Promises and Challenges in Continuous Tracking Utilizing Amino Acids in Skin Secretions for Active Multi-Factor Biometric Authentication for Cybersecurity

TL;DR

This paper explores a novel biometric authentication method using amino acid profiles from skin secretions, aiming for continuous, active user verification on mobile and wearable devices through biochemical signal analysis.

Contribution

It introduces a new concept of biometric cybersecurity based on amino acid analysis in skin secretions, utilizing biocatalytic cascades for continuous active authentication.

Findings

Amino acid levels vary among individuals, enabling biometric differentiation.

Assays can classify groups by age, sex, race, and physiological state.

Multi-input cascades can produce digital outputs for robust authentication.

Abstract

We consider a new concept of biometric-based cybersecurity systems for active authentication by continuous tracking, which utilizes biochemical processing of metabolites present in skin secretions. Skin secretions contain a large number of metabolites and small molecules that can be targeted for analysis. Here we argue that amino acids found in sweat can be exploited for the establishment of an amino acid profile capable of identifying an individual user of a mobile or wearable device. Individual and combinations of amino acids processed by biocatalytic cascades yield physical (optical or electronic) signals, providing a time-series of several outputs that, in their entirety, should suffice to authenticate a specific user based on standard statistical criteria. Initial results, motivated by biometrics, indicate that single amino acid levels can provide analog signals that vary according to the individual donor, albeit with limited resolution versus noise. However, some such assays offer digital separation (into well-defined ranges of values) according to groups such as age, biological sex, race, and physiological state of the individual. Multi-input biocatalytic cascades that handle several amino acid signals to yield a single digital-type output, as well as continuous-tracking time-series data rather than a single-instance sample, should enable active authentication at the level of an individual.