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Differential Privacy for Microdata Streams: Adversarial Approaches
Get PDFMany attacks on personal privacy exist that create a variety of harm. As the world has become more interconnected, always-on, and real-time, many entities collect, aggregate, process, use, and disseminate the whereabouts, preferences, actions, and associations of humans everywhere that can subject them to surveillance, mistreatment, identity theft, and other invasions of our private lives. Laws, rules, and regulations have not protected fundamental rights of privacy, but where policymakers have failed, technological solutions have emerged. A significant development is e-differential privacy, a technique that can inject noise into data about individuals and their actions to strike a balance between the utility of personal information and the privacy of data subjects in such databases.
While differential privacy has a robust and sound approach, many data controllers and processors have failed to adopt it. Differential privacy is a high-stakes endeavor: if implemented incorrectly, published anonymized datasets can be reassociated to identify individuals. Moreover, differential privacy is more difficult to apply to event-level data, also known as microdata streams, that represent the same data subject many times in a database with slight changes as their location or behavior varies close to an identifiable personal norm. While the mathematical guarantees of the approach have withstood two decades of rigorous review and quantitative testing, practitioners lack tools that can validate correct implementations and appropriate privacy loss budgets against future attacks on published microdata streams.
This proposal addresses that research gap through quantitative technical action research that culminates in two experimental artifacts that adversarially interact: one that attempts to identify weaknesses in the application of differential privacy and another that tries to resist privacy-harming reassociation of event-level data. The iterative and adversarial interactions between these two artifacts allow for the progressive improvement of each that can both identify and treat implementation errors of differential privacy across various domains.
The production of generalized tools that can validate applications of differential privacy would yield a novel and significant contribution to many fields that generate or handle microdata streams, including geolocation data. Validating these tools first in a synthetic empirical cycle and then for a real-world scenario in a client engineering cycle using quantitative, statistical approaches enables future research and application of these artifacts and the principles they implement to protect the personal privacy of individuals in our increasingly connected and scrutinized lives
The Impact of Organizational Culture on Artificial Intelligence (AI) and Organizational Performance: A Mixed Methods Approach
Get PDFThe rapid evolution of artificial intelligence (AI) has created many opportunities for organizations to improve organizational performance. AI has increasingly become an integral part of the digital strategy for organizations. Despite these benefits, many organizations are still struggling to leverage AI to gain competitive advantage. Although previous research has explored the notion of technology adoption, very little literature exist that examines the impact of organizational culture on AI adoption in organizations using a mixed methods approach. Specifically, prior literature investigating aspects of the organizations that enable AI adoption has focused on challenges associated with the technical aspects such as the capability, thus providing inadequate insight about the impact of organizational culture on AI adoption and organizational performance. To address this gap, this study has conducted a sequential mixed methods research design indicated as QUAN→ qual to collect data from 96-United States (US) based organizations using a questionnaire and semi-structured interviews with 9 technology leaders in organizations to examine how organizational culture impacts AI adoption and organizational performance. This study proposes a theoretical model adapted from the technology organization environment framework (TOE) and the diffusion of innovation (DOI) theory. Accordingly, informed by the literature, this study has formulated six hypotheses presented in the research model to explain the relationship among constructs: transformational leadership, organizational culture, AI capability, organizational creativity, AI adoption, and organizational performance.
