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Rights protection for discrete numeric streams

机译：离散数字流的权限保护

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摘要

Today's world of increasingly dynamic environments naturally results in more and more data being available as fast streams. Applications such as stock market analysis, environmental sensing, Web clicks, and intrusion detection are just a few of the examples where valuable data is streamed. Often, streaming information is offered on the basis of a nonexclusive, single-use customer license. One major concern, especially given the digital nature of the valuable stream, is the ability to easily record and potentially "replay" parts of it in the future. If there is value associated with such future replays, it could constitute enough incentive for a malicious customer (Mallory) to record and duplicate data segments, subsequently reselling them for profit. Being able to protect against such infringements becomes a necessity. In this work, we introduce the issue of rights protection for discrete streaming data through watermarking. This is a novel problem with many associated challenges including: operating in a finite window, single-pass, (possibly) high-speed streaming model, and surviving natural domain specific transforms and attacks (e.g., extreme sparse sampling and summarizations), while at the same time keeping data alterations within allowable bounds. We propose a solution and analyze its resilience to various types of attacks as well as some of the important expected domain-specific transforms, such as sampling and summarization. We implement a proof of concept software (wms.*) and perform experiments on real sensor data from the NASA Infrared Telescope Facility at the University of Hawaii, to assess encoding resilience levels in practice. Our solution proves to be well suited for this new domain. For example, we can recover an over 97 percent confidence watermark from a highly down-sampled (e.g., less than 8 percent) stream or survive stream summarization (e.g., 20 percent) and random alteration attacks with very high confidence levels, often above 99 percent.
机译：当今世界的环境越来越动态，自然会导致越来越多的数据以快速流的形式可用。股票市场分析，环境感知，Web点击和入侵检测等应用程序只是流传输有价值数据的一些示例。通常，流信息是基于非排他的，一次性使用的客户许可证提供的。一个主要的关注点，尤其是考虑到有价值的流的数字性质，是在将来轻松记录和潜在“重播”部分内容的能力。如果存在与此类将来重放相关的价值，则它可能足以诱使恶意客户（Mallory）记录和复制数据段，随后将其转售以获取利润。能够防止此类侵权成为必要。在这项工作中，我们介绍了通过水印对离散流数据进行权限保护的问题。这是一个新问题，面临许多相关挑战，包括：在有限的窗口中运行，单通道（可能）高速流模型以及在特定条件下幸存的自然域特定转换和攻击（例如，极端稀疏采样和汇总）同时将数据更改保持在允许范围内。我们提出了一种解决方案，并分析了其对各种类型的攻击以及某些重要的预期特定于域的转换（例如采样和汇总）的恢复能力。我们实施了概念验证软件（wms。*），并对来自夏威夷大学NASA红外望远镜设施的真实传感器数据进行了实验，以评估实践中的编码弹性水平。我们的解决方案被证明非常适合这个新领域。例如，我们可以从高度下采样（例如，小于8％）的流中恢复超过97％的置信水印，或者以非常高的置信度（通常高于99）从流摘要（例如20％）和随机变更攻击中幸存下来百分。

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