Proactive Privacy-preserving Learning for Retrieval
Authors: Peng-Fei Zhang, Zi Huang, Xin-Shun Xu3369-3376
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three widely-used realistic datasets prove the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Peng-Fei Zhang,1 Zi Huang, 1 Xin-Shun Xu 2 1 School of Information Technology & Electrical Engineering, University of Queensland, Brisbane, Australia 2 School of Software, Shandong University, Jinan, China |
| Pseudocode | Yes | Algorithm 1 Proactive Privacy-preserving Learning Input: Training data: X, mini-batch size: m, learning rate: σg, σf, hash code length: c, iteration times: t, epoch times: e = [t/m] Output: Adversarial generator: G( ; ϑg) Procedure: Randomly initialize ϑg, ϑf for i = 1 : t do for j = 1 : e do 1. xi X // Construct a mini-batch 2. ϑg ϑg σg θg(Jo Jp) // Update ϑg by SGD 3. ϑf ϑf σf θf Jp // Update ϑf by SGD end for end for Return: ϑg |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | To validate the effectiveness of the proposed PPL, we conduct extensive experiments on three widelyused benchmark datasets, i.e., CIFAR-10 (Krizhevsky et al. 2009), Image Net (Deng et al. 2009), and Fashion MNIST (Xiao, Rasul, and Vollgraf 2017). |
| Dataset Splits | No | The paper specifies 10,000 data points randomly selected for training and some images for testing (e.g., 1000 for CIFAR-10, 1300 for ImageNet, 10,000 for Fashion MNIST test set), but does not explicitly mention a separate 'validation' split. |
| Hardware Specification | Yes | The experiments are implemented with Pytorch on a workstation (with Intel XEON E5-2650 v3 @ 2.60GHz CPU, NVIDIA 1080Ti GPU). |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not specify its version or other software dependencies with version numbers. |
| Experiment Setup | Yes | Implementation Details For experiments on CIFAR-10 and Image Net, Res Net50 (He et al. 2016) network is adopted as the backbone of the surrogate retrieval model... The parameters are set as follows. we set α = γ = λ = 1, β = 10. The perturbation constraints are selected as the l norm with ϵ as 0.1,0.032,0.3 for CIFAR-10, Image Net and Fashion MNIST, respectively. The generated images would be projected into [0, 1] space. The learning rates are 10 4 and 10 3 for the generator and the retrieval model. The iterations are respectively fixed as 500,500 and 1000. We fix the batch sizes as 32,32,512. |