Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Mathematical Framework for the Problem of Security for Cognition in Neurotechnology (2403.07945v4)

Published 11 Mar 2024 in cs.CR, cs.CY, cs.ET, cs.LG, and q-bio.NC

Abstract: The rapid advancement in neurotechnology in recent years has created an emerging critical intersection between neurotechnology and security. Implantable devices, non-invasive monitoring, and non-invasive therapies all carry with them the prospect of violating the privacy and autonomy of individuals' cognition. A growing number of scientists and physicians have made calls to address this issue, but applied efforts have been relatively limited. A major barrier hampering scientific and engineering efforts to address these security issues is the lack of a clear means of describing and analyzing relevant problems. In this paper we develop Cognitive Neurosecurity, a mathematical framework which enables such description and analysis by drawing on methods and results from multiple fields. We demonstrate certain statistical properties which have significant implications for Cognitive Neurosecurity, and then present descriptions of the algorithmic problems faced by attackers attempting to violate privacy and autonomy, and defenders attempting to obstruct such attempts.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (67)
  1. “Cingulate dynamics track depression recovery with deep brain stimulation” In Nature 622.7981 Springer ScienceBusiness Media LLC, 2023, pp. 130–138 DOI: 10.1038/s41586-023-06541-3
  2. “High performance communication by people with paralysis using an intracortical brain-computer interface” In eLife 6 eLife Sciences Publications, Ltd, 2017 DOI: 10.7554/elife.18554
  3. “High-performance brain-to-text communication via handwriting” In Nature 593.7858 Springer ScienceBusiness Media LLC, 2021, pp. 249–254 DOI: 10.1038/s41586-021-03506-2
  4. “A high-performance speech neuroprosthesis” In Nature 620.7976 Springer ScienceBusiness Media LLC, 2023, pp. 1031–1036 DOI: 10.1038/s41586-023-06377-x
  5. “A high-performance neuroprosthesis for speech decoding and avatar control” In Nature 620.7976 Springer ScienceBusiness Media LLC, 2023, pp. 1037–1046 DOI: 10.1038/s41586-023-06443-4
  6. Anthony Gilmore “The Affair of the Brains” Ozymandias Press, 2018
  7. William Gibson “Neuromancer” New York, NY: Penguin Putnam, 1984
  8. Liam Drew “The rise of brain-reading technology: what you need to know” In Nature 623.7986 Springer ScienceBusiness Media LLC, 2023, pp. 241–243 DOI: 10.1038/d41586-023-03423-6
  9. “Edge Cryptography and the Codevelopment of Computer Networks and Cybersecurity” In IEEE Annals of the History of Computing 38.4 Institute of ElectricalElectronics Engineers (IEEE), 2016, pp. 55–73 DOI: 10.1353/ahc.2016.0044
  10. “Exposed! A Survey of Attacks on Private Data” In Annual Review of Statistics and Its Application 4.1 Annual Reviews, 2017, pp. 61–84 DOI: 10.1146/annurev-statistics-060116-054123
  11. Salil Vadhan “The Complexity of Differential Privacy” In Information Security and Cryptography Springer International Publishing, 2017, pp. 347–450 DOI: 10.1007/978-3-319-57048-8˙7
  12. “The algorithmic foundations of differential privacy”, Foundations and Trends (R) in Theoretical Computer Science Hanover, MD: now, 2014
  13. “Differential Privacy: A Primer for a Non-Technical Audience” Vanderbilt Journal of EntertainmentTechnology Law, 2020
  14. Pentti Kanerva “Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors” In Cognitive Computation 1.2 Springer ScienceBusiness Media LLC, 2009, pp. 139–159 DOI: 10.1007/s12559-009-9009-8
  15. “A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations” In ACM Comput. Surv. 55.6 New York, NY, USA: Association for Computing Machinery, 2022 DOI: 10.1145/3538531
  16. “A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges” In ACM Comput. Surv. 55.9 New York, NY, USA: Association for Computing Machinery, 2023 DOI: 10.1145/3558000
  17. Michael N. Jones and Douglas J.K. Mewhort “Representing word meaning and order information in a composite holographic lexicon.” In Psychological Review 114.1 American Psychological Association (APA), 2007, pp. 1–37 DOI: 10.1037/0033-295x.114.1.1
  18. Peer Neubert, Stefan Schubert and Peter Protzel “An Introduction to Hyperdimensional Computing for Robotics” In KI - Künstliche Intelligenz 33.4 Springer ScienceBusiness Media LLC, 2019, pp. 319–330 DOI: 10.1007/s13218-019-00623-z
