Exploring Trusted Platform Module Capabilities: A Theoretical and Experimental Study

Trusted platform modules (TPMs) are hardware modules that are bound to a computer's motherboard, that are being included in many desktops and laptops. Augmenting computers with these hardware modules adds powerful functionality in distributed settings, allowing us to reason about the security of these systems in new ways. In this dissertation, I study the functionality of TPMs from a theoretical as well as an experimental perspective. On the theoretical front, I leverage various features of TPMs to construct applications like random oracles that are impossible to implement in a standard model of computation. Apart from random oracles, I construct a new cryptographic primitive which is basically a non-interactive form of the standard cryptographic primitive of oblivious transfer. I apply this new primitive to secure mobile agent computations, where interaction between various entities is typically required to ensure security. I prove these constructions are secure using standard cryptographic techniques and assumptions. To test the practicability of these constructions and their applications, I performed an experimental study, both on an actual TPM and a software TPM simulator which has been enhanced to make it reflect timings from a real TPM. This allowed me to benchmark the performance of the applications and test the feasibility of the proposed extensions to standard TPMs. My tests also show that these constructions are practical

Coupled superconducting microwave resonators for studies of electro-mechanical interaction

The motivation behind the work described in this thesis is to study the coupling between a nanobar and a pair of identical, coupled, superconducting microwave resonators, where the splitting frequency at their avoided crossing is close to the nanobar resonant frequency. The splitting frequency as a function of the coupling between the microwave resonators has been thoroughly investigated by theoretical simulations in COMSOL and AIM Spice, and experimentally verified by low temperature measurements. Deviations of the measured splitting from the theoretical values and reflection measurements showed that the resonators required to be tuned in order to reach the avoided crossing. A novel tuning mechanism was devised and implemented in-situ in the experiments. Tuning of resonators was successfully achieved and there was excellent agreement of the measured splitting with the predicted values. A wide frequency tuning range of 50 MHz was obtained, more than required for our experiments, without degrading the high resonator quality factors (~10$^5$). This enabled the measurement of the inherent splitting of the coupled resonator frequencies at the avoided crossing, and more importantly, paves the way for studies of electro-mechanical interaction. In the absence of nanobars, an analogous experiment that varied the resonator inductance instead of its capacitance was devised. The resonant frequency of one of the resonators was perturbed using a small amplitude magnetic field using a coil placed underneath the sample, a case that has not been previously explored. The results obtained from these preliminary experiments have shown a good agreement with the theoretical predictions

Machine Learning Framework: Competitive Intelligence and Key Drivers Identification of Market Share Trends Among Healthcare Facilities

The necessity of data driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a Healthcare Provider Facility or a Hospital (from here on termed as Facility) Market Share is of key importance. This pilot study aims at developing a data driven Machine Learning - Regression framework which aids strategists in formulating key decisions to improve the Facilitys Market Share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study; and the data spanning across 60 key Facilities in Washington State and about 3 years of historical data is considered. In the current analysis Market Share is termed as the ratio of facility encounters to the total encounters among the group of potential competitor facilities. The current study proposes a novel two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. The proposed method to identify pool of competitors in current analysis, develops Directed Acyclic Graphs (DAGs), feature level word vectors and evaluates the key connected components at facility level. This technique is robust since its data driven which minimizes the bias from empirical techniques. Post identifying the set of competitors among facilities, developed Regression model to predict the Market share. For relative quantification of features at a facility level, incorporated SHAP a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share.Comment: 7 Pages 5 figures 6 tables To appear in ICHA 202