Open access peer-reviewed Edited Volume

Advances in Differential Privacy-Protected Data Sharing & Privacy-Preserving Machine Learning

Douglas McNair

Bill & Melinda Gates Foundation


Differential privacy anonymization reidentification risk data-sharing complexity theory big-O analysis machine learning privacy assurance open science utility preservation VLDB engineering distributed noise generation probabilistic inference multiple imputation cryptography distributed computing adversary attacks nonrandom linkage disequilibrium regulatory compliance

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About the book

Many recent innovations are directed to robust anonymization of data with specified reidentification risk. Differential privacy (DP) minimizes privacy impact in shareable microdata by injecting noise beyond sampling or by randomly generating synthetic data that is distributionally close to the actual dataset. Additionally, there is great interest in achieving privacy-preserving versions of statistical and machine learning algorithms. With increased emphasis on privacy, DP has growing prevalence in data engineering and analysis. However, many challenges remain. For example, DP operating on real-world data may not preserve enough information utility after DP filtering and augmentation. There is a need to strike a reasoned balance between privacy and precision of data. Some critical questions for statisticians, data scientists, and engineers include:

● Can varying levels of privacy assurance be built into DP models, to accommodate varying willingness to share personal information than others, especially if this results in more utility for them.

● How is differential privacy able to address evolving statistical distributions of model features in streaming data use-cases?

● How shall we statistically account for excess randomness introduced by privacy-preserving methods?

This book aims to provide an account of the current state-of-the-art in DP and developments in this strategically important area.

Publishing process

Book initiated and editor appointed

Date completed: November 27th 2019

Applications to edit the book are assessed and a suitable editor is selected, at which point the process begins.

Chapter proposals submitted and reviewed

Deadline Extended: Open for Submissions

Potential authors submit chapter proposals ready for review by the academic editor and our publishing review team.

Approved chapters written in full and submitted

Deadline for full chapters: May 31st 2020

Once approved by the academic editor and publishing review team, chapters are written and submitted according to pre-agreed parameters

Full chapters peer reviewed

Review results due: August 1st 2020

Full chapter manuscripts are screened for plagiarism and undergo a Main Editor Peer Review. Results are sent to authors within 30 days of submission, with suggestions for rounds of revisions.

Book compiled, published and promoted

Expected publication date: October 31st 2020

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About the editor

Douglas McNair

Bill & Melinda Gates Foundation

Doug McNair serves as a Senior Advisor in Quantitative Sciences – Analytics Innovation in Global Health at the Bill & Melinda Gates Foundation. He assists the foundation’s R&D in drug and vaccine development for infectious diseases, childhood diseases, and neglected tropical diseases. Current projects include product development programs in Discovery & Translational Sciences involving Bayesian Networks. His activity also includes machine-learning and modeling of health economics collaborating with BMGF\'s Global Development division. Previously, Doug was President of Cerner Math Inc., responsible for Artificial Intelligence components of Cerner’s EHR solutions, discovering AI predictive models from real-world de-identified EHR-derived Big Data. Doug is lead inventor on more than 100 patents and pending patent applications, including several involving Bayesian predictive models for clinical diagnostics.

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Book chapters authored 2

Books edited 1

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