Feel Safe to Take More Risks? Insecure Attachment Increases Consumer Risk-Taking Behavior

open access articl

Development of an Improved Cereal Stripping Harvester

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 9 (2007): Development of an Improved Cereal Stripping Harvester. Manuscript PM 07 009. Vol. IX. September, 2007

Membership Inference Attacks and Defenses in Neural Network Pruning

Neural network pruning has been an essential technique to reduce the computation and memory requirements for using deep neural networks for resource-constrained devices. Most existing research focuses primarily on balancing the sparsity and accuracy of a pruned neural network by strategically removing insignificant parameters and retraining the pruned model. Such efforts on reusing training samples pose serious privacy risks due to increased memorization, which, however, has not been investigated yet. In this paper, we conduct the first analysis of privacy risks in neural network pruning. Specifically, we investigate the impacts of neural network pruning on training data privacy, i.e., membership inference attacks. We first explore the impact of neural network pruning on prediction divergence, where the pruning process disproportionately affects the pruned model's behavior for members and non-members. Meanwhile, the influence of divergence even varies among different classes in a fine-grained manner. Enlighten by such divergence, we proposed a self-attention membership inference attack against the pruned neural networks. Extensive experiments are conducted to rigorously evaluate the privacy impacts of different pruning approaches, sparsity levels, and adversary knowledge. The proposed attack shows the higher attack performance on the pruned models when compared with eight existing membership inference attacks. In addition, we propose a new defense mechanism to protect the pruning process by mitigating the prediction divergence based on KL-divergence distance, whose effectiveness has been experimentally demonstrated to effectively mitigate the privacy risks while maintaining the sparsity and accuracy of the pruned models.Comment: This paper has been accepted to USENIX Security Symposium 2022. This is an extended version with more experimental result

Spatial Autoregressive Model of Commodity Housing Price and Empirical Research

AbstractBased on spatial econometric model, the article selects the panel data of eight cities around Beijing from 1998 to 2009. It tests whether there is spatial dependence among cities on commodity housing price. The main influence factors of the housing price are further analyzed. Finally, the housing prices of the 8 cities are tested by the Granger. The results show that there is significant spatial dependence between cities on the housing price. The factors that affect the commodity housing price include spatial factor, urban residents’ disposable income factor, population factor, land price factor and living space factor. Granger test shows that there is one-way relationship of Beijing to Tianjin, Shijiazhuang, Shenyang, Changchun and Jinan. The conclusions establish the theoretical foundation for the formation mechanism of the housing price and offer references for engineering project pricing in real estate and government macro regulation