Emergency Relief Sought to Protect Upcoming Elections From Plot to Facilitate Illegal & Inaccurate Voter Roll Purge

[Quiet Em]

With elections rapidly approaching in locations around the nation, the League of Women Voters, along with three individual plaintiffs, asked a court for emergency relief in a class action lawsuit that seeks to stop the Trump-Vance administration’s unlawful creation of massive government databases consolidating sensitive and legally protected personal information on millions of people in America to unlawfully open investigations and purge voter rolls. Today’s emergency filing follows last week’s class action lawsuit, filed on behalf of the League of Women Voters, League of Women Voters of Virginia, League of Women Voters of Louisiana, and Electronic Privacy Information Center (EPIC), along with five individual plaintiffs. The coalition is represented by Democracy Forward Foundation, Citizens for Responsibility and Ethics in Washington (CREW), and Fair Elections Center.

The request for a preliminary injunction, filed in the U.S. District Court for the District of Columbia, details how the so-called “Department of Government Efficiency” (DOGE), the U.S. Department of Homeland Security (DHS), Social Security Administration (SSA), and other agencies have secretly merged personal data from across the federal government into centralized “Interagency Databases” in direct violation of the Privacy Act of 1974 and the U.S. Constitution.

The complaint alleges that the administration has unlawfully transformed the DHS’s Systematic Alien Verification for Entitlements (SAVE) system into a national citizenship database pooling SSA data known to be unreliable, now being used by some states to purge voter rolls and open criminal investigations. The filing seeks to prevent voters from being unlawfully purged and targeted by this broken and illegal system….

https://electionlawblog.org/?p=152403

[yellow dahlia]

1. This is so important to pay attention to.

Violating the Privacy Act of 1974 and harvesting our data.

Thank you for sharing.