I2S Masters/ Doctoral Theses

As Electronic Health Records (EHRs) become more available, there is increasing interest in discovering hidden disease patterns by leveraging cutting-edge data visualization techniques, such as graph-based knowledge representation and interactive graphical user interfaces (GUIs). In this project, we have developed a web-based interactive EHR analytics and visualization tool to provide healthcare professionals with valuable insights that can ultimately improve the quality and cost-efficiency of patient care. Specifically, we have developed two visualization panels: one for the intelligence of individual patients and the other for the relevance among diseases. For individual patients, we capture the similarity between them by linking them based on their relatedness in diagnosis. By constructing a graph representation of patients based on this similarity, we can identify patterns and trends in patient data that may not be apparent through traditional methods. For disease relationships, we provide an ontology graph for the specific diagnosis (ICD10 code), which helps to identify ancestors and predecessors of a particular diagnosis. Through the demonstration of this dashboard, we show that this approach can provide valuable insights to better understand patient outcomes with an informative and user-friendly web interface.

This research project aims to predict the outcome of the Naval Discharge Review Board decision for an applicant based on factors in the application, using Machine Learning techniques. The study explores three popular machine learning algorithms: MLP, Adaboost, and KNN, with KNN providing the best results. The training is verified through hyperparameter optimization and cross fold validation.

Additionally, the study investigates the ability of ChatGPT's API to classify the data that couldn't be classified manually. A total of over 8000 samples were classified by ChatGPT's API, and an MLP model was trained using the same hyperparameters that were found to be optimal for the 3000 size manual sample.The model was then tested on the manual sample. The results show that the model trained on data labeled by ChatGPT performed equivalently, suggesting that ChatGPT's API is a promising tool for labeling in this domain.

Development and deployment of trusted systems often require definitive identification of devices. A remote entity should have confidence that a device is as it claims to be. An ideal method for fulfulling this need is through the use of secure device identitifiers. A secure device identifier (DevID) is defined as an identifier that is cryptographically bound to a device. A DevID must not be transferable from one device to another as that would allow distinct devices to be identified as the same. Since the Trusted Platform Module (TPM) is a secure Root of Trust for Storage, it provides the necessary protections for storing these identifiers. Consequently, the Trusted Computing Group (TCG) recommends the use of TPM keys for DevIDs. The TCG's specification TPM 2.0 Keys for Device Identity and Attestation describes several methods for remotely proving a key to be resident in a specific device's TPM. These methods are carefully constructed protocols which are intended to be performed by a trusted Certificate Authority (CA) in communication with a certificate-requesting device. DevID certificates produced by an OEM's CA at device manufacturing time may be used to provide definitive evidence to a remote entity that a key belongs to a specific device. Whereas DevID certificates produced by an Owner/Administrator's CA require a chain of certificates in order to verify a chain of trust to an OEM-provided root certificate. This distinction is due to the differences in the respective protocols prescribed by the TCG's specification. We aim to abstractly model these protocols and formally verify that their resulting assurances on TPM-residency do in fact hold. We choose this goal since the TCG themselves do not provide any proofs or clear justifications for how the protocols might provide these assurances. The resulting TPM-command library and execution relation modeled in Coq may easily be expanded upon to become useful in verifying a wide range of properties regarding DevIDs and TPMs.

During remote attestation, a relying party prompts a target to perform some stateful measurement which can be appraised to determine trust in the target's system. In this current framework, requested measurement operations must be provisioned by a knowledgeable system user who may fail to consider situational demands which potentially impact the desired measurement. To solve this problem, we introduce negotiation: a framework that allows the target and relying party to mutually determine an attestation protocol that satisfies both the target's need to protect sensitive information and the relying party's desire for a comprehensive measurement. We designed and verified this negotiation procedure such that for all negotiations, we can provably produce an executable protocol that satisfies the targets privacy standards. With the remainder of this work, we aim to realize and instantiate protocol orderings ensuring negotiation produces a protocol sufficient for the relying party. All progress is towards our ultimate goal of producing a working, fully verified negotiation scheme which will be integrated into our current attestation framework for flexible, end-to-end attestations.