The results from the study indicate that transformational leadership is an antecedent of organizational culture, as is AI capability an antecedent of AI adoption, and organizational creativity plays a mediating role between AI capability and AI adoption to positively influence organizational performance. The results suggest that a nurturing AI-friendly organizational culture can help organizations leverage AI and improve organizational performance. This study makes significant contributions to the information systems (IS) literature by examining the impact of organizational culture, particularly when adopting new technologies like artificial intelligence (AI), and it provides practical insights for managers to leverage AI and enhance organizational performance
Ethanol Concentration in Gasoline
Get PDFFor this research project, we will be measuring the concentration of ethanol present in different types of gasoline from various gas stations. The purpose of this project is to analyze and determine whether there is a substantial variance in the concentration, especially if it is possibly detrimental towards gas mileage and the product you intend to purchase is not what you are receiving. Our results have potential to lead to further research and discovery dependent upon if there is a significant variance found, this could mean a fault in the production or delivery lines of the gasolines.https://scholar.dsu.edu/research-symposium/1062/thumbnail.jp
Research Poster
Get PDFFor my project | sought out to make a traffic light simulator and Opticom as well as a Mobile Infared Transmitter(MIRT). | explored how to wire an Arduino and write instructions to GPIO pins.https://scholar.dsu.edu/research-symposium/1057/thumbnail.jp
NIT: a Dataset for Network Intent Translation
No full textThis paper introduces the NIT (Network Intent Translations) dataset. The NIT dataset is designed for network intent translations, specifically for intent-to-vendor translations. The dataset contains network configuration scenarios described in natural language (intents) and their corresponding low-level configurations for Juniper EX3300 switches with JUNOS Base OS boot [12.3R12-S12]. The dataset was generated following quality measures by validating the correctness of each completion on a Juniper Ex3300 switch and verifying the syntax using configuration manuals. The dataset underwent several revisions to ensure consistent formatting, correct parameter extraction, and accurate outcomes. The dataset is designed to provide translations of high-level network intent to command-line configuration only. It does not target verifying the correctness of the parameters\u27 values in the prompts or any other type of conflict detection or resolution. Despite this, all parameter values included in the dataset are valid values for the corresponding task. The dataset contains 1000 entries; each entry is presented using a JSON object of three elements: question, context, and answer. The question represents the high-level network intent, while the answer represents the corresponding low-level configurations. The context includes the generic form of the low-level configurations. The dataset is suitable for network intent translation research
Dimensions of Artificial Intelligence Maturity Models in Supply Chain Management: A Systematic Literature Review
No full textIn an era where Artificial Intelligence (AI) is revolutionizing industries, understanding its integration within Supply Chain Management (SCM) is paramount. The burgeoning integration of AI into SCM necessitates a robust framework to assess the readiness and maturity of supply chains in adopting AI technologies. The study identifies existing SCM maturity models, delineates their dimensions, and examines the challenges associated with employing these models to measure the maturity level of SCM with respect to integrating AI. By synthesizing the findings, this study underscores AI’s role in optimizing SCM, highlighting key maturity dimensions, enriching the AI maturity discourse, proposing a dimensionality tailored to SCM’s unique demands, and practically it advocates for the development of standardized capability maturity model (CMM) to guide organizations towards AI-driven SCM excellence that navigates digital transformation, enhancing competitive advantage and addressing SCM challenges
Human Cognitive Bias Mitigation Approaches to Fairness within the Machine Learning Value Chain: A Review and Research Agenda
Get PDFThis systematic review examines the influence of human cognitive biases on machine learning (ML) systems across the 9 phases of the ML algorithmic value chain. Following the PRISMA guidelines, it synthesizes 19 studies on bias integration and management within ML, highlighting techniques to reduce bias and increase fairness. The review identifies key gaps: the unclear translation of human cognitive biases to ML biases, absence of metrics to measure biases, re-introduction of biases during debiasing, and the critical need for human intervention. These findings prompt several research themes spanning human cognition and algorithmic bias. The theoretical implications are three-fold: extending bias concepts to human cognition, creating an agenda to associate cognitive biases with ML outcomes, and assessing the need for a new or extended discipline. Practically, it raises awareness of human cognition in ML fairness, leading to improved methods for data handling
Characteristic determinant approach to the spectrum of one-dimensional PT-symmetric systems
