Over-parameterized neural language models (LMs) can memorize and recite long
sequences of training data. While such memorization is normally associated with
undesired properties such as overfitting and information leaking, our work
casts memorization as an unexplored capability of LMs. We propose the first
symmetric encryption algorithm with autoregressive language models (SELM). We
show that autoregressive LMs can encode arbitrary data into a compact
real-valued vector (i.e., encryption) and then losslessly decode the vector to
the original message (i.e., decryption) via random subspace optimization and
greedy decoding. While SELM is not amenable to conventional cryptanalysis, we
investigate its security through a novel empirical variant of the classic
IND-CPA (indistinguishability under chosen-plaintext attack) game. Our code and
datasets are available at this https URL

本文提出了第一个具有自回归语言模型的对称加密算法（SELM），证明了自回归模型可以通过随机子空间优化和贪婪解码将任意数据编码为紧凑的实数向量（即加密），然后无损地解码向量以获取原始信息（即解密）。同时，本文通过一种新的经验方法研究了 SEL​​M 的安全性，并提供了代码和数据集。