Daniel Reed

Professional Summary

Daniel Reed is a leading cryptographer and financial security expert specializing in privacy-preserving traceability technologies for Central Bank Digital Currencies (CBDCs). Bridging advanced cryptography with regulatory compliance, Daniel designs systems that balance anonymity for legitimate users with forensic capabilities for authorities—ensuring CBDCs meet anti-money laundering (AML) requirements without compromising fundamental privacy rights. His work defines the next generation of accountable digital cash.

Core Innovations & Technical Leadership

1. Selective Anonymity Frameworks

  • Develops zero-knowledge proof (ZKP) protocols enabling:

    • User-tiered privacy: Threshold-based identity revelation (e.g., >$10k transactions)

    • Time-delayed decryption: Law-enforcement access via cryptographic time-locks

    • Fuzzy tracing: Probabilistic linkage of suspicious transaction clusters

2. Regulatory-By-Design Architecture

  • Implements modular compliance layers for:

    • Travel Rule integration: FATF-compliant metadata exchange between institutions

    • Risk-based monitoring: Machine learning to flag dark market patterns (e.g., peel chains)

    • Audit trails: Immutable logs with selective redaction capabilities

3. Cross-Jurisdictional Solutions

  • Pioneers inter-CBDC forensic bridges allowing:

    • Cross-border transaction tracing under mutual legal assistance treaties (MLATs)

    • Privacy-preserving proof-of-sanctions-compliance

Career Milestones

  • Architected the traceability system for Digital Euro Phase 2, achieving 99.9% suspicious activity detection while preserving privacy for 98% of low-value transactions

  • Advised 7 central banks on CBDC Anonymity-Security Tradeoff Matrices

  • Patented a homomorphic encryption-based balance proof system

A hand wearing a glove is holding Canadian banknotes against a plain white background. The banknotes are vibrant and colorful, primarily featuring a green and blue design, with the denominations clearly visible as 20 and 5.
A hand wearing a glove is holding Canadian banknotes against a plain white background. The banknotes are vibrant and colorful, primarily featuring a green and blue design, with the denominations clearly visible as 20 and 5.

TheresearchrequiresGPT-4fine-tuningduetothecomplexityandspecificityofthe

datainvolved.WhileGPT-3.5iscapableofgeneral-purposetasks,GPT-4’senhanced

contextualunderstandingandlargerparametersetareessentialforprocessingnuanced

financialandregulatorydata.Fine-tuningwillallowthemodeltoaccuratelysimulate

CBDCscenarios,generatedetaileddesignrecommendations,andanalyzeethical

implications.PubliclyavailableGPT-3.5fine-tuninglackstheprecisionanddepth

neededforthisresearch,particularlyinhandlingtechnicaljargonandcomplex

trade-offs.GPT-4’sadvancedcapabilitiesensurethereliabilityandvalidityofthe

findings,makingitindispensableforthisstudy.

A monochrome photograph capturing a person wearing a long coat, with the coat partially open. The person is also wearing gloves and their face is not visible, adding an element of anonymity.
A monochrome photograph capturing a person wearing a long coat, with the coat partially open. The person is also wearing gloves and their face is not visible, adding an element of anonymity.

Aspartofthesubmission,Irecommendreviewingmypastworkontheintersectionof

AIandfinancialsystems,particularlymypapertitled“AI-DrivenInnovationsin

DigitalCurrency:ACaseStudyofBlockchain-BasedPaymentSystems”.Thisstudy

exploredtheuseofAItooptimizeblockchaintransactions,focusingonscalability

andsecurity.Additionally,myresearchon“EthicalImplicationsofAIinFinancial

Regulation”providesafoundationforunderstandingthesocietalimpactofAI-driven

financialinnovations.TheseworksdemonstratemyexpertiseinapplyingAItocomplex

financialsystemsandhighlightmyabilitytoconductrigorous,interdisciplinary

research.