Acoustic Obfuscation via Distributed Injector Nodes
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Solution Overview
Problem
Open area audible communications between humans and AI-enabled devices lack privacy, as they can be easily eavesdropped upon, unlike electronic communications, making it difficult to maintain private conversations in open listening spaces.
Innovation Solution
A system and method using natural interaction steganography to obfuscate audible messages by introducing noise, involving an orchestrator that selects injector nodes to inject sounds into the listening space, distributing keys derived from a natural interface key, and employing machine learning to calculate this key based on interactions, allowing the target node to interpret the original message from the obfuscated one.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If audible communications are broadcast in open spaces, then communication accessibility is improved, but privacy is compromised due to easy eavesdropping
Solution Approach 1:
The system segments the audible communication channel by introducing multiple injector nodes that broadcast obfuscating sounds independently. Each injector node receives segmented key material and contributes a portion of the obfuscation, collectively creating a privacy-protecting noise field that masks the original communication without requiring a single complex masking system.
Solution Approach 2:
The system introduces intermediary injector nodes that broadcast obfuscating sounds between the source and target nodes. These intermediaries create a acoustic veil that prevents direct eavesdropping while allowing the intended target to recover the original message through context switching and key-based filtering. The intermediaries act as mediators that protect privacy while maintaining communication functionality.
2Object-affected harmful factors
If noise is injected to obfuscate audible messages, then privacy is improved, but communication clarity deteriorates for legitimate listeners
Solution Approach 1:
The system changes the parameters of the audible communication by modulating the obfuscating sounds according to encrypted key material. The injector nodes vary the timing, frequency, and intensity of injected sounds based on the key, creating a dynamic masking effect that appears as random noise to eavesdroppers but contains structured information that the legitimate target can decode through context switching.
Solution Approach 2:
The obfuscation is applied locally to specific communication channels rather than uniformly across all audio. The system identifies the specific source-target communication path and injects obfuscating sounds targeted at that channel, allowing other communications in the environment to remain clear. This localized approach minimizes impact on legitimate communication while maintaining eavesdropping prevention.
3Reliability
If multiple injector nodes are deployed to enhance obfuscation, then privacy security is improved, but system complexity increases
Solution Approach 1:
The injector nodes are designed with multi-functionality, serving both as communication participants and as privacy protection devices. Each node can function as a source, target, or injector depending on the communication context, eliminating the need for separate dedicated masking hardware. The same hardware infrastructure supports both legitimate communication and obfuscation functions, reducing overall system complexity.
Solution Approach 2:
The system uses the existing communication infrastructure and participants to provide the obfuscation service. Rather than requiring external dedicated masking systems, the communication nodes themselves generate and broadcast the obfuscating sounds using their own resources. Each node contributes to the collective privacy protection of the network, making the system self-sufficient and reducing external complexity.
Data Source
AI summary
A system, method and program product for obfuscating audible messages in a listening space A system is provided that includes an orchestrator having: an invocation detection system that triggers an obfuscation event; a system for selecting injector nodes in the listening space for the obfuscation event; and a key management system that distributes keys, derived from a natural interface key, to the injector nodes to cause the injector nodes to inject sounds into the listening space to obfuscate an audible message broadcast by a source node for a target node; and a machine learning system that calculates the natural interface key based on interactions captured from the source node in the listening space.