The rapid development of language models (LMs) brings unprecedented accessibility and usage for both models and users. On the one hand, powerful LMs, trained with massive textual data, achieve state-of-the-art performance over numerous downstream NLP tasks. On the other hand, more and more attention is paid to unrestricted model accesses that may bring malicious privacy risks of data leakage. To address these issues, many recent works propose privacy-preserving language models (PPLMs) with differential privacy (DP). Unfortunately, different DP implementations make it challenging for a fair comparison among existing PPLMs. In this paper, we present P-Bench, a multi-perspective privacy evaluation benchmark to empirically and intuitively quantify the privacy leakage of LMs. Instead of only protecting and measuring the privacy of protected data with DP parameters, P-Bench sheds light on the neglected inference data privacy during actual usage. P-Bench first clearly defines multi-faceted privacy objectives during private fine-tuning. Then, P-Bench constructs a unified pipeline to perform private fine-tuning. Lastly, P-Bench performs existing privacy attacks on LMs with pre-defined privacy objectives as the empirical evaluation results. The empirical attack results are used to fairly and intuitively evaluate the privacy leakage of various PPLMs. We conduct extensive experiments on three datasets of GLUE for mainstream LMs.

翻译：语言模型（LMs）的快速发展为模型与用户带来了前所未有的可获取性与使用便利性。一方面，基于海量文本数据训练的强大语言模型在众多下游自然语言处理任务中取得了最优性能；另一方面，不受限制的模型访问可能引发恶意数据泄露的隐私风险，正受到越来越多的关注。为解决这些问题，近期诸多研究提出了采用差分隐私（DP）的隐私保护语言模型（PPLMs）。然而，不同的DP实现方式使得现有PPLMs之间难以进行公平比较。本文提出P-Bench——一种多视角隐私评估基准，旨在经验性且直观地量化语言模型的隐私泄露程度。不同于仅通过DP参数来保护并衡量受保护数据的隐私，P-Bench关注实际使用过程中被忽视的推理数据隐私问题。P-Bench首先明确定义了私有微调过程中的多层面隐私目标，继而构建了统一化的私有微调流程，最后针对预设隐私目标对语言模型实施现有隐私攻击以获得经验性评估结果。这些经验攻击结果被用于公平且直观地评估各类PPLMs的隐私泄露程度。我们在GLUE基准的三个数据集上对主流语言模型开展了广泛实验。