No full textWe obtain a closed-form expression for the energy spectrum of -symmetric superlattice systems with complex potentials of periodic sets of two potentials in the elementary cell. In the presence of periodic gain and loss, we analyzed in detail a diatomic crystal model, varying either the scatterer distances or the potential heights. It is shown that at a certain critical value of the imaginary part of the complex amplitude, topological states depending on the lattice size and the configuration of the unit cell can be pushed out from the given region. This may happen at the -symmetry breaking (exceptional) points
Logging and Telemetry Gap Identification of Remote Vulnerability Exploitation
No full textThis research study conducts an analysis and provides a method for the measurement of the effectiveness of various logging standards through a design science methodology with an applied experimentation evaluation. The design science method of this research introduces a purpose-created tool, the Security Exploit Telemetry Collection (SETC) framework, to provide a robust lab environment that records rich, repeatable, and highly configurable security telemetry of attacks against vulnerable services. The framework achieves the collection of security telemetry by hosting and exploiting vulnerable services in a controlled container environment. The applied experimentation component of this research utilizes data produced by SETC to evaluate the effectiveness of logging standards through a novel measurement approach. This data is analyzed using both effectiveness scores, which quantify telemetry preservation from raw logs to standardized formats, and cardinal detection scores, which assess practical security monitoring capabilities across a defined attack chain. Through controlled experimentation involving 50 remote code execution vulnerabilities, this research establishes the first quantitative comparison of modern logging standards from a security perspective. The findings reveal that the evaluated standards show varying degrees of telemetry preservation. All examined standards exhibit substantial gaps in detecting initial compromise, with network service-based vulnerabilities proving to exhibit the most significant gap. A critical discovery is that the absence of HTTP POST body and header data in standardized formats required fields renders the majority of web-based exploits undetectable. The research also demonstrates that post-exploitation activities maintain significantly better visibility across all logging standards. These findings provide evidence-based insights for security practitioners and identify systematic blind spots in logging standards that have immediate implications for enterprise security monitoring and incident detection capabilities
Advancing Cybersecurity Assurance: A Novel Outcome-Based Approach for Evaluating Cybersecurity Controls
No full textThe growing frequency and complexity of cyberattacks pose a serious and escalating risk to both individuals and organizations. Current cybersecurity assurance approaches often fall short in effectively measuring the adequacy of cybersecurity controls in terms of mitigating risks and providing measurable value to organizations. The current approaches are often focused on measuring compliance with predetermined requirements as defined in government regulation or an industry standard, thereby overlooking the need for a holistic approach that incorporates all aspects of security. This dissertation is an attempt to address this gap by developing a novel outcome-based cybersecurity assurance approach.
The research problem focused on the lack of a comprehensive, outcome-based cybersecurity assurance approach that effectively assesses whether cybersecurity controls are achieving their intended outcomes instead of simply verifying compliance. Existing cybersecurity assurance approaches fail to consider the human and process aspects of security controls, and the importance of ensuring that cybersecurity controls provide the intended value to the organizations. This limitation creates a scenario where organizations make significant investments in terms of their security controls but lack a way to determine whether those controls are actually providing the protection and value to the organization that they are meant to provide, or more importantly, if the controls are even truly warranted in the context of the organization.
This dissertation followed the design science research methodology, which includes iterative design and an evaluation process. Consistent with design science research methodology, the iterative process included multiple cycles of design and validation. Additionally, the Delphi Method was followed in collecting feedback from a panel of industry experts which was incorporated into the artifact design. The outcome of this dissertation is an artifact in the form of a cybersecurity assurance approach, which is being termed as an outcome-based cybersecurity assurance approach.
The outcome-based approach provides a structured process for conducting comprehensive cybersecurity assurance analyses, including guidance on input considerations for each step such as selecting a relevant framework, determining the objectives of the assessment, identifying and articulating the intended outcome for a control, control evaluation criteria and success metrics etc. The approach was enhanced as part of multiple iterations to ensure that the final version is easy to follow and implement, and easy to adapt by any organization irrespective of its type, size, the industry it operates in. The outcome-based assurance approach is expected to empower and enable organizations to make informed decisions about security, enhancing their operational resilience, and improving overall security posture