  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.
