Abstract
Mental health applications are increasingly leveraging intelligent systems to sup- port psychological well-being, yet preserving user privacy remains a major concern. This thesis presents CogniVault, a secure ecosystem for cognitive distortion data. The framework includes Cognify, a mobile journaling application that detects cog- nitive distortions in user-written journal entries using a locally deployed machine learning model. Cognitive distortions are maladaptive thought patterns such as catastrophizing or personalization, which the app identifies to provide therapeu- tic insights. To ensure privacy-preserving data analytics, CogniVault includes the design and implementation of a hybrid security architecture, PRISM-HDI, that combines Paillier Homomorphic Encryption (HE), Differential Privacy (DP), and Immutability. User data, including mood scores and detected cognitive distortion labels, are encrypted on-device using Paillier HE and sent to a secure backend where encrypted aggregation is performed. After decryption, noise is added ac- cording to differential privacy guarantees to protect individual user contributions before presenting insights to therapists and other users. These insights include label and mood distribution across patients and per-user temporal trends. To protect the safety of stored data on the cloud, we implement blockchain inspired immutability to ensure data is tamper-proof. We evaluate the system’s usabil- ity, encryption overhead, and security resilience to demonstrate its feasibility for real-world deployment. Our results show that privacy can be preserved without significantly affecting performance or sacrificing the utility of mental health ana- lytics, paving the way for responsible AI in mental health care.
School
School of Sciences and Engineering
Department
Computer Science & Engineering Department
Degree Name
MS in Computer Science
Graduation Date
Winter 1-31-2026
Submission Date
9-11-2025
First Advisor
Alia ElBolock
Committee Member 1
Alia ElBolock
Committee Member 2
Amr ElMougy
Committee Member 3
Frank Kargl
Extent
98 p.
Document Type
Master's Thesis
Institutional Review Board (IRB) Approval
Approval has been obtained for this item
Recommended Citation
APA Citation
Dawoud, M.
(2026).CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2589
MLA Citation
Dawoud, Mariam. CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications. 2026. American University in Cairo, Master's Thesis. AUC Knowledge Fountain.
https://fount.aucegypt.edu/etds/2589
Disclosure of AI Use Mariam Dawoud