact Data Cloning (IDC), which simultaneously protects and preserves information in published data while conveying source data intent. IDC preserves the privacy of the records by converting the raw data records into clonesets. The clonesets then pass through a classifier that removes potential compromising clonesets, filtering only good inexact cloneset. The mechanism of IDC is dependent on a set of privacy protection metrics called differential privacy protection metrics (DPPM), which represents the overall protection level. IDC uses two novel performance values, differential privacy protection score (DPPS) and clone classifier selection percentage (CCSP), to estimate the privacy level of protected data. In support of using IDC as a viable data security product, a software tool chain prototype, differential privacy protection architecture (DPPA), was developed to utilize the IDC. DPPA used the engineering security mechanism of IDC. DPPA is a hub which facilitates a market for data DP security mechanisms. DPPA works by incorporating standalone IDC mechanisms and provides automation, IDC protected published datasets and statistically verified IDC dataset diagnostic report. DPPA is currently doing functional, and operational benchmark processes that quantifies the DP protection of a given published dataset. The DPPA tool was recently used to test a couple of health datasets. The test results further validate the IDC mechanism as being feasible.
Contributorsthomas, zelpha (Author) / Bliss, Daniel W (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Banerjee, Ayan (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2023