Generative models are subject to overfitting and thus may potentially leak
sensitive information from the training data. In this work. we investigate the
privacy risks that can potentially arise from the use of generative adversarial
networks (GANs) for creating tabular synthetic datasets. For the purpose, we
analyse the effects of re-identification attacks on synthetic data, i.e.,
attacks which aim at selecting samples that are predicted to correspond to
memorised training samples based on their proximity to the nearest synthetic
records. We thus consider multiple settings where different attackers might
have different access levels or knowledge of the generative model and
predictive, and assess which information is potentially most useful for
launching more successful re-identification attacks. In doing so we also
consider the situation for which re-identification attacks are formulated as
reconstruction attacks, i.e., the situation where an attacker uses evolutionary
multi-objective optimisation for perturbing synthetic samples closer to the
training space. The results indicate that attackers can indeed pose major
privacy risks by selecting synthetic samples that are likely representative of
memorised training samples. In addition, we notice that privacy threats
considerably increase when the attacker either has knowledge or has black-box
access to the generative models. We also find that reconstruction attacks
through multi-objective optimisation even increase the risk of identifying
confidential samples.

我们研究了生成对抗网络（GANs）用于创建表格合成数据集可能导致的隐私风险，发现恶意攻击者可以通过选择与训练样本最接近的合成样本对隐私构成重大威胁，并且当攻击者具有对生成模型的知识或黑盒访问时，隐私威胁显著增加，而使用多目标优化的重构攻击甚至会增加识别机密样本的风险。

表格 GANs 的隐私再识别攻击

Privacy Re-identification Attacks on Tabular GANs

As people's daily life becomes increasingly inseparable from various mobile
electronic devices, relevant service application platforms and network
operators can collect numerous individual information easily. When releasing
these data for scientific research or commercial purposes, users' privacy will
be in danger, especially in the publication of spatiotemporal trajectory
datasets. Therefore, to avoid the leakage of users' privacy, it is necessary to
anonymize the data before they are released. However, more than simply removing
the unique identifiers of individuals is needed to protect the trajectory
privacy, because some attackers may infer the identity of users by the
connection with other databases. Much work has been devoted to merging multiple
trajectories to avoid re-identification, but these solutions always require
sacrificing data quality to achieve the anonymity requirement. In order to
provide sufficient privacy protection for users' trajectory datasets, this
paper develops a study on trajectory privacy against re-identification attacks,
proposing a trajectory K-anonymity model based on Point Density and Partition
(KPDP). Our approach improves the existing trajectory generalization
anonymization techniques regarding trajectory set partition preprocessing and
trajectory clustering algorithms. It successfully resists re-identification
attacks and reduces the data utility loss of the k-anonymized dataset. A series
of experiments on a real-world dataset show that the proposed model has
significant advantages in terms of higher data utility and shorter algorithm
execution time than other existing techniques.

为了保护用户的轨迹数据隐私，本文提出了一种基于点密度和分区的轨迹 K - 匿名模型 (KPDP)，它在轨迹集分区前处理和轨迹聚类算法方面改进了现有的轨迹泛化匿名化技术，成功抵制了重新识别攻击，并减少了 k - 匿名数据集的数据效用损失。实验结果表明，相比其他现有技术，该模型在数据效用和算法执行时间方面具有明显优势。