PRIORIS: Enabling Secure Detection of Suicidal Ideation from Speech Using Homomorphic Encryption

Natarajan, Deepika; Dalskov, Anders P. K.; Kales, Daniel; Khanna, Shabnam

doi:10.1007/978-3-030-77287-1_10

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…However, when we talk about searching across files of variable lengths, the performances may vary and the usability of the system at a large scale may give rise to usability concerns. A paper published by Microsoft [12] proposed a mechanism for the detection of a suicidal idea from phone conversations while preserving privacy using a homomorphic evaluation of neural networks. This research work was also evaluated using the SEAL library.…”

Section: Secure Voice Processingmentioning

confidence: 99%

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A Novel Homomorphic Approach for Preserving Privacy of Patient Data in Telemedicine

Iqbal

Tahir

et al. 2022

Sensors

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Globally, the surge in disease and urgency in maintaining social distancing has reawakened the use of telemedicine/telehealth. Amid the global health crisis, the world adopted the culture of online consultancy. Thus, there is a need to revamp the conventional model of the telemedicine system as per the current challenges and requirements. Security and privacy of data are main aspects to be considered in this era. Data-driven organizations also require compliance with regulatory bodies, such as HIPAA, PHI, and GDPR. These regulatory compliance bodies must ensure user data privacy by implementing necessary security measures. Patients and doctors are now connected to the cloud to access medical records, e.g., voice recordings of clinical sessions. Voice data reside in the cloud and can be compromised. While searching voice data, a patient’s critical data can be leaked, exposed to cloud service providers, and spoofed by hackers. Secure, searchable encryption is a requirement for telemedicine systems for secure voice and phoneme searching. This research proposes the secure searching of phonemes from audio recordings using fully homomorphic encryption over the cloud. It utilizes IBM’s homomorphic encryption library (HElib) and achieves indistinguishability. Testing and implementation were done on audio datasets of different sizes while varying the security parameters. The analysis includes a thorough security analysis along with leakage profiling. The proposed scheme achieved higher levels of security and privacy, especially when the security parameters increased. However, in use cases where higher levels of security were not desirous, one may rely on a reduction in the security parameters.

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Section: Secure Voice Processingmentioning

confidence: 99%

“…Privacy Preservation [11] Newton-Raphson [12] Neural Network [13] Index-based [31] Blockchain [32] Attribute Encryption XACML [16] Hyper-ledger [18] Proxy Signature Group Signature [15] Blockchain [24] Index-based [25] Index-based [26] DGHV HE [27] Index-based…”

Section: Paper Technique Used Confidentiality He Searching Capabilitymentioning

confidence: 99%

A Novel Homomorphic Approach for Preserving Privacy of Patient Data in Telemedicine

Iqbal

Tahir

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Secure speech retrieval method using deep hashing and CKKS fully homomorphic encryption

Zhang,

Wen,

Huang

et al. 2024

Multimed Tools Appl

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PRIORIS: Enabling Secure Detection of Suicidal Ideation from Speech Using Homomorphic Encryption

Cited by 2 publications

References 15 publications

A Novel Homomorphic Approach for Preserving Privacy of Patient Data in Telemedicine

A Novel Homomorphic Approach for Preserving Privacy of Patient Data in Telemedicine

Secure speech retrieval method using deep hashing and CKKS fully homomorphic encryption

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