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Rights protection for discrete numeric streams

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3 Author(s)
Sion, R. ; Dept. of Comput. Sci., Stony Brook Univ., NY, USA ; Atallah, M.J. ; Prabhaka, S.

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.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:18 ,  Issue: 5 )