Abstract
Data analytics on mobile devices is important for improving user experience and developing better products and services. Device analytics need to be performed in compliance with regulations. A privacy compliance utility needs to establish univalence before and after privacy compliance to generate reliable and accurate data for device analytics. This disclosure provides a specification for a privacy utility based on synthetized homotopy that can be utilized by services that perform device analytics. The techniques described in this disclosure can also be used to enable privacy compliant content sharing with nearby devices. The techniques described in this disclosure can be utilized for privacy compliant device analytics without relying on the classic set theory and logic assumptions on axiom of choice (AC) and the law of excluded middle (LEM).
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
n/a, "Synthetized Homotopy Based Privacy for Device Analytics", Technical Disclosure Commons, (June 07, 2023)
https://www.tdcommons.org/dpubs_series/5944