waLLLnut

가장 민감한 데이터를 그대로 활용합니다
— 원본은 한 번도 드러내지 않고

A B2C Demand Forecasting 비공개 수요 조사 · 물량/가격 리포트 · 2026 Coming Soon

B B2B PET Risk Intelligence 신원 이상 · 보험/거래 리스크 · PET 기반 FDS

C Blockchain Confidential Infrastructure 암호화 상태 · 검증 가능한 실행 · 개발자 인프라

01. Market Problem

민감 데이터 때문에 멈춘 의사결정을, 원본 공개 없이 계산 가능한 사업 신호로 바꿉니다

PET는 회사들이 원본 데이터를 서로 보여주지 않고도 필요한 계산만 함께 수행하게 합니다.
waLLLnut은 이 엔진을 수요 예측, 리스크 검증, 블록체인 기밀 인프라로 제품화합니다.

A. 셀러는 수요가 보이기 전에 가격과 출시 물량을 정해야 합니다

한정판·팬덤·수집재 시장은 지불 의향이 늦게 드러나 과잉 재고, 과소 생산, 가격 실패가 반복됩니다.

B. 금융·보험 리스크 신호는 기관 밖으로 나가기 어렵습니다

보험 청구, 신원, 계좌, 거래 패턴은 함께 보면 더 정확하지만 개인정보와 규제로 원본 공유가 어렵습니다.

Blockchain confidential infrastructure visual

C. 블록체인은 검증성은 강하지만 민감 업무에는 너무 많이 보입니다

많은 실제 애플리케이션은 검증성과 기밀성이 함께 필요합니다. 입찰, 신용, 조건, 상태 값이 모두 공개될 수는 없습니다.

Talk to Us

One PET Engine, Three Business Tracks

waLLLnut applies FHE16, MPC, and threshold disclosure to build a staged business:
consumer demand intelligence first, enterprise PET risk validation next, and blockchain confidential infrastructure as the scalable layer.

One privacy engine, three products.

The same FHE16 engine powers all three — consumer demand forecasting today, enterprise risk checks next, and on-chain confidentiality as it scales.

Where we start A. B2C demand

Launch status 2026 Coming Soon

Core technology FHE16 runtime + MPC key layer

What we guarantee Raw data does not leave owner

Public validation Solana 3rd · Mantle final-five voting stage · Public GitHub source

Track

Customer

Product output

Revenue path

Public status

A B2C Demand Forecasting & Allocation

Customer

Limited-drop sellers, creators, IP and brand teams

Product output

Paid demand report: expected demand, price range, launch quantity, inventory risk

Revenue path

Report fee first; seller tools, transaction fee, premium analytics later

Public status

2026 Coming Soon

B B2B PET Risk Intelligence

Customer

Insurers, financial institutions, platforms with fraud exposure

Product output

Private cross-check for claims, identity mismatch, FDS, and anomalous transactions

Revenue path

Use-case design first; subscription or per-check pricing after validation

Public status

Use-case validation track

C Blockchain Confidential Infrastructure

Customer

Chains, dApps, and infrastructure teams needing confidentiality

Product output

Encrypted state, confidential execution, threshold disclosure modules

Revenue path

SDK, integration support, and infrastructure modules

Public status

Solana 3rd · Mantle final-five voting stage

03. FHE16 Solutions

FHE16 deterministic homomorphic encryption visual

FHE16 Deterministic Homomorphic Runtime

Fully homomorphic encryption lets software compute on encrypted values while plaintext stays hidden. FHE16 defines a deterministic 16-bit integer execution spec so compatible implementations return the same result across browser, server, GPU, FPGA, and Python bindings.

LibFHE16Python-compatible · Device-free deterministic output

04. Technology Moat

PET Primer

PET keeps sensitive data useful without making it visible.

TEE is the easiest entry point because teams can move familiar code into secure hardware. Cryptographic PET asks harder engineering questions, but it reduces the need to trust a machine operator or hardware boundary.

