Mitigation: Tamper-Mitigating Routing Fabrics



Intuitively, a biochip designed for a single function is physically unable to realize an undesired operation. On the other hand, a reconfigurable biochip could be configured in a way that is not only undesirable, but potentially destructive. This chapter introduces the concept of a tamper-mitigating routing fabric, which is a reconfigurable biochip technology that is designed in such a way that the effects of control signal tampering are probabilistically less severe or controlled. Both routing fabric analysis and synthesis techniques are developed and then applied to a DNA barcoding application.


Flow-based microfluidic biochip Routing fabric Transposer Security Tampering Mitigation Analysis Synthesis 


  1. 1.
    Y. Luo, K. Chakrabarty, Design of pin-constrained general-purpose digital microfluidic biochips. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 32(9), 1307–1320 (2013)CrossRefGoogle Scholar
  2. 2.
    M. Ibrahim, K. Chakrabarty, U. Schlichtmann, CoSyn: efficient single-cell analysis using a hybrid microfluidic platform, in Design, Automation & Test in Europe Conference & Exhibition (Lausanne) (2017)Google Scholar
  3. 3.
    R. Silva, S. Bhatia, D. Densmore, A reconfigurable continuous-flow fluidic routing fabric using a modular, scalable primitive. Lab. Chip 16(14), 2730–2741 (2016)CrossRefGoogle Scholar
  4. 4.
    I.E. Araci, P. Brisk, Recent developments in microfluidic large scale integration. Curr. Opin. Biotechnol. 25, 60–68 (2014)CrossRefGoogle Scholar
  5. 5.
    S.-J. Kim, D. Lai, J.Y. Park, R. Yokokawa, S. Takayama, Microfluidic automation using elastomeric valves and droplets: reducing reliance on external controllers. Small 8(19), 2925–2934 (2012)CrossRefGoogle Scholar
  6. 6.
    B. Mosadegh, T. Bersano-Begey, J.Y. Park, M.A. Burns, S. Takayama, Next-generation integrated microfluidic circuits. Lab. Chip 11(17), 2813–2818 (2011)CrossRefGoogle Scholar
  7. 7.
    P.N. Duncan, S. Ahrar, E.E. Hui, Scaling of pneumatic digital logic circuits. Lab. Chip 15(5), 1360–1365 (2015)CrossRefGoogle Scholar
  8. 8.
    M. Rhee, M.A. Burns, Microfluidic pneumatic logic circuits and digital pneumatic microprocessors for integrated microfluidic systems. Lab. Chip 9(21), 3131–3143 (2009)CrossRefGoogle Scholar
  9. 9.
    M. Ibrahim, A. Sridhar, K. Chakrabarty, U. Schlichtmann, Sortex: efficient timing-driven synthesis of reconfigurable flow-based biochips for scalable single-cell screening, in Proceedings of IEEE/ACM International Conference on Computer-Aided Design (2017), pp. 623–630Google Scholar
  10. 10.
    G.J. Kost, Preventing medical errors in point-of-care testing: security, validation, performance, safeguards, and connectivity. Arch. Pathol. Lab. Med. 125(10), 1307–1315 (2001)Google Scholar
  11. 11.
    R. Garver, C. Seife, FDA let drugs approved on fraudulent research stay on the market (2013).
  12. 12.
    H. Fereidooni, J. Classen, T. Spink, P. Patras, M. Miettinen, A.-R. Sadeghi, M. Hollick, M. Conti, Breaking fitness records without moving: reverse engineering and spoofing Fitbit, in International Symposium on Research in Attacks, Intrusions, and Defenses (Springer, Berlin, 2017), pp. 48–69Google Scholar
  13. 13.
    D.G. Abraham, G.M. Dolan, G.P. Double, J.V. Stevens, Transaction security system. IBM Syst. J. 30(2), 206–229 (1991)CrossRefGoogle Scholar
  14. 14.
    A. Barenghi, L. Breveglieri, I. Koren, D. Naccache, Fault injection attacks on cryptographic devices: theory, practice, and countermeasures. Proc. IEEE 100(11), 3056–3076 (2012)CrossRefGoogle Scholar
  15. 15.
    H. Bar-El, H. Choukri, D. Naccache, M. Tunstall, C. Whelan, The sorcerer’s apprentice guide to fault attacks. Proc. IEEE 94(2), 370–382 (2006)CrossRefGoogle Scholar
  16. 16.
    E. Biham, A. Shamir, Differential fault analysis of secret key cryptosystems, in Proceedings of Annual International Cryptology Conference (Santa Barbara, CA) (Springer, Berlin, 1997), pp. 513–525zbMATHGoogle Scholar
