使用卷积码的私有流媒体
Private Streaming with Convolutional Codes
摘要：
近年来，基于信息论的编码存储系统私有信息检索（PIR）得到了广泛的关注，并提出了一种通用的星积PIR方案。在本文中，星积方案被采用，适当的修改，私人的情况下（例如，视频）流。假设要流传输的文件以编码形式存储在~ $n$服务器上，并且通过卷积码执行流传输。星产品计划定义为这种特殊的情况下，和各种属性进行了分析，两个信道模型相关的离散和拜占庭服务器，无论是在基线的情况下，以及与勾结服务器。所实现的PIR率为给定的模型推导出，并在容量已知的情况下，第一个模型被证明是渐近最优的，当在一个文件中的条带的数量是大的。在这项工作中引入的第二个方案被证明是相当于块卷积码的PIR设置。对于拜占庭服务器模型，它表现出优于平凡的计划下载所需的文件的条带分别没有内存。
英文摘要：
Recently, information-theoretic private information retrieval (PIR) from coded storage systems has gained a lot of attention, and a general star product PIR scheme was proposed. In this paper, the star product scheme is adopted, with appropriate modifications, to the case of private (e.g., video) streaming. It is assumed that the files to be streamed are stored on~$n$ servers in a coded form, and the streaming is carried out via a convolutional code. The star product scheme is defined for this special case, and various properties are analyzed for two channel models related to straggling and Byzantine servers, both in the baseline case as well as with colluding servers. The achieved PIR rates for the given models are derived and, for the cases where the capacity is known, the first model is shown to be asymptotically optimal, when the number of stripes in a file is large. The second scheme introduced in this work is shown to be the equivalent of block convolutional codes in the PIR setting. For the Byzantine server model, it is shown to outperform the trivial scheme of downloading stripes of the desired file separately without memory.
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