Client-Side Ephemeral De-Identification of Protected Health Information (PHI) in Multi-Center Clinical Trial Analysis and Large Language Model Workflows: Validating Zero-Trust Data Sanitization Under HIPAA Safe Harbor Section 164.514(b)

Background The integration of frontier Large Language Models (LLMs) and Generative AI into biomedical research,clinical trial documentation, electronic health record (EHR) summarization, and diagnostic reasoningpresents a fundamental regulatory impasse under the Health Insurance Portability and Accountability Act(HIPAA). Clinical researchers and healthcare organizations are routinely constrained by the requirement forBusiness Associate Agreements (BAAs) and the risk of catastrophic Protected Health Information (PHI)exfiltration through cloud-hosted AI inference APIs, training pipelines, and multi-tenant telemetry caches. Methods We formulate, implement, and empirically validate an on-device, zero-trust architectural paradigm termed Zero-Trust Data Sanitization (ZTDS) . ZTDS intercepts unstructured clinical text, Case Report Forms(CRFs), lab reports, and physician consultation notes directly within the client's volatile memory (RAM) atkeystroke/document ingestion. It programmatically de-identifies all 18 statutory PHI categories definedunder 45 CFR §164.514(b)(2) (HIPAA Safe Harbor) using deterministic entity tokenization prior to TCP/IPpacket transmission to third-party AI APIs. A client-side volatile session dictionary enables lossless, 1-clicktoken re-identification of LLM output within the local session without persisting data to disk or cloudinfrastructure. Results In a benchmark evaluation across 2,500 synthetic multi-center clinical notes containing dense, co-occurring PHI entities (names, medical record numbers, dates, geographic subdivisions, biometricidentifiers, device identifiers, and contact vectors), the client-side ZTDS engine achieved a 99.94% recall rate on direct HIPAA 18 identifiers with an average processing latency of 1.84 ms per standard clinicalnote (mean length: 1,420 characters). In comparison, conventional cloud-based DLP proxy gatewaysincurred an average latency of 242.6 ms (a 131-fold latency reduction) while introducing server-sideintermediate processing liabilities. Network packet inspection confirmed zero outbound bytes of PHI under both connected and disconnected (Airplane Mode) network states. Discussion & Conclusion Under HHS regulations (45 CFR §164.502(d) and §164.514(a)), health information that has been de-identified according to Safe Harbor standards is no longer considered PHI and is exempt from HIPAArestrictions. By executing comprehensive de-identification entirely within volatile client RAM beforetransmission, the downstream AI service provider never handles PHI and is legally excluded from qualifyingas a Business Associate under 45 CFR §160.103. ZTDS offers an immediate, mathematically auditable, andzero-overhead pathway for hospitals, pharmaceutical research sponsors, and clinicians to securelyleverage public and commercial AI models without legal friction or regulatory non-compliance.