Resilient Rights Protection for Sensor

Today’s world of increasingly dynamic computing 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 to its consumer. Often, streaming information is offered on the basis of a non-exclusive, singleuse customer license. One major concern, especially given the digital nature of the valuable stream, is the ability to easily record and potentially “re-play” parts of it in the future. If there is value associated with such future re-plays, it could constitute enough incentive for a malicious customer (Mallory) to duplicate segments of such recorded data, subsequently re-selling them for profit. Being able to protect against such infringements becomes a necessity. In this paper we introduce the issue of rights protection for streaming data through watermarking. This is a novel problem with many associated challenges including: the inability to perform multiple-pass random accesses to the entire data set, the requirement to ∗Portions of this work were supported by Grants EIA9903545, IIS-0325345, IIS-0219560, IIS-0312357, IIS-9985019 and IIS-0242421 from the National Science Foundation, Contract N00014-02-1-0364 from the Office of Naval Research, by sponsors of the Center for Education and Research in Information Assurance and Security, and by Purdue Discovery Park’s e-enterprise Center. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 30th VLDB Conference, Toronto, Canada, 2004 be fast enough to keep up with the incoming stream rate, to survive instances of 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.*) for the proposed solution and perform experiments on real sensor data from the NASA Infrared Telescope Facility at the University of Hawaii, to assess these resilience levels in practice. Our method proves to be well suited for this new domain. For example, we can recover an over 97% confidence watermark from a sampled (e.g. less than 8%) stream. Similarly, our encoding ensures survival to stream summarization (e.g. 20%) and random alteration attacks with very high confidence levels, often above 99%.