Privacy-Preserving Financial Surveillance: An Architectural Framework for CBDC Implementation

Abstract Current CBDC proposals assume comprehensive transaction surveillance is necessary for financial stability and crime prevention. This paper challenges that assumption by proposing an alternative architecture achieving 87–95% of surveillance-based detection effectiveness while preserving complete transactional privacy. Key Contributions Privacy-preserving detection: 87–95% effectiveness without surveillance infrastructure Watchlist access is worthless: Provides zero marginal detection improvement beyond pseudonymous linking Three mechanisms: Anonymized pattern detection, transaction-level intervention, opt-in deanonymization Inverted burden of proof: System must demonstrate suspicion before intervention; users can abandon flagged transactions without identity exposure Game-theoretic deterrent: Illicit actors cannot complete transactions; legitimate users experience minimal friction Links ASCRI: systems.ac/2/DAI-2511 Research Lab: Dissensus AI