The U.S. may finally get a federal privacy law to rival Europe’s GDPR

Meet APRA, which—if it passes—would be the mythical federal privacy law that Americans want and deserve.

Fortune

M. de Arruda Botelho Herr et al., "Bringing the Algorithms to the Data -- Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC)"¹

The need for data privacy and security -- enforced through increasingly strict data protection regulations -- renders the use of healthcare data for machine learning difficult. In particular, the transfer of data between different hospitals is often not permissible and thus cross-site pooling of data not an option. The Personal Health Train (PHT) paradigm proposed within the GO-FAIR initiative implements an 'algorithm to the data' paradigm that ensures that distributed data can be accessed for analysis without transferring any sensitive data. We present PHT-meDIC, a productively deployed open-source implementation of the PHT concept. Containerization allows us to easily deploy even complex data analysis pipelines (e.g, genomics, image analysis) across multiple sites in a secure and scalable manner. We discuss the underlying technological concepts, security models, and governance processes. The implementation has been successfully applied to distributed analyses of large-scale data, including applications of deep neural networks to medical image data.

#arXiv #ResearchPapers #MedicalData #privacy4

__
¹ https://arxiv.org/abs/2212.03481

Bringing the Algorithms to the Data -- Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC)

The need for data privacy and security -- enforced through increasingly strict data protection regulations -- renders the use of healthcare data for machine learning difficult. In particular, the transfer of data between different hospitals is often not permissible and thus cross-site pooling of data not an option. The Personal Health Train (PHT) paradigm proposed within the GO-FAIR initiative implements an 'algorithm to the data' paradigm that ensures that distributed data can be accessed for analysis without transferring any sensitive data. We present PHT-meDIC, a productively deployed open-source implementation of the PHT concept. Containerization allows us to easily deploy even complex data analysis pipelines (e.g, genomics, image analysis) across multiple sites in a secure and scalable manner. We discuss the underlying technological concepts, security models, and governance processes. The implementation has been successfully applied to distributed analyses of large-scale data, including applications of deep neural networks to medical image data.

arXiv.org

44 bits

So, a redditor tracked down the location of a monolith placed in the Utah desert a few years ago, recently discovered by authorities, who did not disclose where it was.[1]

It's relatively well known that 33 distinct bits is enough to uniquely identify any individual person now alive on Earth.[2]

Geospatially, assuming 10m2 resolution, 44 bits is enough to identify any unique region on Earth's land surface (46 bits buys you the oceans).

Searching for a ~1m2 monolith visually within a 10m2 square is reasonable.

GNU units:

You have: ln((.3 * 4 * (earthradius^2) * pi)/10m^2)/ln(2)
Definition: 43.798784
You have: ln((1 * 4 * (earthradius^2) * pi)/10m^2)/ln(2)
Definition: 45.535749

49 bits buys 1m accuracy, 63 1cm, 69 1mm. Anywhere on Earth, land or sea.

For comparison, cellphone positioning accuracy is typically 8--600m:

  • 3G iPhone w/ A-GPS ~ 8 meters
  • 3G iPhone w/ wifi ~ 74 meters
  • 3G iPhone w/ Cellular positioning ~ 600 meters

https://communityhealthmaps.nlm.nih.gov/2014/07/07/how-accurate-is-the-gps-on-my-smart-phone-part-2/

https://www.gps.gov/systems/gps/performance/accuracy/

The power of disparate data traces to rapidly narrow down search spaces on a specific item, individual, or location, is what makes #BigData aggreggation so powerful, and terrifying.

Notes:

  • https://old.reddit.com/r/geoguessr/comments/jzw628/help_me_find_this_obelisk_in_remote_utah/gdfbzee/ https://news.ycombinator.com/item?id=25199879

  • https://web.archive.org/web/20160304012305/33bits.org/about/

  • #privacy4 #location #33bits #44bits #data #deanonimization #DataAreLiability #surveillance #SurveillanceState #SurveillanceCapitalism

    How Accurate is the GPS on my Smart Phone? (Part 2)

    Community Health Maps
    We just released CryptPad 3.6.0, named after the Panamanian golden frog. This release features a number of bug fixes and another batch of usability improvements. Read the full release notes on GitHub (https://github.com/xwiki-labs/cryptpad/releases/tag/3.6.0) and try it out on https://CryptPad.fr ! #privacy4
    xwiki-labs/cryptpad

    The Encrypted Collaboration Suite. Contribute to xwiki-labs/cryptpad development by creating an account on GitHub.