  17. 17.
    H. Chen, S. Potluri, F. Koushanfar, BioChipWork: reverse engineering of microfluidic biochips, in Proceedings of IEEE International Conference on Computer Design (Newton, MA) (2017), pp. 9–16Google Scholar
  18. 18.
    Y. Moradi, M. Ibrahim, K. Chakrabarty, U. Schlichtmann, Fault-tolerant valve-based microfluidic routing fabric for droplet barcoding in single-cell analysis, in 2018 Design, Automation & Test in Europe Conference & Exhibition (2018)Google Scholar
  19. 19.
    M. Mesbahi, State-dependent graphs, in Proceedings of IEEE Conference on Decision and Control (Lahaina, HI), vol. 3 (2003), pp. 3058–3063Google Scholar
  20. 20.
    J. Tang, M. Ibrahim, K. Chakrabarty, R. Karri, Security implications of cyberphysical flow-based microfluidic biochips, in Proceedings of IEEE Asian Test Symposium (Taipei) (2017), pp. 110–115Google Scholar
  21. 21.
    J. Tang, M. Ibrahim, K. Chakrabarty, R. Karri, Security trade-offs in microfluidic routing fabrics, in Proceedings of IEEE International Conference on Computer Design (Newton, MA) (2017), pp. 25–32Google Scholar
  22. 22.
    S.-I. Minato, N. Ishiura, S. Yajima, Shared binary decision diagram with attributed edges for efficient Boolean function manipulation, in Proceedings of IEEE/ACM Design Automation Conference (1990), pp. 52–57Google Scholar
  23. 23.
    R.I. Bahar, E.A. Frohm, C.M. Gaona, G.D. Hachtel, E. Macii, A. Pardo, F. Somenzi, Algebric decision diagrams and their applications. Formal Methods Syst. Des. 10(2–3), 171–206 (1997)CrossRefGoogle Scholar
  24. 24.
    D.E. Knuth, A.C. Yao, The complexity of non-uniform random number generation, in Algorithms and Complexity: New Directions and Recent Results, ed. by J.F. Traub (Academic, New York, 1976)Google Scholar
  25. 25.
    H. Zhou, H.-L. Chen, J. Bruck, Synthesis of stochastic flow networks. IEEE Trans. Comput. 63(5), 1234–1247 (2014)MathSciNetCrossRefGoogle Scholar
  26. 26.
    R.E. Bryant, Graph-based algorithms for Boolean function manipulation. IEEE Trans. Comput. 100(8), 677–691 (1986)CrossRefGoogle Scholar
  27. 27.
    K.M. Horsman, J.M. Bienvenue, K.R. Blasier, J.P. Landers, Forensic DNA analysis on microfluidic devices: a review. J. For. Sci. 52(4), 784–799 (2007)Google Scholar
  28. 28.
    J. El-Ali, P.K. Sorger, K.F. Jensen, Cells on chips. Nature 442(7101), 403–411 (2006)CrossRefGoogle Scholar
  29. 29.
    S. Hosic, S.K. Murthy, A.N. Koppes, Microfluidic sample preparation for single cell analysis. Anal. Chem. 88(1), 354–380 (2015)CrossRefGoogle Scholar
  30. 30.
    A.M. Klein, L. Mazutis, I. Akartuna, N. Tallapragada, A. Veres, V. Li, L. Peshkin, D.A. Weitz, M.W. Kirschner, Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161(5), 1187–1201 (2015)CrossRefGoogle Scholar
  31. 31.
    T.M. Cover, J.A. Thomas, Elements of Information Theory (Wiley, New Delhi, 2012)zbMATHGoogle Scholar
  32. 32.
    M. Ibrahim, K. Chakrabarty, K. Scott, Synthesis of cyberphysical digital-microfluidic biochips for real-time quantitative analysis. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36(5), 733–746 (2017)CrossRefGoogle Scholar
  33. 33.
    J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann, San Francisco, 1988)zbMATHGoogle Scholar
  34. 34.
    L. Xing, S.V. Amari, Binary Decision Diagrams and Extensions for System Reliability Analysis (Scrivener, Beverly, 2015)CrossRefGoogle Scholar
  35. 35.
    C. Clos, A study of non-blocking switching networks. Bell Labs Tech. J. 32(2), 406–424 (1953)CrossRefGoogle Scholar
  36. 36.
    J. Duato, S. Yalamanchili, L.M. Ni, Interconnection Networks: An Engineering Approach (Morgan Kaufmann, San Francisco, 2003)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.New York UniversityBrooklynUSA
  2. 2.Intel (United States)Santa ClaraUSA
  3. 3.Department of ECEDuke UniversityDurhamUSA

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