Critical operations often involve stakeholders with diverse perspectives, yet centralized optimization assumes participation or private information, neither of which is a priori guaranteed. Additionally, decision-making involves discrete decisions, making optimization computationally challenging. Centralized formulations use approximations to manage complexity, often overlooking stakeholder perspectives, leading to bias. To resolve these challenges, we adopt a privacy-aware participatory-distributed (PAPD) optimization approach that reduces the combinatorial complexity upon decomposition, efficiently coordinates stakeholder subproblems without requiring private information, and allows stakeholders to actively and flexibly participate. Using challenging instances of large-scale Generalized Assignment Problems (GAPs) from the OR library and electrification of transportation scheduling instances, we demonstrate that the quality of our PAPD-obtained solutions decreases marginally compared to non-privacy-aware centralized solutions. However, this minor reduction in solution quality is outweighed by significantly lower make-whole payments (MWP), namely, compensations provided to participants to ensure they cover their costs and do not incur losses following the coordinated solution, than those associated with centralized methods. Reducing MWP enhances market transparency by ensuring stakeholder compensation is based on their decisions rather than centralized or out-of-market settlements. This minimizes the need for additional compensation and promotes a more fair and transparent market. The main takeaway is that PAPD decision-making aligns closely with minimizing system costs while preserving privacy, improving participation, and enhancing transparency compared to both traditional centralized and non-privacy-aware decomposition methods.