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CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications
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 journalin...
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| Format: | Thesis |
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AUC Knowledge Fountain
2026
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| _version_ | 1867613428218068992 |
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| access_status_str | Open Access |
| author | Dawoud, Mariam |
| author_browse | Dawoud, Mariam |
| author_facet | Dawoud, Mariam |
| author_sort | Dawoud, Mariam |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-3627 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:59.467Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-3627 CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications Dawoud, Mariam 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. 2026-01-31T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/2589 https://fount.aucegypt.edu/context/etds/article/3627/viewcontent/mariam_dawoud_thesis.pdf https://fount.aucegypt.edu/context/etds/article/3627/filename/7/type/additional/viewcontent/Disclosure_of_AI_Use_Form_.pdf Theses and Dissertations AUC Knowledge Fountain privacy-preservation homomorphic encryption differential pri- vacy immutability mental health mobile applications cognitive distortions Other Computer Engineering |
| spellingShingle | privacy-preservation homomorphic encryption differential pri- vacy immutability mental health mobile applications cognitive distortions Other Computer Engineering Dawoud, Mariam CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications |
| title | CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications |
| title_full | CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications |
| title_fullStr | CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications |
| title_full_unstemmed | CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications |
| title_short | CogniVault: Enabling Privacy-Aware Cognitive Distortion Detection in Intelligent Mental Health Applications |
| title_sort | cognivault enabling privacy aware cognitive distortion detection in intelligent mental health applications |
| topic | privacy-preservation homomorphic encryption differential pri- vacy immutability mental health mobile applications cognitive distortions Other Computer Engineering |
| url | https://fount.aucegypt.edu/etds/2589 https://fount.aucegypt.edu/context/etds/article/3627/viewcontent/mariam_dawoud_thesis.pdf https://fount.aucegypt.edu/context/etds/article/3627/filename/7/type/additional/viewcontent/Disclosure_of_AI_Use_Form_.pdf |
| work_keys_str_mv | AT dawoudmariam cognivaultenablingprivacyawarecognitivedistortiondetectioninintelligentmentalhealthapplications |