Software Agents for Privacy-Preserving Affective Computing
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Solution Overview
Problem
The collection and utilization of affective response measurements from wearable devices pose challenges in maintaining user privacy while providing benefits such as personalized experiences and crowd-based results, as excessive data sharing can lead to biased models that infringe on user privacy.
Innovation Solution
A framework utilizing software agents to collect and interpret affective response measurements, acting as gatekeepers to selectively provide relevant data, ensuring privacy by determining the risk of disclosure and correcting for user biases before sharing, thereby maintaining user privacy while enabling the generation of crowd-based results like scores and rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If affective response measurements are shared with external parties for crowd-based results, then the benefit to the user from data sharing is improved, but the risk to user privacy worsens
Solution Approach 1:
A software agent is introduced as an intermediary between the user and external parties. The agent selectively determines which affective response measurements to share, acting as a gatekeeper that filters data based on privacy risk assessment. This intermediary enables crowd-based result generation while protecting user privacy by controlling information flow.
Solution Approach 2:
The affective response data is segmented into different levels of disclosure. The software agent selectively shares only relevant measurements with external parties rather than all raw data, dividing the information into public aggregate results and private individual measurements. This segmentation allows benefit realization while minimizing privacy exposure.
2Measurement precision
If detailed affective response data is provided to external parties, then the accuracy of crowd-based results is improved, but the extent of private data exposure worsens
Solution Approach 1:
The software agent applies partial action by sharing only the necessary portion of affective response data required for accurate crowd-based results. Rather than providing complete raw data, the agent selectively discloses measurements that contribute to aggregate accuracy while withholding information that would expose private user characteristics or biases.
3Measurement precision
If comprehensive user modeling is performed to interpret emotional responses accurately, then the interpretation accuracy is improved, but the complexity of the system worsens
Solution Approach 1:
The system performs self-service by automatically generating and updating user models through the software agent without requiring external intervention. The agent continuously learns from affective response measurements to improve interpretation accuracy while managing the complexity internally, shielding users from the underlying system complexity.
Data Source
AI summary
Software agents collect measurements of affective response and provide them in a selective manner that maintains user privacy. In one embodiment, a sensor takes measurements of affective response of the user. A computer receives a request for an affective value indicative of an emotional response to an experience. The computer selects, from among the measurements, a certain measurement of affective response of the user that corresponds to an event in which the user had the experience. The computer then utilizes a model to calculate, based on the certain measurement, the affective value indicative of the emotional response of the user to experience. Optionally, the model is generated based on previously taken measurements of affective response of the user, taken while the user had various experiences, and indications of emotional responses the user had while said previously taken measurements were taken. The computer sends the affective value to fulfil the request.