Privacy-Preserving Intelligent Collaboration Platform with Searchable Encryption and Client Side AI: A Survey of Privacy-Preserving Methods for Secure Collaboration

Abstract: The rapid growth of cloud-based collaboration platforms has introduced significant privacy concerns, as traditional systems expose sensitive user data to servers and third-party services during storage, indexing, and AI- based processing. Existing platforms routinely access user files for operational purposes, creating substantial risks for confidential information. This paper surveys recent privacy-preserving techniques including searchable encryption, end-to-end encryption, federated learning, and on-device AI inference, analyzing their strengths and limitations in the context of secure collaboration. Based on identified research gaps, we propose a unified Privacy-Preserving Intelligent Collaboration Platform that integrates client-side AES encryption, searchable symmetric encryption with secure tokens, RSA-based key sharing, and locally-executed AI inference to enable secure file storage, encrypted search, and intelligent features without exposing user data to servers.

Keywords: Privacy-Preserving Systems, Searchable Encryption, End-to-End Encryption, Client-Side AI, Federated Learning, Cloud Security, Secure Collaboration, AES Encryption, RSA Key Exchange, DevSecOps