June 2018 Privacy Speaker Series Flier
Perspectives on Privacy and the Internet of Things PRIVACY IN ACTION SPEAKER SERIES EVENT Attendees are eligible to receive Continuing Privacy Education credits. VA employees are also eligible to receive Talent Management System credits. Understanding Internet of Things (IoT) and Privacy Reviewing privacy-related IoT issues Protecting data in IoT age VA Privacy Service Virtual Panel […]
Democracy human rights and the rule of law by design for artificial intelligence Correction Finalebis PN (4)
DEMOCRACY , HUMAN RIGHTS, AND THE RULE OF LAW BY DESIGN FOR ARTIFICIAL INTELLIGENCE BY PAUL NEMITZ , PRINCIPAL ADVISER, EUROPEAN COMMISSION @paulnemitz #AI Y A H O O F I N A N C E , F A C E B O O K I N C . , A P R I L […]
2018_0504 FPF CPUC Reply Comments
BEFORE THE PUBLIC UTILITIES COMMISSION OF THE STATE OF CALIFORNIA Order Instituting Rulemaking on Regulations Relating to Passenger Carriers, Ridesharing, and New Online -Enabled Transportation Services R.12 -12 -011 (Filed December 20, 2012) REPLY COMMENTS OF THE FUTURE OF PRIVACY FORUM ON THE COMMISSION’S DECISION AUTHORIZING AUTONOMO US VEHICLE PASSENGER SERVICE WITH DRIVER S AND […]
MLA-Code-v2-1-10-18
01/10/201 8 (v2.0) Page 1 of 4 Future of Privacy Forum www.smartstoreprivacy.com Mobile Location Analytics Code of Conduct Preamble Mobile Location Analytics (MLA) provides technological solutions for Retailers by developing aggregate reports used to reduce waiting times at check – out, to optimize store layouts and to understand consumer shopping patterns. The reports are generated […]
20180413 Legitimate Interest_FPF_Nymity 2018
1 Processing Personal D ata on the Basis of Legitimate Interests under the GDPR PRACTICAL CASES The Future of Privacy Forum and Nymity would like to thank Gabriela Zanfir -Fortuna (Policy Counsel, FPF) and Teresa Troester -Falk (Chief Global Privacy Strategist, Nymity) for authoring this paper and Me aghan McCluskey (Director, Compliance Research, Nymity) principal […]
20180413-Legitimate-Interest_FPF_Nymity-2018
1 Processing Personal D ata on the Basis of Legitimate Interests under the GDPR PRACTICAL CASES The Future of Privacy Forum and Nymity would like to thank Gabriela Zanfir -Fortuna (Policy Counsel, FPF) and Teresa Troester -Falk (Chief Global Privacy Strategist, Nymity) for authoring this paper and Me aghan McCluskey (Director, Compliance Research, Nymity) principal […]
Learning From the 'Accidental Consequences' of Student-Data-Privacy Laws – Market Brief
3/16/2018 Learning From the ‘Accidental Consequences’ of Student-Data-Privacy Laws – Market Brief https://marketbrief.edweek.org/analysts-view/learning-accidental-consequences-student-data-privacy-laws/ 1/6 Michele Molnar Associate Editor Analyst’s View March 16, 2018 Learn in g F ro m t he ‘A cci den ta l C on se q u en ce s’ o f S tu den t- D ata – P riv […]
Learning From the 'Accidental Consequences' of Student-Data-Privacy Laws – Market Brief
3/16/2018 Learning From the ‘Accidental Consequences’ of Student-Data-Privacy Laws – Market Brief https://marketbrief.edweek.org/analysts-view/learning-accidental-consequences-student-data-privacy-laws/ 1/6 Michele Molnar Associate Editor Analyst’s View March 16, 2018 Learn in g F ro m t he ‘A cci den ta l C on se q u en ce s’ o f S tu den t- D ata – P riv […]
Model Benefit-Risk Analysis
1 Appendix C: Model Benefit -Risk Analysis Step 1: Evaluate the I nformation the Dataset Contains Dataset: ___ ___________________________ Consider the following categories of information: o Direct Identifiers: These are data points that identify a person without additional information or by linking to other readily availabl e information. “Personally Identifiable Information,” or PII, often falls […]
FPF Open Data Risk Assessment for City of Seattle
City of Se a ttle Open Data Risk Assessment JANUARY 2018 – FINAL REPORT 2 Table of Contents Executive Summary ………………………….. ………………………….. ………………………….. ………………………….. …… 3 Background ………………………….. ………………………….. ………………………….. ………………………….. ………………. 6 Open Data Privacy Risks ………………………….. ………………………….. ………………………….. …………………………. 7 Re-identification ………………………….. ………………………….. ………………………….. ………………………….. … 7 Data Quality and Equity ………………………….. ………………………….. […]