Allele Frequency Count

# Security notes — `allele_frequency_count`Scoped to this bundle. The platform-wide threat model lives in`docs/manifesto.md`, `docs/requirements.md`, and `docs/simulation_mode.md` §5.Kerckhoffs applied to a product: **no guarantee rests on the secrecy — or thehonesty — of the server.** Don't trust, verify.## Trust classes (what may cross the wire)| class | example artifact | may leave the owner's machine? ||-------|------------------|-------------------------------|| RAW | `raw.json` genotypes | **no** || ENCODED | `encoded.json` dosage vector | **no** || PRIVATE | `secret_context.tenseal` (secret key), `plain.json` | **no, ever** || ENCRYPTED | `cipher.bin`, `result.bin` | yes || PUBLIC | `public_context.tenseal` | yes |Only ENCRYPTED and PUBLIC are ever uploaded. `00_keygen.py` writes the secret keyto `secret_context.tenseal`, which is used **only** by `40_decrypt.py` on theresearcher's machine. There is no `/api/v1` endpoint that accepts a secret key.## Server holds no secret key`30_compute_encrypted.py` — the only server-side stage, a kit shim that runs`server.py`'s `compute` — loads the **public** context plus ciphertexts andhomomorphically adds. It defensively refuses a context that carries a secret key(`context.is_private()` → error). The servertherefore never sees a single plaintext genotype; it operates on ciphertext andreturns ciphertext. Decryption happens only where the secret key lives: locally.## The append-1 sentinel is NOT a MACThe trailing sentinel slot decrypts to the exact contributor count N, anddropping one upload yields N−1 (test: `test_sentinel_tracks_dropped_upload`). Itcatches **mechanical corruption / miscounting** — it gives **no** guarantee thatcontributions are distinct, genuine, or non-Sybil. Call it what it is: anintegrity check, not authenticity.## What FHE here does and does not hide- **Hides:** individual genotype vectors from the server (inputs are ciphertext).- **Does not hide:** the released aggregate itself, and metadata (researcher  identity, participant count/timing, ciphertext sizes, protocol choice).- **Differencing (K vs K+1):** the *statistic* leaks an individual if you can  compute `A_{K+1} − A_K`. `aggregate_only` + `min_contributors ≥ 20` +  `allowed_runs_per_project: 1` (cohort freeze + min-N + run cap) **mitigate**  this; they are not a complete defense. Overlapping/Sybil differencing across  separately frozen cohorts needs DP + cross-job query budgets (v2). Documented,  not hand-waved — see `docs/simulation_mode.md` §5.- **Verify-by-re-execution is determinism, not zero-knowledge.** Re-running  `30_compute_encrypted.py` on the same ciphertexts reproduces a bit-identical  result digest; it proves the computation, it is not a ZK proof.## Exactness / parameter safetyBFV is exact in `Z_t`. The plaintext modulus must satisfy `t > max coordinatesum = 2·N`. The default `t = 1032193` (a 20-bit batching prime) stays exact for Nup to ~500k; a real run at larger N must raise `t` (or the simulation feasibilitysweep will report `infeasible-at-these-params` on overflow). The sentinel sum isN, always ≪ t.