This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Rights Protection for Discrete Numeric Streams
May 2006 (vol. 18 no. 5)
pp. 699-714
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.

[1] R. Agrawal, P.J. Haas, and J. Kiernan, “Watermarking Relational Data: Framework, Algorithms and Analysis,” The VLDB J., vol. 12, no. 2, pp. 157-169, 2003.
[2] M. Arnold, S.D. Wolthusen, and M. Schmucker, Techniques and Applications of Digital Watermarking and Content Protection. Artech House Publishers, 2003.
[3] M.J. Atallah and S.S. Wagstaff Jr., “Watermarking with Quadratic Residues,” Proc. IS-T/SPIE Conf. Security and Watermarking of Multimedia Contents, SPIE, vol. 3657, pp. 283-288, 1999.
[4] B. Babcock, S. Babu, M. Datar, and R. Motwani, “Models and Issues in Data Stream Systems,” Proc. ACM Symp. Principles of Database Systems (PODS), pp. 1-16, 2002.
[5] M. Barni and F. Bartolini, Watermarking Systems Engineering: Enabling Digital Assets Security and Other Applications. Marcel Dekker, 2004.
[6] D. Carney, U. Cetintemel, M. Cherniack, C. Convey, S. Lee, G. Seidman, N. Stonebraker, M. Tatbul, and S. Zdonik, “Monitoring Streams— A New Class of Data Management Applications,” Proc. Int'l Conf. Very Large Data Bases (VLDB), 2002.
[7] S. Chandrasekaran and M.J. Franklin, “Streaming Queries over Streaming Data,” Proc. Int'l Conf. Very Large Data Bases (VLDB), pp. 203-214, 2002.
[8] I. Cox, J. Bloom, and M. Miller, “Digital Watermarking,” Digital Watermarking, Morgan Kaufmann, 2001.
[9] M. Datar, A. Gionis, P. Indyk, and R. Motwani, “Maintaining Stream Statistics over Sliding Windows,” Proc. ACM-SIAM Symp. Discrete Algorithms, pp. 635-644, 2002.
[10] J. Eggers and B. Girod, Informed Watermarking. Kluwer Academic Publishers, 2002.
[11] N.F. Johnson, Z. Duric, and S. Jajodia, Information Hiding: Steganography and Watermarking-Attacks and Countermeasures. Kluwer Academic Publishers, 2001.
[12] J. Kang, J.F. Naughton, and S.D. Viglas, “Evaluating Window Joins over Unbounded Streams,” Proc. Int'l Conf. Design Eng., 2003.
[13] Information Hiding Techniques for Steganography and Digital Watermarking, S. Katzenbeisser and F. Petitcolas, eds., Artech House, 2001.
[14] J. Kiernan and R. Agrawal, “Watermarking Relational Databases,” Proc. 28th Int'l Conf. Very Large Databases (VLDB), 2002.
[15] F. Korn, S. Muthukrishnan, and D. Srivastava, “Reverse Nearest Neighbor Aggregates over Streams,” Proc. Int'l Conf. Very Large Data Bases (VLDB), 2002.
[16] Y. Li, V. Swarup, and S. Jajodia, “A Robust Watermarking Scheme for Relational Data,” Proc. Workshop Information Technology and Systems (WITS), pp. 195-200, 2003.
[17] C.-S. Lu, Multimedia Security: Steganography and Digital Watermarking Techniques for Protection of Intellectual Property, Idea Group Publishing, 2004.
[18] NASA, The Hawaii Univ. Infrared Telescope Facility, http:/irtfweb.ifa.hawaii.edu, 2004.
[19] B. Schneier, Applied Cryptography: Protocols, Algorithms and Source Code in C. Wiley & Sons, 1996.
[20] H.T. Sencar, M. Ramkumar, and A.N. Akansu, Data Hiding Fundamentals and Applications: Content Security in Digital Multimedia. Elsevier Science and Technology Books, 2004.
[21] R. Sion, “Proving Ownership over Categorical Data,” Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2004.
[22] R. Sion, M. Atallah, and S. Prabhakar, “Rights Protection for Relational Data,” Proc. ACM Special Interest Group on Management of Data Conf. (SIGMOD), 2003.
[23] R. Sion, M. Atallah, and S. Prabhakar, “Relational Data Rights Protection through Watermarking,” IEEE Trans. Knowledge and Data Eng., vol. 16, no. 6, June 2004.
[24] R. Sion, M. Atallah, and S. Prabhakar, “Resilient Rights Protection for Sensor Streams,” Proc. Very Large Databases Conf. (VLDB), 2004.

Index Terms:
Rights protection, discrete streams, sensor networks, watermarking.
Citation:
Radu Sion, Mikhail Atallah, Sunil Prabhakar, "Rights Protection for Discrete Numeric Streams," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 5, pp. 699-714, May 2006, doi:10.1109/TKDE.2006.82
Usage of this product signifies your acceptance of the Terms of Use.