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.

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A. 출시 수요와 물량은 아직 감에 의존합니다

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

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B. 사기·이상 거래 데이터는 기관별로 끊겨 있습니다

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

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C. 공개 원장은 민감 업무 확장을 막습니다

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

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02. Executive Summary

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 pilots next, and blockchain confidential infrastructure as the scalable layer.

waLLLnut builds a PET business around markets where useful data cannot be exposed. The first wedge is B2C demand forecasting and allocation; the second is B2B identity, fraud, and anomalous transaction risk; the third is confidential blockchain infrastructure.

Consumer teams need private demand signals before they commit inventory; financial and insurance teams need cross-organization risk checks without moving raw data; blockchain applications need confidentiality without giving up verification. One PET engine can serve all three with staged go-to-market risk.

A. B2C Demand Forecasting & Allocation — private demand survey, pricing/allocation report, 2026 Coming Soon.

B. B2B PET Risk Intelligence — insurance claim cross-check, FDS, identity and anomalous transaction detection without raw data exposure.

C. Blockchain Confidential Infrastructure — encrypted state, confidential execution, threshold disclosure, and public verifiability.

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

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 pilot 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

Portfolio Strategy

  • A creates the first market wedge: privacy-preserving demand reports for consumer launches and allocation planning.
  • B converts the same PET engine into enterprise pilots for insurance claim review, identity risk, FDS, and anomalous transaction workflows.
  • C packages the engine as confidential blockchain infrastructure for encrypted state, threshold disclosure, and verifiable execution.
Business Model & GTM

  • A. Consumer Demand Intelligence

    Closed demand survey, private willingness-to-pay analysis, and launch quantity reports. Revenue path: report fee, transaction fee, premium seller tools, and later authenticity/fulfillment modules. 2026 Coming Soon.

  • B. Enterprise Risk Intelligence

    Insurance claim cross-validation, FDS, identity mismatch, and anomalous transaction screening without moving raw customer data. GTM path: PoC, paid pilot, subscription or per-check pricing.

  • C. Confidential Chain Infrastructure

    Developer-facing modules for confidential state, encrypted execution, and threshold disclosure. Revenue path: infrastructure integration, SDK support, and partner deployments.

Commercial Milestones

2026 Ongoing

A. B2C demand forecasting and allocation launch
Closed survey, report MVP, creator/seller onboarding

2026

B. PET risk intelligence PoC and pilots
Insurance claim cross-check, FDS, identity and anomaly workflows

2027+

C. Confidential blockchain infrastructure expansion
SVM/EVM modules, threshold disclosure, developer docs, partner integration

07. Team

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

08. Ecosystem

Research, pilots, and technical collaboration for PET commercialization

09. Developer Infrastructure

Start building confidential workflows with FHE16

GitHub

Explore implementation references and research code

Documentation

Technical guides for PET and confidential computation pilots

 code

SDK

Libraries and integration modules under staged release

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Playground

Browser execution path for encrypted comparison and demos

10. Roadmap

Three-year path from market wedge to PET infrastructure

2024

Research Foundation

FHE16 research foundation and browser-executable encrypted computation path established

2025

Company and Product Architecture

FHE16, SSFHE, and threshold disclosure unified into one product engine

2026

A. B2C Coming Soon

Demand forecasting, private survey, report MVP, and allocation workflow prepared for launch

2026

B. Enterprise PET Pilots

Insurance claim cross-validation, FDS, identity, anomaly, and audit workflows

2027

C. Blockchain Modules

Confidential state layers, threshold disclosure, SDKs, and developer-facing technical docs

2028

Cross-market PET Platform

Unified infrastructure spanning consumer launch intelligence, enterprise risk, and blockchain confidentiality

11. Research

Publications & Academic Collaboration

waLLLnut의 FHE16 기술은 한양대학교 Coding & Communication Research Lab(CCRL)와의 긴밀한 산학협력을 통해 개발되었습니다.

Hanyang University

Coding & Communication Research Lab

Prof. Dong-Joon Shin (신동준 교수님)

Visit Lab open_in_new

IACR ePrint Archive Publications

2026
Exact Error Analysis for Blind Rotation in Fully Homomorphic Encryption

Sin Kim, Seunghwan Lee, Dohyuk Kim, Dong-Joon Shin

ePrint 2026/269 2025
Low-Latency Fully Homomorphic Arithmetic Using Parallel Prefix Group Circuit

Dohyuk Kim, Sin Kim, Seunghwan Lee, Dong-Joon Shin

ePrint 2025/2150 2025 CRYPTO 2025
Actively Secure MPC in the Dishonest Majority Setting

Seunghwan Lee, Jaesang Noh, Taejeong Kim, Dohyuk Kim, Dong-Joon Shin

ePrint 2025/810 2024 FHE16
Fast, Compact and Hardware-Friendly Bootstrapping in less than 3ms

Seunghwan Lee, Dohyuk Kim, Dong-Joon Shin

ePrint 2024/1916 2022
Overflow-detectable Floating-point Fully Homomorphic Encryption

Seunghwan Lee, Dong-Joon Shin

ePrint 2022/186

12. Press & Media

In the News

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