Abstract
This disclosure describes ephemeral data sharing, a hardware-enforced framework enabling short-term, zero-retention context leases for artificial intelligence (AI) models while reducing the risk of data leaks, unauthorized logging, or model retraining. Ingested via dynamic optical bursts, ambient radio frequency (RF) proximity, or decentralized ledgers, confidential data is processed exclusively inside an isolated graphics processing unit (GPU) Trusted Execution Environment (TEE). Silicon-level memory controllers block unauthorized bus exports and enforce time/iteration lifecycles, while block-diagonal attention masking suppresses cross-context leakage during concurrent multi-party processing. Upon completion or expiration, an unmaskable hardware interrupt triggers a 3-pass microcode purge (voltage saturation, zero-fill, and true random number generator (TRNG) noise injection) to reduce physical data remanence. An on-chip Hardware Security Module (HSM) then emits a signed Proof of Zero Retention (PoZR) receipt to settle downstream escrowed payments or reciprocal barter credits.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Picozzi, Google Case Managers and Athaley, Chaitanya, "Ephemeral Data Sharing for Artificial Intelligence Models", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12244