TEEfast entry, hardware trust

Raw data enters enclaveNormal code computesResult exits

Low barrier and fast to prototype. Because many teams can adopt it, long-term differentiation comes from policy, deployment, and trust management rather than the primitive itself.

PETcryptographic privacy

Data stays privateProtocol computes or provesOnly allowed output is revealed

MPC, PSI, PIR, ZK, FHE, and iO sit in this family. They are harder to build, but the security story is closer to math and protocol design than to a trusted box.

MPC

Joint computation

Several parties compute one result without exposing each party's input.

PSI

Private matching

Find overlap between datasets without revealing non-matching records.

PIR

Private retrieval

A user reads one item without revealing which item was requested.

Proof without disclosure

Prove a statement is true without revealing the underlying secret data.

FHE

Compute while encrypted

Hard to implement, but optimized systems can move parts of FHE into practical latency and throughput ranges.

Hide the program

The most powerful idea conceptually, but still not practical for product workloads.

Low implementation barrierHarder cryptographic engineering

TEEeasy to start

PSI / PIRnarrow, usable protocols

MPC / ZKpowerful, workflow-specific

FHEpractical when engineered down

iOresearch-stage

FHE16

A 16-bit integer computation–centric deterministic FHE structure eliminating floating-point operations and ensuring identical results regardless of the execution environment

Key Features

Elimination of floating-point operations, ensuring identical results regardless of execution environments
Elimination of floating-point environment-specific error issues
Ultra-fast 2.89 ms bootstrapping through GINX gate optimization (suitable for real-time and low-latency applications)

Reference: ePrint 2024/1916

FHE16-based MPC (SSFHE)

An efficient MPC that combines FHE16 and CRT-SPDZ, utilizing composite numbers of 16-bit primes as modulus values and effective sampling.

Key Features

O(1) complexity for each of communication, computation, rounds and input size
O(n κ d²) complexity for generation of evaluation key (ev)
CRT-SPDZ–based secure random number and distribution sampling (including Discrete Gaussian)
Circuit Privacy with active security in a dishonest majority setting

Reference: ePrint 2024/1916

View Details

05. Competitive Position

Why FHE16 is the common engine behind A/B/C

Feature	FHE16	Standard Analytics	MPC / PSI	ZK Proofs	TEE
Raw-data minimization	Encrypted by design	Data exposed	Strong, workflow-specific	Proves statements	Hardware trust
Encrypted comparison and scoring	Core focus	Plaintext only	Possible with coordination	Heavy for live scoring	Fast, trust-dependent
Browser / client execution path	FHE16-WASM path	Easy	Depends on protocol	Prover cost	Hardware-bound
Enterprise validation path	TEE -> PSI -> FHE path	Easy but privacy-limited	Good for matching	Audit-specific	Short-term bridge
Blockchain confidentiality fit	Confidential state and execution	Public by default	Coordination cost	Verification layer	Off-chain trust layer
Cross-market reuse	A/B/C shared engine	Siloed systems	Use-case specific	Proof circuits per use case	Vendor-specific

View Details

06. Team

Research-grade PET execution team
turning FHE16 into consumer, enterprise,
and blockchain business tracks.

Seunghwan Lee (CEO)
이승환
Ph.D. in Electronic Engineering, Hanyang University; Leads FHE16 R&D
한양대 전자공학 박사, FHE16 연구·개발 총괄
Dohyuk Kim (CTO)
김도혁
Leads FHE16 and MPC Implementation
한양대 전자공학 박사과정, FHE 상용화·납품 개발 경험
Dong-Joon Shin (CSO)
신동준
Establishes Academic-Industry Strategies
한양대 전자공학 교수, 학계·산업 네트워킹 보유
KiIn Shin
신기인
Marketing Manager and Graphic Designer
마케팅 매니저 · 그래픽 디자이너
JiIn Shin
신지인
PR Manager and UIUX Designer
PR 매니저 · UI/UX 디자이너