Hypothetical undergraduate submission, Spring Term 2028
1 Data‑Lease Manifest
{
"@context": "https://schema.org/",
"@type": "DataLease",
"lease_id": "DL-23-419B7",
"lessor": {
"@type": "Person",
"identifier": "did:key:z6Mk...A91"
},
"lessee": {
"@type": "SoftwareAgent",
"name": "DinnerNano v0.7"
},
"purpose": "Generate single meal plan for 2026‑04‑09 household dinner",
"data_scope": [
"child_alergy_profile",
"today_school_calendar",
"pantry_inventory_snapshot"
],
"ttl": "PT20M", // 20 minutes from grant
"datapath": "edge‑only",
"revocation_uri": "https://vault.local/revoke/DL-23-419B7",
"compensation": "royalty‑free",
"sig_less_or": "0x79e2c…",
"sig_lessee": "0xe810f…"
}Comment: Manifest serialized, signed with Ed25519; hashes written to the household proof‑of‑help ledger.
2 ZKP Compliance Essay (≈250 words)
Goal – prove that the nano‑service’s chosen dinner meets (1) each family member’s allergy constraints and (2) a 600–700 kcal target window without revealing the actual menu or the allergies.
I model the menu as a fixed‑length vector m ∈ ℤ₊⁵ (protein, carb, fat, fibre, misc) and the allergy vector a ∈ {0,1}ⁿ.
The circuit implements two predicates:
Σ mᵢ ≈ 650 kcal AND ∀ j ( aⱼ ⇒ ingredientⱼ = 0 ).
Using zk‑SNARKs (Groth16 on BLS12‑381) I compile the circuit; the proving key lives in the enclave, never leaves.
The nano‑service publishes:
π = Prove(pk, m, a)
h_menu = Poseidon(m) h_allergy = Poseidon(a)
Verifiers see only π, h_menu, h_allergy, and the public kcal bounds.
The SNARK guarantee means a malicious agent cannot fake compliance unless it solves the discrete‑log challenge.
After receipt is logged, the enclave zeroes m and a; only the hashes persist, so future analytics cannot reconstruct them.
Thus we satisfy “prove, then forget”: auditable nutrition without disclosing the recipe or the child’s medical data.
3 Joule‑per‑Inference Estimate
| Parameter | Phone (Tensor‑enabled SoC) | Cloud GPU (A100, shared) |
|---|---|---|
| Model size | 3 B params, int8 | 6 B params, fp16 |
| Runtime / inference | 45 ms | 12 ms |
| Power draw | 3.1 W (measured via Android Batterystats) | 180 W (GPU) + 40 W (host amort.) |
| Parallel batch | 1 | 256 simultaneous |
Calculations
- Phone Energy = 3.1 W × 0.045 s ≈ 0.14 J per inference.
- Cloud Server energy per inference = (220 W × 0.012 s)/256 ≈ 0.010 J. Network cost: 2 × 1500 B packet @ 0.2 µJ/B ≈ 0.6 mJ. Total cloud ≈ 0.0106 J.
Result
Phone inference = 0.14 J; cloud = 0.011 J.
Edge sovereignty costs ~13× more joules locally, but avoids data export—acceptable trade‑off for sensitive contexts.
4 Transcript — Funding Open‑Source Security in Zero‑Rent Conditions
Participants
Alex (Kernel Club President)
Jordan (Econ‑CS dual major)
Prof. Nguyen (Moderator)
Prof. N: We’ve lost ad revenue as a security piggy‑bank. Where do we find resources to audit nano‑service code?
Alex: Stake‑slash pools. Every agent must post an entropy‑token bond; if a CVE harms users, the bond funds the patch bounty. It’s skin‑in‑the‑game instead of budgets.
Jordan: But initial staking still costs money. Who fronts it if there’s no revenue?
Alex: Manufacturers and vault hosts—they benefit from a trustworthy ecosystem. Think of it as compulsory insurance baked into hardware price.
Jordan: I propose a complementary public‑good mint: each verifiable proof‑of‑help burns a micro‑fee in a deflationary utility token that accrues to a global security DAO. Essentially a self‑tax on successful agents.
Prof. N: Critique? Slashing discourages small developers; burn‑tax resembles the tragedy‑of‑the‑commons if everyone free‑rides.
Alex: Free‑rider risk is mitigated because the burn is protocol‑level—agents can’t opt out. Small devs post smaller stakes proportional to usage.
Jordan: And if usage is zero‑rent, token value derives from collective willingness to defend the commons, not speculative profit. No panic when margins vanish.
Prof. N: Consensus: hybrid pool—mandatory micro‑burn + proportional stake. Next semester we prototype treasury smart‑contracts and simulate actuarial viability with campus IoT data.
End of submission