满足ε—differentital privacy 2.4 Sensitivity, Privacy Budget (ε), and Determination of the Probability (p) of Randomization 为了量化传输比特串的LDP过程中的随机化概率p,我们可以使用随机聚合隐私保护顺序响应RAPPOR,这是google提出的LDP算法。RAPPOR是从离散数据字典中估计字符串的客户端分布。 灵敏度定义为单...
AMOUE: Adaptive modified optimized unary encoding method for local differential privacy data preservation Deep learning has gained popularity recently, and privacy concerns have increased simultaneously. Adversaries gain unauthorized access to the private train... T Gao,H Fu,S Wang,... - 《Computers ...
Privacy-preservingRecommender systemCondensed local differential privacyDeep neural networkFactorization machineRecommender systems aim at predicting users' future behaviors by learning the users' personal information and historical behaviors. Unfortunately, training by the user's raw data will inevitably cause ...
These three classical models were chosen since they are advanced deep learning-based architectures that have been developed for video prediction tasks. They operate on the principle of RNNs, which are specifically designed for modeling sequential data. PredRNN: PredRNN is an RNN architecture tailored ...
Local differential privacy has emerged as a well-suited technique for systems that aggregate sensitive user information. Its popularity can also be attributed to the fact that it can be achieved through a wide selection of privatizing mechanisms. These mechanisms can be selected for specific requiremen...
Federated latent Dirichlet allocation: a local differential privacy based framework. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2020. 34: 6283–6290 Yang W S, Zhang Y H, Ye K J, et al. FFD: a federated learning based method for credit card fraud detection. In: ...
The experimental results confirm the effectiveness of our model to obtain better detection accuracies than benchmarks in four Non-IID scenarios by keeping data privacy for secure computing in fabric IIoT.1 Introduction Deep learning (DL) techniques are increasingly deployed in the Internet of ...
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Gunduz, A. et al. Differential roles of high gamma and local motor potentials for movement preparation and execution.Brain–Comput. Interfaces3, 88–102 (2016). Article Li, G. et al. Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic rec...
Here we show that a random forest machine learning approach, which incorporates the 3D-shape of DNA, enhances binding prediction for all 216 tested Arabidopsis thaliana transcription factors and improves the resolution of differential binding by transcription factor family members which share the same ...