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[…] and that advance practical de-identification and support the utility of data. Future of Privacy Forum 34 De-Identification Highlights of our work in 2016: FPF’s framework described in Shades of Gray: Seeing the Full Spectrum of Practical Data De- Identification, was published in the Santa Clara Law Review and FPF also created an accompanying Visual […]

[…] continuing to develop models that improve transparency and terminology around de- identification, and that advance practical de-identification and support the utility of data. FPF’s framework described in Shades of Gray: Seeing the Full Spectrum of Practical Data De- Identification, was published in the Santa Clara Law Review and FPF also created an accompanying Visual […]

[…] based on the nature of data that is collected, the risks of re-identification, and the legal and administrative protections that may be applied. FPF’s framework, described in Shades of Gray: Seeing the Full Spectrum of Practical Data De-Identification, was published in the Santa Clara Law Review. Ethics: FPF is calling for new frameworks and […]

[…] applied. FPF is continuing to develop models that improve transparency and terminology around de-identification and that advance practical de-identification measures. Highlights Include: • FPF’s framework described in Shades of Gray: Seeing the Full Spectrum of Practical Data De-Identification, was published in the Santa Clara Law Review; • FPF created a Visual Guide to Practical […]

[…] The Guide is available at fpf.org/consumerguide Comments written in response to NHTSA’s Request for Comment on the Federal Automated Vehicles Policy, available at: https://fpf.org/2016/11/22/fpf-submits-comments-on-nhtsas-federal-automated-vehicles-policy/ An academic paper, Shades of Gray: Seeing the Full Spectrum of Practical Data De-identification, which suggests that rather than treat personal data as a black or white dichotomy, policymakers should […]

[…] based on the nature of data that is collected, the risks of re-identification, and the legal and administrative protections that may be applied. FPF’s framework, described in Shades of Gray: Seeing the Full Spectrum of Practical Data De-Identification, was published in the Santa Clara Law Review. Ethics: FPF is calling for new frameworks and […]

[…] based on the nature of data that is collected, the risks of re-identification, and the legal and administrative protections that may be applied. FPF’s framework, described in Shades of Gray: Seeing the Full Spectrum of Practical Data De-Identification, was published in the Santa Clara Law Review. Ethics: FPF is calling for new frameworks and […]

[…] applied. FPF is continuing to develop models that improve transparency and terminology around de-identification and that advance practical de-identification measures. Highlights Include: • FPF’s framework described in Shades of Gray: Seeing the Full Spectrum of Practical Data De-Identification, was published in the Santa Clara Law Review; • FPF created a Visual Guide to Practical […]

[…] -content/uploads/2016/11/Rubinstein_framing -paper.pdf (last time visited on 9 March 2017). 15 For an analysis of the spectrum of identifiability, see J. Polonetsky, O. Tene and K. Finch , Shades of Gray: Seeing the full spectrum of practical data de -identification, in Santa Clara Law Review, vol. 56, 2016. 4 asserts that, notwithstanding the first one, […]

[…] a dataset to the original identity of a data subject, and is therefore merely a “useful security me asure.” 9 Jules Polonetsky, Omer Tene, & Kelsey Finch, Shades of Gray: Seeing the Full Spectrum of Practical Data De – Identification , SANTA CLARA L. REV . (forthcoming 2016). 10 See, e.g. , Khaled El […]