Data depersonalization using on-device generative models

An edge-based system comprising one or more processors and memory storing instructions that, in response to execution by the one or more processors, cause the one or more processors to:

• retrieve, from local memory of an edge computing device, a generative model input prompt that was processed using one or more generative models to generate generative model output;
• submit the generative model input prompt to an edge-based anonymization process that includes:
• assemble, as an anonymization prompt, at least a portion of the generative model input prompt, wherein the portion comprises one or more images;
• process the anonymization prompt using one or more on-device generative models of the edge computing device to generate an anonymized version of the portion of the generative model input prompt that is stripped of at least some personally identifiable information (PII), wherein at least one of the one or more on-device generative model comprises a vision language model (VLM) or an image generation model;
• inspect the anonymized version for any remaining PII;
• and
• in response to a determination, based on the inspecting, that the anonymized version is free of any remaining PII, upload the anonymized version from the edge computing device to a remote server.

At least one non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to:

• retrieve, from local memory of an edge computing device, a generative model input prompt that was processed using one or more generative models to generate generative model output;
• submit the generative model input prompt to an edge-based anonymization process that includes:
• assemble, as an anonymization prompt, at least a portion of the generative model input prompt, wherein the portion comprises one or more images;
• process the anonymization prompt using one or more on-device generative models of the edge computing device to generate an anonymized version of the portion of the generative model input prompt that is stripped of at least some personally identifiable information (PII), wherein at least one of the on-device generative models comprises a vision language model (VLM) or an image generation model;
• inspect the anonymized version for any remaining PII;
• and
• in response to a determination, based on the inspecting, that the anonymized version is free of any remaining PII, upload the anonymized version from the edge computing device to a remote server.