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Privacy and cryptography.

theguardian.com2026-06-30Securityprivacyrel 9/10 score 7.8

US supreme court rules geofence warrants require constitutional privacy protections

The ruling establishes critical privacy protections for digital data under the Fourth Amendment, setting a precedent for how constitutional rights apply in the digital age.

  • Justice Elena Kagan wrote the majority opinion in Chatrie v US with a 6-3 decision against the government
  • Geofence warrants allow law enforcement to compel tech companies for cell phone location data from individuals within a virtual 'fence'
  • The court ruled that people aren't voluntarily sharing private information by using smartphones and apps that collect location data
Full summary

In Chatrie v US, the Supreme Court ruled that law enforcement's use of geofence warrants to access smartphone location data requires constitutional privacy protections under the Fourth Amendment. Justice Elena Kagan’s majority opinion held that individuals have a reasonable expectation of privacy in their cell phone location data, even if they are in public areas. The case focused on tracking an armed bank robber using Google’s optional 'location history' feature, and the court rejected the government's argument that accessing short-term cellphone location information does not constitute a Fourth Amendment search.

arxiv.org2026-06-30Securityprivacyrel 8/10 score 4.7

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation

MIMFlow offers a novel approach to integrating Masked Image Modeling with Normalizing Flows, potentially advancing the state-of-the-art in end-to-end image generation.

Details
  • Proposes MIMFlow as an end-to-end framework for latent semantics, pixel reconstruction, and generative flow
  • Achieves 71.3% linear probing accuracy on ImageNet 256x256 dataset
  • FID score of 2.50 on the same dataset

MIMFlow integrates Masked Image Modeling with Normalizing Flows to create an end-to-end framework for image generation, addressing the capacity bottleneck of NFs by focusing on high-level semantic structures while handling pixel details separately. This approach achieves a linear probing accuracy of 71.3% and an FID score of 2.50 on ImageNet 256x256 using only 128 tokens, outperforming similar-scale NF baselines by 32.8%. The framework demonstrates the potential to improve generative models' efficiency and performance.