Privacy-Preserving Operations on Spiral-Domain Encoded Time-Series States: Anonymization, Aggregation, and Federated Learning with Composition Rules and Streaming Variants

v2 update (2026-06-01): Reproducibility ZIP added back to the latest version alongside the manuscript files, so that downloading from the concept DOI gives all materials in one place rather than requiring navigation to v1. This revised deposit contains the Paper 7 manuscript (PDF and DOCX) along with the supplementary reproducibility archive. The manuscript files were added in this revised version of the deposit; the supplementary ZIP file remains unchanged from the original deposit. Manuscript targets IEEE Transactions on Information Forensics and Security (under preparation). Coverage. 28 pre-registered studies spanning Phases XII-XX of the spiral-domain encoder validation campaign (privacy primitives foundation, edge-case stress, deployment realism, hardware context, composition + streaming, reviewer preemption, Tier-3 strengthening, plus surgical-RT latency). 84 hypotheses, 65 SUPPORTED (77%), 8 honest bounded negatives substantively interpreted. Substantive findings. Three architectural privacy primitives uniquely enabled by spiral encoder mathematical structure: Primitive A (per-subject angular phase-shift anonymization, k=10 cross-subject anonymity) ; Primitive B (cross-subject mean aggregation, 8. 84 million-fold inversion resistance) ; Primitive C (federated AR (1) learning, exact 1-round convergence invariant to site count and heterogeneity). 5 deployment embodiments: multi-hospital clinical, multi-factory industrial, federated prosthesis fleet, surgical robotics RT privacy, cloud-scale parallel. Contents. Manuscript (PDF and DOCX of the paper itself) ; supplementary reproducibility archive containing: README. md (submission-package map and reproduction instructions) ; preregistrations/ (frozen pre-registration. md documents with literal-threshold decision rules) ; reports/ (per-study. md verdict reports against frozen rules + phase summaries) ; runners/ (deterministic Python runners under PYTHONHASHSEED=0) ; rawdata/ (per-study CSV outputs and JSON verdict blocks) ; figures/ (manuscript figures at 300 DPI + figure-build script) ; code/ (encoder source code). Reproducibility. Full validation pipeline is reproducible end-to-end under PYTHONHASHSEED=0 on a standard Python 3. 9+ installation with NumPy 2. 0+ and PyTorch 2. 8+ (required for the learned-adversary autoencoder attack of Study 86). Reference machine: Apple Silicon arm64 (M-series), macOS 14. See README. md for per-study run commands. Methodological discipline. Every hypothesis was pre-registered with externally anchored decision rules frozen prior to runner execution. Zero post-hoc threshold adjustments were applied. Honest bounded negatives are interpreted substantively rather than discarded. Related companion archives. Paper 1 (10. 5281/zenodo. 20129137), Paper 2 (10. 5281/zenodo. 20138786), Paper 3 (10. 5281/zenodo. 20139171), and the corresponding Papers 4, 5, 6, 8 archives in this same Zenodo collection. Paper 8 (10. 5281/zenodo. 20466035) extends Paper 7 Composition III to musculoskeletal-kinematic clinical digital twin deployment with 21 additional studies and 91 hypotheses validated across CMU Motion Capture and KIMORE rehabilitation datasets including patient populations.