Advanced Data Integrity and Privacy in Machine Learning: Integrating Data Cleaning, Differential Privacy, and Model Robustness

Abstract

The exponential growth of machine learning (ML) applications in contemporary data-driven environments has necessitated rigorous frameworks for ensuring data integrity, privacy, and model reliability. This research explores the intersections of data cleaning, differential privacy, and adversarial robustness within ML pipelines, highlighting their collective significance in maintaining credible and secure predictive systems. Emphasis is placed on the role of automated data cleaning systems, such as NADEEF, in detecting and resolving inconsistencies in heterogeneous datasets, thereby facilitating high-quality training inputs that enhance model generalization (Dallachiesat et al., 2013). Concurrently, differential privacy mechanisms are examined for their capacity to mitigate information leakage while balancing utility, drawing upon seminal frameworks and noise calibration techniques (Dwork, 2008; Dwork et al., 2006; Dwork & Roth, 2014). The paper further addresses the challenges of model underspecification and susceptibility to adversarial manipulation, elucidating their implications for credibility and reproducibility in ML applications (D’Amour et al., 2020; Feinman et al., 2017; Fredrikson et al., 2015). Methodological considerations encompass descriptive analyses of data integration, automated workflow validation, and symbolic reasoning strategies, demonstrating the synergistic potential of combining data-centric and model-centric interventions (Dong & Rekatsinas, 2018; Chandra, 2025; Ling et al., 2023). The discussion contextualizes these frameworks within software engineering practices, considering development methodologies, algorithmic verification, and build management in relation to secure, transparent, and maintainable ML systems (Sommerville, 2015; Anghel et al., 2022; Varanasi, 2019). Finally, this study underscores critical future directions, advocating for adaptive privacy-preserving pipelines, enhanced robustness against adversarial threats, and integrative strategies that bridge data integrity with reliable model reasoning.