Privacy-Preserved Meeting Organization

Abstract

Abstract The modern organizations increasingly depend on virtual and hybrid meetings to coordinate remote and distributed teams. While these meeting formats offer flexibility and accessibility, they also introduce significant meeting privacy challenges specially when sensitive documents and diverse participant roles are involved. Ad-hoc scheduling practices often result in unauthorized access to confidential content and the selection of inappropriate meeting modes, thereby compromising meeting privacy. This study investigates whether enforcing document-based access control policies (Access Control Lists or ACLs) and considering participant locations can lead to a more privacy-preserving meeting scheduling process. The core hypothesis assumes that meeting participant selection should be governed by the ACLs of documents to be discussed, and that the meeting mode—onsite, online, or hybrid—should be determined based on participants’ joining locations to mitigate privacy risks. To address this, we formulate a scheduling problem that integrates these constraints and analyze its computational complexity. A prototype system was developed using widely adopted tools like Google Calendar and Google Drive to demonstrate the feasibility of the proposed approach. Experimental analysis confirms that the problem can be solved in polynomial time using constraint-checking and filtering algorithms, validating the practicality of the hypothesis. The study offers both theoretical and practical contributions: a novel, privacy-aware scheduling framework and an efficient algorithmic approach to enforce access-based participant selection and context-aware meeting modes.