Personal privacy protection issues has gradually caused widespread concern in society which will lead to economic and reputation losses, hinder network and E-commerce innovation or some other consequences if not handled properly. In this paper, we make use of the de-centralization, permanent and audibility of the blockchain to propose a blockchain-based personal privacy protection mechanism, which uses Online taxi-hailing as the application scenario. We not only provide the details of the blockchain custom transaction domain used by the scene, but also expound the information exchanging and blockchain auditing between passengers, Online taxi-hailing platform and drivers in Online taxi-hailing scene, providing a case model for the blockchain solution to personal privacy protection and a technical mechanism solution for further study of personal privacy protection issues.
In this paper, we study ARQ feedback in the context of two-way wireless communications. In particular, we consider two nodes which wish to exchange data over a frequency division duplex, time-varying wireless additive white Gaussian noise with Rayleigh fading, channel. In two-way scenarios, unlike the more well studied one-way data scenarios, the data and resources allocated to feedback and channel estimation may share the same link, leading to interesting tradeoffs. To analyze these, we present a two-way framework in which 1) training (estimation of channel state), 2) feedback (in the form of ARQ), and 3) data are taken into account, and share the same noisy fading channel. We obtain an expression which captures the tradeoffs between allocating resources for these three tasks on the overall throughput achievable in each direction, which we numerically evaluate. In particular, we obtain the optimal resource allocations corresponding to different channel conditions, SNR regimes, and receiver feedback protocols under fast and slow fading conditions.
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