As smart devices are becoming increasingly common in people’s daily lives, privacy and security concerns make data collection expensive and limited, which further hinder the development of data-driven tasks. This paper studies how to better conduct private data trading via a novel generator method rather than direct trading of raw data. This new method facilitates more convenient data transactions by generator, protects the privacy of data owners and is satisfactory in terms of privacy compensation and query pricing. In detail, we propose RARIEA, a market framework for tRading privAte data geneRators based on GAN under rényI diffErential privAcy, which involves data owners, a data broker, and data consumers. To start, the broker employs the GAN training generator to augment the data to relieve the data shortage, introducing noise into its training process to preserve the owners’ privacy. After that, the broker uses rényi differential privacy to quantify the privacy loss at the data item level during the GAN training process and compensates each owner according to their respective privacy policies. Finally, the data broker charges each of the data consumers for their queries, where the price is lower bounded by the total privacy compensation. We then evaluate the performance of RARIEA on classic data sets: MNIST, Fashion-MNIST, and CelebA. The analysis and simulation results reveal that the generator provided by RARIEA can not only meet the data consumers’ demand for quantity and quality but also protect the owners’ privacy. In addition, RARIEA not only allows finer control over data owner compensation, but also excels at controlling the data broker’s revenue to improve market efficiency while ensuring fairness, balance, and monotonicity of pricing.

With the development and popularization of cloud computing, more and more services and applications are migrated to cloud for the sake of low cost, high availability and excellent performance. As the foundation of cloud computing, virtualization technology integrates and reallocates the computing capability, storage and network resource fairly among virtual machines and provides a full-featured, isolated and reliable hardware environment for various operating systems. Owe to the virtualization technology, computing capability of virtual machines has achieved fantastic performance, some even achieve near native speed. However, low I/O performance is still a bottleneck, especially in I/O intensive applications. The leading causes include redundant data copy and frequent VM exits. Focusing on network I/O optimization, we design and implement virtsocket, a new network socket library in virtualization scenario which utilizes shared memory for data transmission. A ring buffer data structure stores I/O requests of virtual machine which is triggered to issue all requests with only one hypercall according to scheduler. Data referred in the I/O requests is read directly from virtual machine memory by host machine kernel module with interfaces provided by modified hypervisor. Experimental results show that throughput is improved by hundreds of times when compared with original virtualization scenario, and the latency also achieves a remarkable reduction. Both throughput and latency performance exceed existing para-virtualization solutions.