PrivaC: Ultra-Fast Blind C Reverse Proxy & Symbolic Micro-Math Engine

PrivaC is a bare-metal, ultra-fast C reverse proxy designed for privacy-preserving, zero-knowledge LLM workflows. It intercepts inbound prompts, strips sensitive PII and raw figures into type-preserving surrogates and symbolic algebraic variables (VAR_1, VAR_2), and compiles mathematical expressions returned by remote LLMs (<<calc: ... >>) locally using an embedded micro-math VM (tinyexpr) over streaming Server-Sent Events (SSE) with sub-millisecond latency.

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🚀 Key Advantages

Capability	Standard Gateway / Python Proxy	PrivaC (Bare-Metal C)
End-to-End Latency	+150 ms to +400 ms	< 1.0 µs (Bare-metal C11)
Agent / JSON Schema Safe	❌ Fails on typed integer fields	✅ Yes (Type-preserving surrogates)
Math Accuracy	❌ LLM hallucinates calculations	✅ 100% Deterministic (Local C math VM)
Numeric Privacy	❌ Sends clear numbers to LLM	✅ Zero-Knowledge (Symbolic derivation)
Memory Footprint	~200–500 MB (Python/Node runtime)	< 15 MB static binary
Harness Compatibility	⚠️ Needs custom SDK extensions	✅ Drop-in (OpenAI base_url)

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🏛 Architecture

[Agent / SDK] ───> [PrivaC Proxy: Schema & Masking] ──(Blind Prompt)──> [Remote LLM Cloud]
                            │                                                   │
                      (Local Vault)                                             │ (Formula AST)
                    [mlock + Arena]                                             │
                            │                                                   ▼
[Agent / SDK] <─── [PrivaC Proxy: TinyExpr Math + SSE Stream FSM] <─────────────┘

The Hybrid Pipeline

1. Schema-Aware Type-Preserving Masking: Replaces names, emails, and entities with Entity_A, Entity_B, while converting monetary and numerical figures into symbolic variables (VAR_1, VAR_2).
2. Blind Symbolic Prompt Injection: Injects system-level directives forcing the remote LLM to reason abstractly and emit formula ASTs:

   [Original]: "Alice made $1,250,000 in revenue with $820,000 in costs. Net profit after 22% tax?"
   [Masked]:   "Entity_A has Revenue = VAR_1 and Costs = VAR_2. Derive profit with Tax = VAR_3. Return <<calc: expression>>."
   

3. Remote LLM Logic Compilation: The model generates:

   <<calc: (VAR_1 - VAR_2) * (1 - VAR_3)>>
   

4. Streaming SSE Delimiter FSM: Buffers split tokens across arbitrary chunk boundaries without breaking SSE framing or delaying Time-To-First-Token (TTFT).
5. Embedded Micro-Math VM: Evaluates the formula locally in $< 2\ \mu\text{s}$ using real session vault values, formatting $335,400.00 before forwarding the unmasked stream to the client.

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⚡ Performance Benchmarks

Measured on Apple Silicon / Modern Linux x86_64:

==================================================================
  PRIVAC: BLIND C PROXY & SYMBOLIC ENGINE - BENCHMARK SUITE
==================================================================

[1] Micro-Math VM Performance:
    Total Iterations: 100,000
    Average Latency:  0.777 µs / evaluation
    Throughput:       1,287,365.79 ops / second

[2] JSON Masking & Prompt Extraction Performance:
    Total Iterations: 20,000
    Average Latency:  0.900 µs / request
    Throughput:       1,111,419.84 reqs / second

==================================================================
  SUMMARY: Total proxy overhead is < 0.002 ms (< 2 microseconds)
==================================================================

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🔒 Security Hardening

• Page-Locked Vaults (mlock): Guarantees sensitive session keys and cleartext values reside strictly in RAM and are never swapped to unencrypted disk swap space.
• Defensive Zeroization (privac_secure_zero): Wipes memory regions immediately upon session termination to prevent compiler dead-store optimization.
• Region-Based Arena Allocation: Zero runtime malloc/free calls during request handling, preventing heap fragmentation and memory leaks.
• Strict Sanitizer Verification: Fully verified under -fsanitize=address,undefined with 0 leaks and 0 warnings.

---

📦 Getting Started

Prerequisites

• C11 compliant compiler (clang or gcc)
• make

1. Build

# Compile standard release binary
make

# Run all unit and integration tests
make test

# Run with AddressSanitizer and UndefinedBehaviorSanitizer
make sanitize

# Run latency and throughput benchmarks
make bench

2. Run the Proxy

# Run in standalone mock mode (offline demo with zero API keys required)
./bin/privac --port 8080 --mock

# Run as upstream forwarder to a local Ollama or vLLM instance
./bin/privac --port 8080 --upstream 127.0.0.1:11434

# Run with verbose token and masking logs
./bin/privac --port 8080 --mock -v

---

💡 Usage Examples

cURL

curl -N -X POST http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-session-id: sess_1001" \
  -d '{
    "model": "gpt-4",
    "messages": [
      {
        "role": "user",
        "content": "Alice made $1,250,000 in revenue with $820,000 in expenses. What is her net profit after a 22% tax?"
      }
    ],
    "stream": true
  }'

Python OpenAI SDK (Drop-in Replacement)

from openai import OpenAI

# Point client to local PrivaC Proxy
client = OpenAI(
    base_url="http://127.0.0.1:8080/v1",
    api_key="none"
)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "Alice made $1,250,000 in revenue with $820,000 in expenses. What is her net profit after a 22% tax?"}
    ],
    stream=True,
    extra_headers={"x-session-id": "user_session_42"}
)

for chunk in response:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
print()

---

📂 Project Structure

privacy_c/
├── include/
│   ├── arena.h          # Fixed-block / region bump memory allocator
│   ├── vault.h          # Page-locked session state & bidirectional map
│   ├── symbolic_vm.h    # TinyExpr compilation, evaluation, and formatting
│   ├── mask_engine.h    # SIMD PII regex + type-preserving surrogacy
│   ├── stream_fsm.h     # Chunk-split delimiter FSM with fallback flush
│   ├── sse_parser.h     # Zero-copy SSE framing and delta re-assembler
│   └── server.h         # Non-blocking HTTP reverse proxy & mock engine
├── src/
│   ├── main.c           # CLI entrypoint (--port, --upstream, --mock)
│   ├── arena.c
│   ├── vault.c
│   ├── symbolic_vm.c
│   ├── mask_engine.c
│   ├── stream_fsm.c
│   ├── sse_parser.c
│   └── server.c
├── deps/
│   ├── yyjson/          # Embedded SIMD JSON parser (yyjson.h, yyjson.c)
│   └── tinyexpr/        # Embedded Micro-Math VM (tinyexpr.h, tinyexpr.c)
├── tests/
│   ├── test_arena.c     # Arena bump allocator & bounds tests
│   ├── test_vault.c     # Session mappings, zeroization, and LRU eviction
│   ├── test_mask.c      # PII/number extraction and prompt rewriting
│   ├── test_math_vm.c   # Symbolic math evaluation and currency formatting
│   ├── test_stream_fsm.c# Split-token SSE streaming tests across chunk boundaries
│   ├── test_e2e_mock.c  # End-to-end proxy integration test
│   └── benchmark.c      # Microsecond latency benchmark suite
├── .github/workflows/
│   └── ci.yml           # Automated CI for Linux & macOS (Clang + GCC)
├── Makefile
├── .gitignore
└── README.md

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📄 License

MIT License.