Privacy Preserving Digital Personal Assistant Using Homomorphic Encryption
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Solution Overview
Problem
Existing digital personal assistant technologies compromise user privacy by requiring voice commands to be sent to cloud servers for processing, where voiceprints are decrypted, exposing users to privacy risks and latency issues due to computationally intensive deep neural networks.
Innovation Solution
Implementing a privacy-preserving digital personal assistant using homomorphic encryption, where voice commands are encrypted locally and processed on an edge device, with homomorphic encryption-based algorithms performing speech-to-text and natural language processing without decrypting the data, ensuring user privacy and reducing latency by using simpler string matching algorithms in the cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice commands are sent to cloud servers for processing, then speech recognition and natural language processing can be performed, but user privacy is compromised and latency increases
Solution Approach 1:
The patent applies preliminary action by encrypting voice commands locally on the user device before they are transmitted to the cloud server. This encryption occurs in advance, allowing the data to be processed remotely while maintaining privacy protection throughout the transmission and processing pipeline
Solution Approach 2:
Homomorphic encryption serves as an intermediary mechanism that enables cloud servers to process encrypted speech data without being able to decrypt or read the actual content. This intermediary layer allows accurate speech recognition while preventing privacy loss, as the server operates on mathematically transformed data
2Measurement precision
If voice commands are sent to cloud servers for processing, then comprehensive speech processing can be performed, but communication latency increases
Solution Approach 1:
The patent applies preliminary action by performing local preprocessing of voice commands, including encryption and initial speech-to-text conversion, before transmitting data to the cloud. This reduces the amount of data that needs to be transmitted and processed remotely, thereby reducing communication latency while maintaining comprehensive speech processing capabilities
Solution Approach 2:
The patent segments the speech processing workflow into local and remote components. Local processing handles encryption and initial transcription, while cloud processing handles natural language understanding and response generation. This segmentation reduces communication overhead and latency by minimizing the data that must be transmitted
3Measurement precision
If deep neural networks are used for speech processing in the cloud, then accurate speech recognition can be achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by performing speech-to-text conversion locally on the user device before transmitting data to the cloud server. This preprocessing step converts complex audio data into simpler text representations, reducing the computational burden on cloud-based deep neural networks while maintaining recognition accuracy
Data Source
AI summary
A method comprises receiving, from an input device, an input speech signal, encoding the input speech signal to generate a first homomorphically encrypted string, sending the homomorphically encrypted string to a remote device via communication link, receiving, from the remote device, a reply comprising a second homomorphically encrypted string, decoding the second homomorphically encrypted string into an output speech signal, and outputting the output speech signal on an audio output device.