# Multi-owner Secure Encrypted Search Using Searching Adversarial Networks

## TL;DR

This paper introduces a novel multi-owner encrypted search method using searching adversarial networks, achieving high query precision, strong privacy, and efficient dynamic updates in cloud data environments.

## Contribution

It proposes a secure, efficient multi-keyword ranked search scheme leveraging searching adversarial networks for optimal privacy and precision in multi-owner encrypted cloud data.

## Key findings

- Query precision exceeds 95%
- Computational complexity approaches O(log N)
- Low overhead on computation, communication, and storage

## Abstract

Searchable symmetric encryption (SSE) for multi-owner model draws much attention as it enables data users to perform searches over encrypted cloud data outsourced by data owners. However, implementing secure and precise query, efficient search and flexible dynamic system maintenance at the same time in SSE remains a challenge. To address this, this paper proposes secure and efficient multi-keyword ranked search over encrypted cloud data for multi-owner model based on searching adversarial networks. We exploit searching adversarial networks to achieve optimal pseudo-keyword padding, and obtain the optimal game equilibrium for query precision and privacy protection strength. Maximum likelihood search balanced tree is generated by probabilistic learning, which achieves efficient search and brings the computational complexity close to $\mathcal{O}(\log N)$. In addition, we enable flexible dynamic system maintenance with balanced index forest that makes full use of distributed computing. Compared with previous works, our solution maintains query precision above 95% while ensuring adequate privacy protection, and introduces low overhead on computation, communication and storage.