LNCS 2950 - Aspects of Molecular Computing (Frontmatter Pages)

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184 Ashish Gehani, Thomas LaBean, and John Reif Separation Error 0.05 0.1 0.15 0.2 0.25 Iterations 3 Distracters 12500 100000 337500 800000 1562500 Messages 217 184 155 129 107 6 Distracters 2 100 1139 6400 24414 Messages 47 34 24 17 11 9 Distracters 0 0 4 51 381 Messages 10 6 4 2 1 4.8 Improving DNA Steganography We can improve a DNA steganography system by reducing the difference between the plaintext and distracter strands. This can be done by making the distracters similar to the plaintext by creating them using random assembly of elements from the dictionary D. Alternatively, DNA-SIEVE can be employed on a set of random distracters to shape the population into one whose distribution matches that of the plaintext. We note, however, that if the relative entropy [9] between the plaintext and the distracter strand populations is large enough, DNA-SIEVE can be employed as previously described. An attacker can use a larger dictionary, which provides a better model of the plaintext source, to increase the relative entropy re-enabling the use of DNA-SIEVE. If the M plaintext strands are tagged with a sequence that allows them to be extracted, then they may be recognized by the high frequency of the tag sequence in the population. To guard against this, N sets of M strands each can be mixed in. This results in a system that uses V = O(MN) volume. To prevent a brute force attack, N must be large, potentially detracting from the practicality of using using the DNA steganographic system. The other approach to reduce the distinguishability of the plaintext from the distracters is to make the former mimic the latter. By compressing the plaintext with a lossless algorithm, such as Lempel-Ziv [51], the relative entropy of the message and the distracter populations can be reduced. If the plaintext is derived from an electronic source, it can be compressed in a preprocessing step. If the source is natural DNA, it can be recoded using a substitution method similar to the one described in Section 2. However, the security of such a recoding remains unknown. In the case of natural DNA, for equivalent effort, DNA cryptography offers a more secure alternative to DNA steganography. 5 Conclusion This paper presented an initial investigation into the use of DNA-based information security. We discussed two classes of methods: (i) DNA cyptography methods based on DNA one-time-pads, and (ii) DNA steganography methods. Our DNA substitution and XOR methods are based on one-time-pads, which are in principle unbreakable. We described our implementation of DNA cyptography with 2D input/output. We showed that a class of DNA steganography methods
DNA-based Cryptography 185 offer limited security and can be broken with a reasonable assumption about the entropy of the plaintext messages. We considered modified DNA steganographic systems with improved security. Steganographic techniques rest on the assumption that the adversary is unaware of the existence of the data. When this does not hold, DNA cryptography must be relied upon. Acknowledgments Work supported by Grants NSF/DARPA CCR-9725021, CCR-96-33567, NSF IRI-9619647, ARO contract DAAH-04-96-1-0448, and ONR contractN00014-99- 1-0406. A preliminary version appears in DIMACS DNA Based Computers V, American Mathematical Society, 2000. References 1. L.M. Adleman, Molecular computation of solutions to combinatorial problems, Science, 266 (1994), 1021–1024. 2. W.M. Barnes, PCR amplification of up to 35-kb DNA with high fidelity and high yield from bacteriophage templates, Proc. Natl. Acad. Sci., 91 (1994), 2216–2220. 3. E.B. Baum, Building an associative memory vastly larger than the brain, Science, 268 (1995), 583–585. 4. T. Bell, I.H. Witten, and J.G. Cleary, Modeling for Text Compression, ACM Computing Surveys, 21, 4 (1989), 557–592. 5. D. Boneh, C. Dunworth, and R.J. Lipton, Breaking DES Using a Molecular Computer, DNA Based Computers (E.B. Baum and R.J. Lipton, eds.), American Mathematical Society, 1996, DIMACS: Series in Discrete Mathematics and Theoretical Computer Science. 6. A.P. Blanchard, R.J. Kaiser, and L E. Hood, High-density oligonucleotide arrays, Biosens. Bioelec., 11 (1996), 687–690. 7. D. Boneh, C. Dunworth, R.J. Lipton, and J. Sgall, Making DNA computers error resistant, DNA based computer II (Editors: L. Landwaber, E. Baum) DIMACS series in Discrete Math. and Theoretical Comp. Sci. 44 (1999). 8. M. Chee, R. Yang, E. Hubbell, A. Berno, X.C. Huang, D. Stern, J. Winkler, D.J. Lockhart, M.S. Morris, S.P.A. Fodor, Accessing genetic information with high-density DNA arrays, Science, 274 (1996), 610–614. 9. Th.M. Cover and J.A. Thomas, Elements of Information Theory, New York, NY, USA, John Wiley & Sons, 1991. 10. R. Deaton, R.C. Murphy, M. Garzon, D.R. Franceschetti, and S.E. Stevens, Jr., Good Encodings for DNA-based Solutions to Combinatorial Problems, DNA based computer II (Editors:L.Landwaber,E.Baum)DIMACSseriesinDiscrete Math. and Theoretical Comp. Sci. 44 (1999). Proceedings of the Second Annual Meeting on DNA Based Computers, 1996, American Mathematical Society, DIMACS: Series in Discrete Mathematics and Theoretical Computer Science, 1052–1798. 11. R. Deaton, R.C. Murphy, M. Garzon, D.R. Franceschetti, and S.E.Stevens, Jr., Reliability and efficiency of a DNA-based computation, Phys. Rev.Lett., 80 (1998), 417–420.
