International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 2
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Privacy-Preserving Digital Phenotyping: A Zero-Knowledge Framework for Early Autism Spectrum Disorder Detection

Author(s) Ms. Sneha Giri
Country India
Abstract Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition in which early intervention is the key factor of the long-term function. However, with older-style efforts in clinical diagnosis, the channels are often subject to delay due to subjective diagnostic assessments, and limited availability of specialists. While Machine Learning (ML) provides a novel approach via digital phenotyping, by leveraging telemetry from smartphones such as keystrokes dynamics, sensor patterns, and frequency of social interaction etc., and the gathering of such granular data from individuals, an ethical and privacy issue carcinogenic. This paper proposes novel, privacy focussed objects, called Zero-Knowledge Proofs (ZKP), where Digital Phenotyping works alongside ZKP to enable secure ASD screening. Unlike current models, which involve centralisation of sensitive data pertaining to behavioural classification the proposed model uses on-device machine learning (ML) for behavioural classification, followed by generation of a ZKP to validate the diagnostic result without revealing underlying raw telemetry. By filling the information gap between high frequency behavioural monitoring and cryptographic data privacy, this research work is a scalable, objective, and "privacy-by-design" solution for early screening of ASD. Preliminary analysis indicates that using this dual layer architecture will not only ensure diagnostics sensitivity at the highest level possible but also overcomes the "Privacy Paradox" (a reluctance to share data for privacy reasons) by promoting broader adoption of digital health monitoring among vulnerable populations.
Keywords Keywords-- Autism Spectrum Disorder (ASD), Machine Learning, Zero-Knowledge Proofs (ZKP), Privacy-Preserving Artificial Intelligence (AI), Keystroke Dynamics, Behavioural Informatics.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-17
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E-ISSN 2582-2160
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