Mitigating product abuse through privacy-preserving and secure technologies in digital and industrial systems
Date
2025
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Delaware
Abstract
The rapid digitalization of modern life has enabled unprecedented convenience and efficiency while simultaneously creating new opportunities for exploitation, misuse, and privacy violations. This dissertation investigates product abuse (the intentional misuse or manipulation of technological systems beyond their intended design) across distinct digital domains to surface its security, privacy, and operational implications. Using real world datasets, deployed prototypes, and empirical vulnerability assessments, it provides a cross domain examination of how abuse emerges and how defenses succeed or fail in practice. ☐ Case Study 1 (Email Tracking) analyzes how embedded tracking beacons in email communication can be repurposed as tools for covert surveillance and behavioral profiling. Through large scale measurement and analysis, the study exposes the privacy risks posed by such mechanisms, highlighting how legitimate business tools can cross the boundary into privacy abuse. ☐ Case Study 2 (CAPTCHA) surveys 24,000+ web pages from the Alexa Top 50K and correlates implementation patterns with 179 MITRE CVEs (2005–2025). The study finds that most failures stem from implementation errors, weak server-side validation, and supply chain issues, not the intrinsic design of challenges and documents how AI assisted solvers & paid solving economies further erode resilience, with practical hardening recommendations. ☐ Case Study 3 (Industrial IoT / WMS) examines abuse in industrial environments integrating Decision Support Systems, IoT devices, and Warehouse Management Systems. Drawing on a deployed prototype and operational data, it identifies attack surfaces that enable product manipulation, data leakage, and supply chain interference, and proposes blockchain backed audit trails, stronger authentication, and anomaly detection to enhance cyber resilience. ☐ Case Study 4 (Automated Crypto Trading) evaluates Mean Reversion, Arbitrage, Grid Trading, and Mean Deviation strategies as both efficiency enablers and abuse vectors. Experiments highlight how automation, if poorly designed or exploited, can induce market manipulation and systemic instability, motivating transparency, guardrails, and regulation aware algorithmic design. ☐ The dissertation (i) consolidates empirical evidence that product abuse recurs across heterogeneous systems; (ii) maps dominant failure modes from client-side exposure and automation to server-side validation gaps and supply chain weaknesses; (iii) demonstrates deployable countermeasures, privacy preserving email defenses, CAPTCHA hardening practices, IIoT/WMS auditability and access control, and ethics \& compliance aware algorithm design; and (iv) offers a practical threat informed rubric for engineering teams to anticipate misuse, not merely react to incidents. ☐ In conclusion, this dissertation offers both diagnostic and prescriptive perspectives on digital product abuse. It establishes that while product abuse cannot be fully eliminated, it can be systematically reduced through better architecture, stronger accountability, and adaptive security mechanisms that evolve alongside technological progress. By capturing the interplay between innovation, exploitation, and defense, this work contributes to the ongoing discourse on building secure, privacy preserving, and trustworthy digital ecosystems.
Description
Keywords
Automated trading systems, CAPTCHA security, Cybersecurity, Email tracking, Product abuse, Warehouse management