Extrapolation: Shingle Algorithm – Who & Why Who: NYSBOE and contractors (Saber Corp primary) during 2005–2007 HAVA-mandated NYSVoter rollout. Structured SBOEIDs were batch-generated at state level while migrating legacy county data. Why (most insightful facts): 1. Benign view: Scalable ID assignment in rushed centralization/deduping of millions of purged records – likely batch-logic artifact for partitioning or audit controls. 2. Researchers' view: Patterns far too precise (99%+ purged/clone correlation) and non-random to be accidental. Functions as hidden steganographic tagging, creating a "shadow" pool of structured records (born purged, mismatched dates) that could be reactivated independently. Similar algorithmic designs found in multiple states. No NYSBOE explanation exists; structure verifiable in public voter files. Bottom line: Deliberately engineered into the HAVA database architecture. Efficiency vs. covert classification remains the open question.
Extrapolation: Shingle Algorithm – Who & Why Who: NYSBOE and contractors (Saber Corp primary) during 2005–2007 HAVA-mandated NYSVoter rollout. Structured SBOEIDs were batch-generated at state level while migrating legacy county data. Why (most insightful facts): 1. Benign view: Scalable ID assignment in rushed centralization/deduping of millions of purged records – likely batch-logic artifact for partitioning or audit controls. 2. Researchers' view: Patterns far too precise (99%+ purged/clone correlation) and non-random to be accidental. Functions as hidden steganographic tagging, creating a "shadow" pool of structured records (born purged, mismatched dates) that could be reactivated independently. Similar algorithmic designs found in multiple states. No NYSBOE explanation exists; structure verifiable in public voter files. Bottom line: Deliberately engineered into the HAVA database architecture. Efficiency vs. covert classification remains the open question.