  19. “In-memory hyperdimensional computing” In Nature Electronics 3.6 Springer ScienceBusiness Media LLC, 2020, pp. 327–337 DOI: 10.1038/s41928-020-0410-3
  20. May-Britt Moser, David C Rowland and Edvard I Moser “Place cells, grid cells, and memory” In Cold Spring Harb. Perspect. Biol. 7.2 Cold Spring Harbor Laboratory, 2015, pp. a021808
  21. “Explicit Encoding of Multimodal Percepts by Single Neurons in the Human Brain” In Current Biology 19.15 Elsevier BV, 2009, pp. 1308–1313 DOI: 10.1016/j.cub.2009.06.060
  22. Alexander N Gorban, Valeri A Makarov and Ivan Y Tyukin “The unreasonable effectiveness of small neural ensembles in high-dimensional brain” In Physics of life reviews 29 Elsevier, 2019, pp. 55–88
  23. Asim Roy “A theory of the brain - the brain uses both distributed and localist (symbolic) representation” In The 2011 International Joint Conference on Neural Networks, 2011, pp. 215–221 DOI: 10.1109/IJCNN.2011.6033224
  24. “Conceptual representations in mind and brain: Theoretical developments, current evidence and future directions” In Cortex 48.7 Elsevier BV, 2012, pp. 805–825 DOI: 10.1016/j.cortex.2011.04.006
  25. Alexander N. Gorban, Valery A. Makarov and Ivan Y. Tyukin “High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality” In Entropy 22.1 MDPI AG, 2020, pp. 82 DOI: 10.3390/e22010082
  26. , 2023 URL: https://atom-computing.com/quantum-startup-atom-computing-first-to-exceed-1000-qubits/
  27. “High-fidelity gates and mid-circuit erasure conversion in an atomic qubit” In Nature 622.7982 Springer ScienceBusiness Media LLC, 2023, pp. 279–284 DOI: 10.1038/s41586-023-06438-1
  28. “High-fidelity parallel entangling gates on a neutral-atom quantum computer” In Nature 622.7982 Springer ScienceBusiness Media LLC, 2023, pp. 268–272 DOI: 10.1038/s41586-023-06481-y
  29. “Erasure conversion in a high-fidelity Rydberg quantum simulator” In Nature 622.7982 Springer ScienceBusiness Media LLC, 2023, pp. 273–278 DOI: 10.1038/s41586-023-06516-4
  30. Bruce M Brown, Peter Hall and G Alastair Young “On the effect of inliers on the spatial median” In journal of multivariate analysis 63.1 Elsevier, 1997, pp. 88–104
  31. David Donoho “High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality” In AMS Math Challenges Lecture, 2000, pp. 1–32
  32. “Blessing of dimensionality: mathematical foundations of the statistical physics of data” In Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376.2118 The Royal Society, 2018, pp. 20170237 DOI: 10.1098/rsta.2017.0237
  33. “Embracing the blessing of dimensionality in factor models” In Journal of the American Statistical Association 113.521 Taylor & Francis, 2018, pp. 380–389
  34. “The blessing of Dimensionality: Feature Selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation” In PloS one 13.8 Public Library of Science San Francisco, CA USA, 2018, pp. e0201660
  35. Guangcan Liu, Qingshan Liu and Ping Li “Blessing of dimensionality: Recovering mixture data via dictionary pursuit” In IEEE transactions on pattern analysis and machine intelligence 39.1 IEEE, 2016, pp. 47–60
  36. “The more, the merrier: the blessing of dimensionality for learning large Gaussian mixtures” In Conference on Learning Theory, 2014, pp. 1135–1164 PMLR
  37. Hun-Seok Kim “HDM: Hyper-Dimensional Modulation for Robust Low-Power Communications” In 2018 IEEE International Conference on Communications (ICC), 2018, pp. 1–6 DOI: 10.1109/ICC.2018.8422472
  38. Peter D. Bruza, Zheng Wang and Jerome R. Busemeyer “Quantum cognition: a new theoretical approach to psychology” In Trends in Cognitive Sciences 19.7 Elsevier BV, 2015, pp. 383–393 DOI: 10.1016/j.tics.2015.05.001
  39. Emmanuel M. Pothos and Jerome R. Busemeyer “Quantum Cognition” In Annual Review of Psychology 73.1 Annual Reviews, 2022, pp. 749–778 DOI: 10.1146/annurev-psych-033020-123501
  40. Jerome R. Busemeyer and Emmanuel M. Pothos “Quantum Models of Cognition” In The Cambridge Handbook of Computational Cognitive Sciences Cambridge University Press, 2023, pp. 242–274 DOI: 10.1017/9781108755610.010
  41. James M. Yearsley and Jerome R. Busemeyer “Quantum cognition and decision theories: A tutorial” In Journal of Mathematical Psychology 74 Elsevier BV, 2016, pp. 99–116 DOI: 10.1016/j.jmp.2015.11.005
