waLLLnut

PET 기반 기밀 연산으로
B2C 수요, B2B 리스크, 블록체인 인프라를 연결합니다

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

원본 데이터를 공유해야 풀리는 문제들이지만, 원본 공유는 불가능합니다

수요 예측, 보험·거래 리스크, 블록체인 상태 검증은 모두 민감한 신호에 의존합니다.
waLLLnut은 PET 기술로 원본은 숨기고 필요한 계산 결과만 사업 의사결정에 연결합니다.

A. 출시 수요와 물량은 아직 감에 의존합니다

팬덤·한정판·수집재 시장은 지불 의향과 실제 수요가 숨겨져 있어 가격, 생산량, 재고 의사결정이 흔들립니다.

B. 사기·이상 거래 데이터는 기관별로 끊겨 있습니다

보험 청구, 신원, 계좌, 거래 패턴은 교차 검증이 필요하지만 개인정보와 규제로 원본 이동이 어렵습니다.

Blockchain confidential infrastructure visual

C. 공개 원장은 민감 업무 확장을 막습니다

온체인 애플리케이션은 검증성은 필요하지만 입찰, 신용, 거래 조건, 상태 값까지 공개되면 실사용이 제한됩니다.

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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 PET engine is being packaged into three commercial tracks.

The company starts with a consumer demand-intelligence wedge, expands into enterprise risk workflows where raw data cannot move, and turns the same encrypted computation core into blockchain confidentiality infrastructure.

Entry wedge A. B2C demand

Launch status 2026 Coming Soon

Core engine FHE16 + MPC

Data rule Raw data stays private

Track

Customer

Product output

Revenue path

Public status

A B2C Demand Forecasting & Allocation

Customer

Brands, creators, limited-drop sellers

Product output

Private demand survey, willingness-to-pay analysis, pricing and allocation report

Revenue path

Report fee, transaction fee, premium seller tools

Public status

2026 Coming Soon

B B2B PET Risk Intelligence

Customer

Insurers, financial institutions, platforms

Product output

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

Revenue path

PoC design, then per-check or subscription pricing after validation

Public status

Use-case validation track

C Blockchain Confidential Infrastructure

Customer

Chains, applications, infrastructure teams

Product output

Encrypted state, confidential execution, threshold disclosure, public verifiability

Revenue path

Infrastructure integration, SDK support, partner deployment

Public status

Infrastructure track

03. Business Tracks

B2C Demand Forecasting & Allocation

Convert private willingness-to-pay and demand signals into launch quantity, pricing, and allocation decisions. Product name undisclosed.

2026 Coming Soon

04. Technology Moat

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