Synthetic Steganographic Series and Finance

Philip C. Ritchey; Vernon J. Rego

doi:10.17648/enig.v1i1.15

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Philip C. Ritchey
Department of Computer Science, Purdue University, West Lafayette, Indiana, USA

Philip C. Ritchey received the B.S. degree in computer engineering in 2008 from Texas A&M University. He is currently a PhD candidate in Com- puter Science at Purdue University, where he is also a member of the Center for Education and Research in Information Assurance and Security (CERIAS) and the Center for Science of Information (CSoI). His research interests include information hiding, censorship-resistant technologies, privacy protection, and multi-agent systems.

Vernon J. Rego
Department of Computer Science, Purdue University, West Lafayette, Indiana, USA

Vernon J. Rego received the M.Sc. degree in mathematics in 1979 from the Birla Institute of Technology and Science, and the M.S. and Ph.D. degrees in computer science in 1982 and 1985, respectively, from Michigan State University. He is currently Professor of Computer Science at Purdue University, and a faculty member of the Center for Science of Information (CSoI). His research interests include software systems for high-performance dis- tributed computation, parallel stochastic simulation, and software engineering.

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Synthetic Steganographic Series and Finance

Philip C. Ritchey, Vernon J. Rego

Abstract

Whenever agent Alice is in a position to send a non- trivially generated data series to agent Bob, Alice is in a position to signal Bob in various ways, including an ability to hide a message for Bob in the series. Such data series may be generated in a variety of situations, including stock prices in any time scale, patterns, textures, tilings, and virtually any processes in which seemingly random values may appear. We focus on stock-price series and demonstrate each step of the data generation and hiding process in detail, using authentic data from the underlying application to generate synthetic data that cannot be compared to data that will naturally be absent in real applications.

Keywords

Steganography; TES; Financial; Time Series

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References

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DOI: https://doi.org/10.17648/enig.v1i1.15