Trump Orders States to Hand Over Medicaid, Food Assistance, and Voter Roll Data for Federal Surveillance

The Trump administration implemented its March 2025 executive order ‘Stopping Waste, Fraud and Abuse by Eliminating Information Silos,’ demanding that states hand over comprehensive data on Medicaid recipients, SNAP (food assistance) beneficiaries, and voter registration rolls to federal agencies. By August, at least 27 states had turned over sensitive data on millions of SNAP recipients including names, dates of birth, Social Security numbers, home addresses, and benefits amounts dating back to 2020. Federal health officials also shared Medicaid recipient data from multiple states with the Department of Homeland Security without notifying states or seeking consent.

Context: The executive order calls for federal government ‘unfettered access’ to data from state programs receiving federal funds. The New York Times characterized this as the ‘most aggressive bid yet’ for universal surveillance targeting immigrants and political opponents. DHS and DOGE began analyzing state voter rolls to identify noncitizens as part of Trump’s election-related directives. Democratic state officials filed multiple lawsuits arguing the data demands are unlawful, with a federal judge granting a preliminary injunction temporarily blocking the USDA’s SNAP data demands in some jurisdictions.

Significance: This systematic data centralization creates an unprecedented federal surveillance infrastructure combining healthcare, social services, and voter information to target vulnerable populations for deportation and political retaliation. The ‘fraud’ pretext masks the true purpose: building comprehensive databases to identify and punish immigrants, political opponents, and anyone receiving government assistance. This mirrors authoritarian surveillance states that weaponize social welfare programs and voter registration systems to control and intimidate populations, fundamentally transforming federalism into a centralized surveillance apparatus.