Understanding customer journeys through their interactions with marketing touchpoints is critical for user's behavior modeling and advertiser's digital marketing strategy optimization, but privacy restrictions from Walled Garden platforms create barriers and challenges to such practices. Although the ideal data an advertiser requires for marketing data analysis would be detailed individual level activity data and ads interaction behaviors, the data an advertiser can obtain from a Walled Garden platform are aggregate level statistics. To overcome this gap, this paper introduces a novel approach, Linear Programming Reconstruction, which primarily relies on the linear programming algorithm that leverages aggregated Walled Garden platform statistics, while integrating detailed event level data from other marketing channels to reconstruct the complete individual level interaction journeys. We detail the proposed approach and provide an initial demonstration of its effectiveness in reconstruction by applying it in building an attribution model through experiments. The approach provides a valuable solution for overcoming data limitations from Walled Garden platforms, making it possible for deeper user behavior modeling and improved marketing strategies in the context of evolving privacy regulations.