Skeleton-based motion visualization is a rising field in computer vision,
especially in the case of virtual reality (VR). With further advancements in
human-pose estimation and skeleton extracting sensors, more and more
applications that utilize skeleton data have come about. These skeletons may
appear to be anonymous but they contain embedded personally identifiable
information (PII). In this paper we present a new anonymization technique that
is based on motion retargeting, utilizing adversary classifiers to further
remove PII embedded in the skeleton. Motion retargeting is effective in
anonymization as it transfers the movement of the user onto the a dummy
skeleton. In doing so, any PII linked to the skeleton will be based on the
dummy skeleton instead of the user we are protecting. We propose a
Privacy-centric Deep Motion Retargeting model (PMR) which aims to further clear
the retargeted skeleton of PII through adversarial learning. In our
experiments, PMR achieves motion retargeting utility performance on par with
state of the art models while also reducing the performance of privacy attacks.

基于运动重定向的隐私保护深度动作重定向模型（PMR）通过对抗学习，帮助实现与最先进模型相当的运动重定向效果，并降低隐私攻击的成功率。

骨骼匿名化的对抗引导运动重定向

Adversary-Guided Motion Retargeting for Skeleton Anonymization

MRI scans provide valuable medical information, however they also contain
sensitive and personally identifiable information (PII) that needs to be
protected. Whereas MRI metadata is easily sanitized, MRI image data is a
privacy risk because it contains information to render highly-realistic 3D
visualizations of a patient's head, enabling malicious actors to possibly
identify the subject by cross-referencing a database. Data anonymization and
de-identification is concerned with ensuring the privacy and confidentiality of
individuals' personal information. Traditional MRI de-identification methods
remove privacy-sensitive parts (e.g. eyes, nose etc.) from a given scan. This
comes at the expense of introducing a domain shift that can throw off
downstream analyses. Recently, a GAN-based approach was proposed to de-identify
a patient's scan by remodeling it (e.g. changing the face) rather than by
removing parts. In this work, we propose CP-MAE, a model that de-identifies the
face using masked autoencoders and that outperforms all previous approaches in
terms of downstream task performance as well as de-identification. With our
method we are able to synthesize scans of resolution up to $256^3$ (previously
128 cubic) which constitutes an eight-fold increase in the number of voxels.
Using our construction we were able to design a system that exhibits a highly
robust training stage, making it easy to fit the network on novel data.

利用 CP-MAE 提出的模型，通过遮蔽自动编码器对人脸进行去识别，表现出卓越的下游任务性能和去识别效果，同时能够合成高达 $256^3$ 分辨率的扫描，大大增加了体素的数量。

增强 MRI 扫描的隐私保护的 3D 掩蔽自编码器

3D Masked Autoencoders for Enhanced Privacy in MRI Scans

Trustera, the first functional system that redacts personally identifiable
information (PII) in real-time spoken conversations to remove agents' need to
hear sensitive information while preserving the naturalness of live
customer-agent conversations. As opposed to post-call redaction, audio masking
starts as soon as the customer begins speaking to a PII entity. This
significantly reduces the risk of PII being intercepted or stored in insecure
data storage. Trustera's architecture consists of a pipeline of automatic
speech recognition, natural language understanding, and a live audio redactor
module. The system's goal is three-fold: redact entities that are PII, mask the
audio that goes to the agent, and at the same time capture the entity, so that
the captured PII can be used for a payment transaction or caller
identification. Trustera is currently being used by thousands of agents to
secure customers' sensitive information.

Trustera 是一种可实时红 acting PII 的功能性系统，用于删除代理人需要在保存现场客户代理人对话的自然性时听取敏感信息的需求。

Trustera：一种实时对话去隐私化系统

Trustera: A Live Conversation Redaction System

Honeywords are fictitious passwords inserted into databases in order to
identify password breaches. The major difficulty is how to produce honeywords
that are difficult to distinguish from real passwords. Although the generation
of honeywords has been widely investigated in the past, the majority of
existing research assumes attackers have no knowledge of the users. These
honeyword generating techniques (HGTs) may utterly fail if attackers exploit
users' personally identifiable information (PII) and the real passwords include
users' PII. In this paper, we propose to build a more secure and trustworthy
authentication system that employs off-the-shelf pre-trained language models
which require no further training on real passwords to produce honeywords while
retaining the PII of the associated real password, therefore significantly
raising the bar for attackers.
We conducted a pilot experiment in which individuals are asked to distinguish
between authentic passwords and honeywords when the username is provided for
GPT-3 and a tweaking technique. Results show that it is extremely difficult to
distinguish the real passwords from the artifical ones for both techniques. We
speculate that a larger sample size could reveal a significant difference
between the two HGT techniques, favouring our proposed approach.

使用预训练语言模型构建的身份验证系统，生成包含个人身份信息的 honeywords，难以被攻击者区分，从而提高密码保护的安全性。