  42. Jerome R. Busemeyer and Zheng Wang “What Is Quantum Cognition, and How Is It Applied to Psychology?” In Current Directions in Psychological Science 24.3 SAGE Publications, 2015, pp. 163–169 DOI: 10.1177/0963721414568663
  43. “At the crossroad of the search for spontaneous radiation and the Orch OR consciousness theory” In Physics of Life Reviews 42 Elsevier BV, 2022, pp. 8–14 DOI: 10.1016/j.plrev.2022.05.004
  44. Greg Egan “Quarantine” New York, NY: HarperCollins, 1994
  45. Robert J Sawyer “Quantum Night” Ace Books, 2016
  46. Mike Chen “A quantum love story” Mira Books, 2024
  47. John Neumann “Mathematische Grundlagen der Quantenmechanik” Berlin: Springer-Verlag; English translation: Mathematical Foundations of Quantum Mechanics, Princeton: Princeton University Press, 1955
  48. A N Kolmogorov “Grundbegriffe der Wahrscheinlichkeitsrechnung (Foundations of the theory of probability)”, 1933
  49. Joschka Roffe “Quantum error correction: an introductory guide” In Contemporary Physics 60.3 Informa UK Limited, 2019, pp. 226–245 DOI: 10.1080/00107514.2019.1667078
  50. “Challenges and Opportunities of Near-Term Quantum Computing Systems” In Proceedings of the IEEE 108.8 Institute of ElectricalElectronics Engineers (IEEE), 2020, pp. 1338–1352 DOI: 10.1109/jproc.2019.2954005
  51. “Quantum Error Mitigation as a Universal Error Reduction Technique: Applications from the NISQ to the Fault-Tolerant Quantum Computing Eras” In PRX Quantum 3.1 American Physical Society (APS), 2022 DOI: 10.1103/prxquantum.3.010345
  52. Salonik Resch and Ulya R. Karpuzcu “Benchmarking Quantum Computers and the Impact of Quantum Noise” In ACM Computing Surveys 54.7 Association for Computing Machinery (ACM), 2021, pp. 1–35 DOI: 10.1145/3464420
  53. “Materials challenges and opportunities for quantum computing hardware” In Science 372.6539 American Association for the Advancement of Science (AAAS), 2021 DOI: 10.1126/science.abb2823
  54. “Forecasting timelines of quantum computing”, 2020 arXiv:2009.05045 [quant-ph]
  55. “Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research” In Neural Networks 144, 2021, pp. 603–613 DOI: https://doi.org/10.1016/j.neunet.2021.09.018
  56. Shimon Ullman “Using neuroscience to develop artificial intelligence” In Science 363.6428 American Association for the Advancement of Science (AAAS), 2019, pp. 692–693 DOI: 10.1126/science.aau6595
  57. “Neuroscience-Inspired Artificial Intelligence” In Neuron 95.2 Elsevier BV, 2017, pp. 245–258 DOI: 10.1016/j.neuron.2017.06.011
  58. Kwabena Boahen “Dendrocentric learning for synthetic intelligence” In Nature 612.7938 Springer ScienceBusiness Media LLC, 2022, pp. 43–50 DOI: 10.1038/s41586-022-05340-6
  59. “Neural inference at the frontier of energy, space, and time” In Science 382.6668 American Association for the Advancement of Science (AAAS), 2023, pp. 329–335 DOI: 10.1126/science.adh1174
  60. “Photonics for artificial intelligence and neuromorphic computing” In Nature Photonics 15.2 Springer ScienceBusiness Media LLC, 2021, pp. 102–114 DOI: 10.1038/s41566-020-00754-y
  61. “QubitHD: A Stochastic Acceleration Method for HD Computing-Based Machine Learning” In CoRR abs/1911.12446, 2019 arXiv: http://arxiv.org/abs/1911.12446
  62. “Integrating complex valued hyperdimensional computing with modular artificial neural networks” In Disruptive Technologies in Information Sciences VII SPIE, 2023 DOI: 10.1117/12.2664585
  63. Wikimedia Commons URL: https://commons.wikimedia.org/wiki/File:Bloch_sphere.svg
  64. Philip Hall “The Distribution of Means for Samples of Size N Drawn from a Population in which the Variate Takes Values Between 0 and 1, All Such Values Being Equally Probable” In Biometrika 19.3/4 JSTOR, 1927, pp. 240 DOI: 10.2307/2331961
  65. “Recent reliability reporting practices in Psychological Assessment: Recognizing the people behind the data.” In Psychological Assessment 23.3 American Psychological Association (APA), 2011, pp. 656–669 DOI: 10.1037/a0023089
  66. “Psychological testing and psychological assessment: A review of evidence and issues.” In American Psychologist 56.2 American Psychological Association (APA), 2001, pp. 128–165 DOI: 10.1037/0003-066x.56.2.128
  67. George Poste “Biosecurity: Complexity, Connectivity, Complacency & Commitment” Stanford University Biosecurity and Pandemic Resilience lectures, 2024

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now