A privacy preserving graph neural networks framework by protecting user’s attributes

Zhou, Li; Wang, Jing; Fan, Dongmei; Zhang, Haifeng; Zhong, Kai

doi:10.1016/j.physa.2023.129187

Cited by 1 publication

(2 citation statements)

References 22 publications

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“…• LoDPGAN [7] : DPGAN is a leading-edge approach in GAN-based single-party data publishing. In LoDPGAN, each party locally applies DP to sanitize its local sensitive data.…”

Section: Evaluations 41 Datasets and Baselinementioning

confidence: 99%

“…Another line of work achieves private data sharing by exploiting secure multi-party computation (SMC) [6] Nevertheless, these approaches face the problem of high computation and communication overhead, and exhibit scalability issues as the number of participating parties increases. Some works are dedicated to private single-party data sharing [7][8] [9] . These approaches focus on the independent sanitization of each party's data and are applicable to address the above issue.…”

Section: Introductionmentioning

confidence: 99%

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Privacy-preserving multiparty power data sharing based on generative adversarial network

wang,

Liu,

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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With the rapid proliferation of smart grid technologies, a large amount of fine-grained power data records have been collected and stored by different parties (e.g., the power supply bureau). Pooling together the records held by the parties makes mining the data value, and therefore promises enhanced energy management and efficiency. Despite the benefits of sharing these data, it also raises concerns about data privacy and security. To this end, we present a novel approach for privacy-preserving cross-party power data sharing approach in light of the Generative Adversarial Network (named PowerGAN), which enables the involved parties to construct a shared dataset without compromising the privacy of these parties. In PowerGAN, a centralized curator is assigned a generator, while each party possesses a discriminator. The key idea of PowerGAN is to let data holders jointly train the generator held by the centralized server. In addition, to prevent the curator from inferring sensitive data about the parties, we designed a privacy-preserving RMSProp (Root Mean Square Propagation) optimizer. Furthermore, we design a dynamic noise perturbation method, which dynamically tunes the noise to further promote the utility of the final shared data. Through comprehensive privacy analysis, we show that our PowerGAN approach provides strict privacy protection. Evaluations of real-world datasets show the effectiveness of PowerGAN in addressing the privacy concerns associated with multi-party power data sharing.

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“…• LoDPGAN [7] : DPGAN is a leading-edge approach in GAN-based single-party data publishing. In LoDPGAN, each party locally applies DP to sanitize its local sensitive data.…”

Section: Evaluations 41 Datasets and Baselinementioning

confidence: 99%