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Issue Date: March 12th 2019
Last modified: August 1st 2019
Learning

UN Privacy Preserving Techniques Handbook

In this UN handbook, we define specific goals for privacy-preserving computation for public good in two salient use cases: giving NSOs access to new sources of (sensitive) Big Data; and enabling Big Data Collaborations Across Multiple NSOs.

Link: https://docs.google.com/document/d/1GYu6UJI81jR8LgooXVDsYk1s6FlM-SbOvo3oLHglFhY

Partner: The GWG Task Team on Privacy Preservation Techniques

We describe the limits of current practice in analyzing data while preserving privacy; explain emerging privacy-preserving computation techniques; and outline key challenges to bringing these technologies into mainstream use.

For each technology addressed, we provide:

  • a technical overview; examples of applied uses;
  • an explanation of modeling adversaries and security arguments that typically apply;
  • an overview of the costs of using the technology;
  • an explanation of availability of the technology;
  • and a Wardley map that illustrates the technology readiness and suggested development focus for the technology.

Handbook Purpose and Target Audience

This document describes motivations for privacy-preserving approaches for the statistical analysis of sensitive data; presents examples of use cases where such methods may apply; and describes relevant technical capabilities to assure privacy preservation while still allowing analysis of sensitive data. Our focus is on methods that enable protecting privacy of data while it is being processed rather than while it is at rest on a system or in transit between systems.

This document is intended for use by statisticians and data scientists, data curators and architects, IT specialists, and security and information assurance specialists, so we explicitly avoid cryptographic technical details of the technologies we describe.

Also available as a pdf

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