# A Mathematical Framework for the Problem of Security for Cognition in Neurotechnology

arXiv: https://arxiv.org/abs/2403.07945 · HTML: https://mlsanity.com/paper/2403.07945/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 78 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [Photonics for artificial intelligence and neuromorphic computing](https://mlsanity.com/paper/2011.00111/) (2020)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations](https://mlsanity.com/paper/2111.06077/) (2021)
- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [Blessing of dimensionality: mathematical foundations of the statistical physics of data](https://mlsanity.com/paper/1801.03421/) (2018)
- [In-memory hyperdimensional computing](https://mlsanity.com/paper/1906.01548/) (2019)
- [The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures](https://mlsanity.com/paper/1311.2891/) (2013)
- [The unreasonable effectiveness of small neural ensembles in high-dimensional brain](https://mlsanity.com/paper/1809.07656/) (2018)
- [High--Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality](https://mlsanity.com/paper/2001.04959/) (2020)
- [Embracing the Blessing of Dimensionality in Factor Models](https://mlsanity.com/paper/1610.07697/) (2016)
- [QubitHD: A Stochastic Acceleration Method for HD Computing-Based Machine Learning](https://mlsanity.com/paper/1911.12446/) (2019)
- [Cyberattacks on Miniature Brain Implants to Disrupt Spontaneous Neural Signaling](https://mlsanity.com/paper/2007.09466/) (2020)

## Built on by (most cited first, 1 of 1)

- [Approximating the Mathematical Structure of Psychodynamics](https://mlsanity.com/paper/2511.05580/) (2025)

## Abstract (arXiv)

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.