Differentially private quantum sensor network protocols are introduced that inject noise into the sensing Hamiltonian, achieving (O(1), δ)-differential privacy while retaining Heisenberg-limited MSE scaling under honest-fraction and common-source-of-randomness assumptions.
Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1)
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abstract
This document describes the aggregation and anonymization process applied to the initial version of Google COVID-19 Community Mobility Reports (published at http://google.com/covid19/mobility on April 2, 2020), a publicly available resource intended to help public health authorities understand what has changed in response to work-from-home, shelter-in-place, and other recommended policies aimed at flattening the curve of the COVID-19 pandemic. Our anonymization process is designed to ensure that no personal data, including an individual's location, movement, or contacts, can be derived from the resulting metrics. The high-level description of the procedure is as follows: we first generate a set of anonymized metrics from the data of Google users who opted in to Location History. Then, we compute percentage changes of these metrics from a baseline based on the historical part of the anonymized metrics. We then discard a subset which does not meet our bar for statistical reliability, and release the rest publicly in a format that compares the result to the private baseline.
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A differentially private pipeline using node-level DP summaries to fit ERGMs or SBMs, generate synthetic networks, and simulate SIS disease spread on ARTNet sexual contact data produces incidence, prevalence, and intervention effect sizes close to non-private versions.
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Differentially private quantum sensor networks
Differentially private quantum sensor network protocols are introduced that inject noise into the sensing Hamiltonian, achieving (O(1), δ)-differential privacy while retaining Heisenberg-limited MSE scaling under honest-fraction and common-source-of-randomness assumptions.
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Differentially Private Modeling of Disease Transmission within Human Contact Networks
A differentially private pipeline using node-level DP summaries to fit ERGMs or SBMs, generate synthetic networks, and simulate SIS disease spread on ARTNet sexual contact data produces incidence, prevalence, and intervention effect sizes close to non-private versions.