# Interpretable Encrypted Searchable Neural Networks

## TL;DR

This paper introduces Interpretable Encrypted Searchable Neural Networks (IESNN), combining machine learning and encryption to enable efficient, dynamic, and interpretable search in cloud environments with reduced computational and communication costs.

## Contribution

The paper presents a novel IESNN framework that uses probabilistic learning and adversarial training for encrypted search, improving efficiency and interpretability over traditional searchable encryption methods.

## Key findings

- Query complexity reduced to approximately O(log N)
- Lower computational and communication overhead
- Enhanced adaptability with automatic weight updates

## Abstract

In cloud security, traditional searchable encryption (SE) requires high computation and communication overhead for dynamic search and update. The clever combination of machine learning (ML) and SE may be a new way to solve this problem. This paper proposes interpretable encrypted searchable neural networks (IESNN) to explore probabilistic query, balanced index tree construction and automatic weight update in an encrypted cloud environment. In IESNN, probabilistic learning is used to obtain search ranking for searchable index, and probabilistic query is performed based on ciphertext index, which reduces the computational complexity of query significantly. Compared to traditional SE, it is proposed that adversarial learning and automatic weight update in response to user's timely query of the latest data set without expensive communication overhead. The proposed IESNN performs better than the previous works, bringing the query complexity closer to $O(\log N)$ and introducing low overhead on computation and communication.