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Lecture Notes in Computer Science 2
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Nata˘sa Jonoska Gheorghe Păun Grz
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Thomas J. Head
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VIII Preface portant to keep in min
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X Table of Contents Formal Properti
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Solving Graph Problems by P Systems
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where Solving Graph Problems by P S
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Writing Information into DNA Masano
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Writing Information into DNA 25 Ham
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Writing Information into DNA 27 Fig
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Writing Information into DNA 29 Dea
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5 Results 5.1 DNA Code for the Engl
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Writing Information into DNA 33 3.
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Writing Information into DNA 35 101
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Balance Machines: Computing = Balan
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+ .. . + Balance Machines: Computin
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x Balance Machines: Computing = Bal
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1 Balance Machines: Computing = Bal
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Eilenberg P Systems with Symbol-Obj
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2 Definitions Definition 1. A strea
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5 Conclusions Eilenberg P Systems w
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Molecular Tiling and DNA Self-assem
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3 Molecular Self-assembly Processes
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8 Hierarchical Tiling Molecular Til
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References Molecular Tiling and DNA
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On Some Classes of Splicing Languag
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ˆxa ˆby ✬ ✩✬ ✩ a b x y
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The Power of Networks of Watson-Cri
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Fixed Point Approach to Commutation
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Here the last one holds if and only
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� � � � � Fixed Point App
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Page 143 and 144: Remarks on Relativisations and DNA
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Page 157 and 158: Splicing Test Tube Systems 147 (c)
Page 159 and 160: I0 Ai i Splicing Test Tube Systems
Page 161 and 162: Splicing Test Tube Systems 151 4. J
Page 163 and 164: Digital Information Encoding on DNA
Page 177 and 178: DNA-based Cryptography Ashish Gehan
Page 179 and 180: DNA-based Cryptography 169 concern.
Page 181 and 182: DNA-based Cryptography 171 The one-
Page 183 and 184: DNA-based Cryptography 173 of DNA t
Page 185 and 186: DNA-based Cryptography 175 Fig. 3.
Page 187 and 188: DNA-based Cryptography 177 the comp
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Page 191 and 192: DNA-based Cryptography 181 4.4 DNA-
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Page 197 and 198: DNA-based Cryptography 187 30. C. M
Page 199 and 200: Splicing to the Limit Elizabeth Goo
Page 201 and 202: Splicing to the Limit 191 molecular
Page 203 and 204: Splicing to the Limit 193 Discussio
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Page 211 and 212: 6 Conclusion Splicing to the Limit
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Page 227 and 228: n-Insertion on Languages 217 Theore
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Transducers with Programmable Input
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start tile Transducers with Program
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Transducers with Programmable Input
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Methods for Constructing Coded DNA
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u u Methods for Constructing Coded
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On the Universality of P Systems wi
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An Algorithm for Testing Structure
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Definition 1. Define the relation
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280 Manfred Kudlek Definition 5. Co
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On Languages of Cyclic Words 283 Th
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On Languages of Cyclic Words 285 No
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On Languages of Cyclic Words 287 Th
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A DNA Algorithm for the Hamiltonian
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Formal Languages Arising from Gene
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A Proof of Regularity for Finite Sp
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--- ◦ ❄ ⊗ γ ✲ A Proof of R
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The Duality of Patterning in Molecu
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The Duality of Patterning in Molecu
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Membrane Computing: Some Non-standa
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where: Membrane Computing: Some Non
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where: Membrane Computing: Some Non
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The P Versus NP Problem Through Cel
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Realizing Switching Functions Using
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Realizing Switching Functions Using
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AND Gate Realizing Switching Functi
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NOR Gate Realizing Switching Functi
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Plasmids to Solve #3SAT Rani Siromo
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Plasmids to Solve #3SAT 363 A comme
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Plasmids to Solve #3SAT 365 from th
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Communicating Distributed H Systems
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Proof Communicating Distributed H S
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Books: Publications by Thomas J. He
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Publications by Thomas J. Head 387
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Publications by Thomas J. Head 389
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