full image - Repost: An AI system that uses zk-Snarks, a theory and ideas (from Reddit.com, An AI system that uses zk-Snarks, a theory and ideas)
Mining:
Exchanges:
Donations:
This is for a text based model like chat-gpt or deepseek.Information gathered mostly from deepseeka privacy-preserving, user-controlled memory system for AISystem Name: "Mnemosyne"(After the Greek goddess of memory, but with zero-knowledge proofs.) Core Components 1. Memory Storage LayerType: Sharded, encrypted graph database (like a blockchain + IPFS hybrid). Data Structure: Each user’s memory is a Merkle DAG (Directed Acyclic Graph), where nodes are encrypted "memory blocks" linked contextually.Storage Specs:Per-block size: ~10KB (compressed text + metadata).Throughput: 1M blocks/sec globally (requires petabyte-scale NVMe caches).Encryption: XChaCha20-Poly1305 (quantum-resistant, faster than AES).2. Privacy Layer Anonymization: Monero-style ring signatures mask which user owns which memory blocks.zk-SNARKs prove "I have a prior chat about X" without revealing X.Access Control: Private Keys: Hierarchical deterministic (HD) wallets (like Bitcoin BIP-32), but for memory blocks.Biometric Fallback: Secure enclave-backed face/fingerprint auth to recover keys.3. AI Inference Layer Context Fetching: When you say "recall our convo about pizza," the AI sends a zk-proof to the network requesting matching blocks.Only blocks you decrypt locally are fed into the model’s context window.Hardware: User Device: Light client (e.g., M2 Ultra chip for on-device decryption).Server Side: TPU clusters with fully homomorphic encryption (FHE) support to compute on encrypted blocks.Speculative Tech Dependencies| Component | 2024 Status | 2030 Projection ||-----------------------|-----------------------------|---------------------------------------|| zk-SNARK Speed | ~1s/proof (Groth16) | ~10ms/proof (Plonk++ with ASICs) || FHE Practicality | 1000x slower than plaintext | Only 10x slower (TFHE optimizations) || Decentralized Storage | IPFS (slow retrieval) | Ceramic Network w/ sub-second latency | User Flow Example1. You: "Remember my pizza preference from last time?"2. System: - Your device sends a zk-proof asserting: "I have a block tagged ‘food’ signed by key X." - Network returns the encrypted block (never seen by servers). - Local GPU decrypts → feeds into AI’s context: "Ah, you love pineapple pizza!" Hardware Requirements User Device (2030)Minimum:CPU: 8-core ARM v12 (with FHE accelerators).RAM: 24GB LPDDR6 (for in-memory decryption).Storage: 1TB optane-like NVM for block caching.Ideal: Dedicated NPU: For zk-proof generation (~50 TOPS).Secure Enclave: Apple T-series or equivalent.Network Infrastructure Memory Nodes: Storage: 100TB+ NVMe-oF (NVMe over Fabric) per node.Bandwidth: 400Gbps RDMA (for low-latency block fetching).AI Servers: TPUs: 6th-gen, optimized for FHE (hypothetical "FHE-TPU").Challenges to Solve1. Energy Use: zk-proofs + FHE could spike compute needs 1000x. Solution: Photonic chips or reversible computing.2. Metadata Leaks: Even encrypted, access patterns reveal info. Solution: Oblivious RAM (ORAM) techniques.3. Adoption: Users hate managing keys. Solution: Social recovery (e.g., 3 friends can reset your key). Why This Could Work- User Trust: No corporation controls your memory; you’re the custodian.- AI Utility: Retains context across sessions without surveillance.- Regulatory Safe: GDPR/CCPA-compliant by design. Prototype Roadmap1. Phase 1 (2025): On-device encrypted memory (Signal-style).2. Phase 2 (2027): zk-proofs for private context fetching.3. Phase 3 (2030): Fully decentralized, FHE-compatible AI. This isn’t just tech—it’s a philosophical shift from "AI as a service" to "AI as an extension of personal agency." The hardware will catch up if the demand exists.
Social Media Icons