Are any of these methods viable?
I asked AI (Gemini) how to avoid ( if possible ) exposing the pubkey during a spend
and here are the results:
To achieve your goal of moving funds without exposing the underlying
public key during the spend transaction, you must leverage advanced
script structures or cryptographic wrappers:
Bitcoin Transaction Types to Conceal Public Keys During a Spend
P2SH (Pay-to-Script-Hash):
Funds are sent to a 20-byte hash of a script.
To spend them, you provide the full script (redeemScript).
If the script uses an asymmetric opcode setup or a custom
condition that does not explicitly include a public key
(e.g., using hash locks), the public key is never revealed.
P2WSH (Pay-to-Witness-Script-Hash):
This is the SegWit equivalent of P2SH. It uses a 32-byte hash of a
script. Like P2SH, if the underlying witness script relies on non-
pubkey conditions, the public key remains unexposed during a
spend.
P2TR (Pay-to-Taproot - Script Path):
Taproot allows spending via a key path or a script path.
If you spend via the script path (using a Merkle Abstract Syntax
Tree), you only reveal the specific script branch being executed.
The other branches, along with any public keys buried inside them,
remain completely hidden as unrevealed hashes.Methods to Verify
and Move Funds Without Revealing a Public KeyTo safely validate
ownership and move Bitcoin without revealing a public key during
the spending transaction, developers utilize specific locking
mechanisms inside P2SH, P2WSH, or P2TR scripts called Hash Locks.
Hash Locks
(Preimage Verification):
You can lock Bitcoin using the opcode sequences OP_SHA256
OP_EQUAL or OP_HASH160 OP_EQUAL. To spend the funds, the
spender only provides the secret "preimage" (a random string of data)
that hashes to the target value. No public key or cryptographic
signature is ever used or revealed.
Zero-Knowledge Proofs (ZKPs):
While native ZKPs are not fully supported on the Bitcoin mainnet base layer,
specialized script paths can verify computational state transitions or
zero-knowledge-like preimages. This allows validation of a claim without
disclosing the underlying key infrastructure.Lamport Signatures
(Quantum-Resistant One-Time Signatures):
You can construct a custom Bitcoin script that mimics a Lamport signature.
This setup relies entirely on hash functions. The spending transaction
verifies the signature by hashing the input data and comparing it to the
state hashes, bypassing traditional ECDSA/Schnorr public keys entirely.
Covert Consolidation (Dust Attack Defection):
If Address A has a known low-entropy private key (e.g., a weak brainwallet or a known faulty key generation tool), it is a high-risk asset. Automated bots running the Pollard's Kangaroo algorithm monitor the blockchain for these specific weak keys and will instantly sweep them the second they see the public key in the mempool.To defeat a front-running bot, you must prevent it from having enough time to calculate the private key. You can achieve this by artificially inflating the transaction fee using Address B's funds. This technique is called a Child-Pays-For-Parent (CPFP) bundle or a high-fee flash sweep.By sending a small amount of Bitcoin ("dust") from Address B to Address A, you can craft a transaction that pays an extreme miner fee, ensuring a miner picks it up in the very next block before a bot can process the Kangaroo algorithm.
Conclusion: To move Bitcoin funds without exposing a public key
during the spend, you must use Pay-to-Script-Hash (P2SH),
Pay-to-Witness-Script-Hash (P2WSH), or Pay-to-Taproot (P2TR)
script paths combined with Hash Locks (preimages) instead of traditional
digital signatures.
People over-sensualize AI or anthropomorphize robots they are fundamentally just probability and pattern-matching machines.
Counting & Math: Current AI models (Large Language Models) process words as chunks called "tokens," rather than viewing a string of characters individually. When it sees a long string of zeros, it is guessing based on statistical averages rather than performing logical calculations. This is why simple tasks like counting letters, often result in wild estimations
Physical Reasoning: AI lacks "common sense" and a conceptual understanding of physics. While an LLM can regurgitate a recipe, a physical robot struggles to identify differences.
The hype portrays robots as flawless, but the underlying architecture heavily relies on humans to do the actual critical thinking and maintenance
