Face recognition service has been used in many fields and brings much
convenience to people. However, once the user's facial data is transmitted to a
service provider, the user will lose control of his/her private data. In recent
years, there exist various security and privacy issues due to the leakage of
facial data. Although many privacy-preserving methods have been proposed, they
usually fail when they are not accessible to adversaries' strategies or
auxiliary data. Hence, in this paper, by fully considering two cases of
uploading facial images and facial features, which are very typical in face
recognition service systems, we proposed a data privacy minimization
transformation (PMT) method. This method can process the original facial data
based on the shallow model of authorized services to obtain the obfuscated
data. The obfuscated data can not only maintain satisfactory performance on
authorized models and restrict the performance on other unauthorized models but
also prevent original privacy data from leaking by AI methods and human visual
theft. Additionally, since a service provider may execute preprocessing
operations on the received data, we also propose an enhanced perturbation
method to improve the robustness of PMT. Besides, to authorize one facial image
to multiple service models simultaneously, a multiple restriction mechanism is
proposed to improve the scalability of PMT. Finally, we conduct extensive
experiments and evaluate the effectiveness of the proposed PMT in defending
against face reconstruction, data abuse, and face attribute estimation attacks.
These experimental results demonstrate that PMT performs well in preventing
facial data abuse and privacy leakage while maintaining face recognition
accuracy.

该研究探讨了面部识别服务中的数据隐私问题，并提出了一种数据隐私最小化转换方法，该方法能够处理原始面部数据以获取模糊化的数据，并通过增强扰动方法和多重限制机制来提高方法的鲁棒性和可扩展性。实验证明该方法能有效防止面部数据滥用和隐私泄露，同时保持面部识别的准确性。

面部数据最小化：浅层模型作为您的隐私过滤器

Facial Data Minimization: Shallow Model as Your Privacy Filter

Pre-training is an effective technique for ensuring robust performance on a
variety of machine learning tasks. It typically depends on large-scale crawled
corpora that can result in toxic or biased models. Such data can also be
problematic with respect to copyright, attribution, and privacy. Pre-training
with synthetic tasks and data is a promising way of alleviating such concerns
since no real-world information is ingested by the model. Our goal in this
paper is to understand what makes for a good pre-trained model when using
synthetic resources. We answer this question in the context of neural machine
translation by considering two novel approaches to translation model
pre-training. Our first approach studies the effect of pre-training on
obfuscated data derived from a parallel corpus by mapping words to a vocabulary
of 'nonsense' tokens. Our second approach explores the effect of pre-training
on procedurally generated synthetic parallel data that does not depend on any
real human language corpus. Our empirical evaluation on multiple language pairs
shows that, to a surprising degree, the benefits of pre-training can be
realized even with obfuscated or purely synthetic parallel data. In our
analysis, we consider the extent to which obfuscated and synthetic pre-training
techniques can be used to mitigate the issue of hallucinated model toxicity.

本研究旨在探讨何种情况下用合成资源进行预训练是有效的。我们将神经机器翻译作为情景背景，研究使用两种新方法进行翻译模型预训练的影响，并在多种语言对上进行实证评估，结果表明预训练的好处即使在混淆或纯合成数据的情况下也可以实现，并研究混淆和合成预训练技术能够减轻虚假模型毒性的程度。