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Bryant Gipson

Bryant Gipson

Bryant Gipson has been at Google since 2013, for pre-Google publications see Google Scholar.
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    A General Purpose Transpiler for Fully Homomorphic Encryption
    Shruthi Gorantala
    Rob Springer
    Sean Purser-Haskell
    Asra Ali
    Eric P. Astor
    Itai Zukerman
    Sam Ruth
    Phillipp Schoppmann
    Sasha Kulankhina
    Alain Forget
    David Marn
    Cameron Tew
    Rafael Misoczki
    Bernat Guillen
    Xinyu Ye
    Damien Desfontaines
    Aishe Krishnamurthy
    Miguel Guevara
    Yurii Sushko
    Google LLC (2021)
    Preview abstract Fully homomorphic encryption (FHE) is an encryption scheme which enables computation on encrypted data without revealing the underlying data. While there have been many advances in the field of FHE, developing programs using FHE still requires expertise in cryptography. In this white paper, we present a fully homomorphic encryption transpiler that allows developers to convert high-level code (e.g., C++) that works on unencrypted data into high-level code that operates on encrypted data. Thus, our transpiler makes transformations possible on encrypted data. Our transpiler builds on Google's open-source XLS SDK (https://github.com/google/xls) and uses an off-the-shelf FHE library, TFHE (https://tfhe.github.io/tfhe/), to perform low-level FHE operations. The transpiler design is modular, which means the underlying FHE library as well as the high-level input and output languages can vary. This modularity will help accelerate FHE research by providing an easy way to compare arbitrary programs in different FHE schemes side-by-side. We hope this lays the groundwork for eventual easy adoption of FHE by software developers. As a proof-of-concept, we are releasing an experimental transpiler (https://github.com/google/fully-homomorphic-encryption/tree/main/transpiler) as open-source software. View details
    Evaluation of US State-Based Policy Interventions on Social Distancing Using Aggregated Mobility Data during the COVID-19 Pandemic
    Gregory Alexander Wellenius
    Swapnil Suresh Vispute
    Valeria Espinosa
    Thomas Tsai
    Jonathan Hennessy
    Krishna Kumar Gadepalli
    Adam Boulanger
    Adam Pearce
    Chaitanya Kamath
    Arran Schlosberg
    Catherine Bendebury
    Chinmoy Mandayam
    Charlotte Stanton
    Shailesh Bavadekar
    Christopher David Pluntke
    Damien Desfontaines
    Benjamin H. Jacobson
    Zan Armstrong
    Katherine Chou
    Andrew Nathaniel Oplinger
    Ashish K. Jha
    Evgeniy Gabrilovich
    Nature Communications (2021)
    Preview abstract Social distancing has emerged as the primary mitigation strategy to combat the COVID-19 pandemic in the United States. However, large-scale evaluation of the effectiveness of social distancing policies are lacking. We used aggregated mobility data to quantify the impact of social distancing policies on observed changes in mobility. Declarations of states of emergency resulted in approximately a 10% reduction in time spent outside places of residence and an increase in visits to grocery stores and pharmacies. Subsequent implementation of ≥1 social distancing policies resulted in an additional 25% reduction in mobility in the following week. The seven states that subsequently ordered residents to shelter in place on or before March 23, 2020 observed an additional 29% reduction in time spent outside the residence. Our findings suggest that state-wide mandates are highly effective in achieving the goals of social distancing to minimize the transmission of COVID-19. View details
    Forecasting influenza activity using machine-learned global mobility map
    Srinivasan Venkatramanan
    Adam Sadilek
    Arindam Fadikar
    Christopher L. Barrett
    Matthew Biggerstaff
    Jiangzhuo Chen
    Xerxes Dotiwalla
    Paul Eastham
    Dave Higdon
    Onur Kucuktunc
    Allison Lieber
    Bryan L. Lewis
    Zane Reynolds
    Anil K. Vullikanti
    Lijing Wang
    Madhav Marathe
    Nature Communications, vol. https://www.nature.com/articles/s41467-021-21018-5 (2021)
    Preview abstract Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model’s performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model’s ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology. View details
    Differentially Private SQL with Bounded User Contribution
    Celia Yuxin Zhang
    Damien Desfontaines
    Daniel Simmons-Marengo
    Proceedings on Privacy Enhancing Technologies Symposium (2020) (to appear)
    Preview abstract Differential privacy (DP) provides a theoretical promise to users and analysts limiting the ability to determine a user’s contribution (if any) to the results of analysis. While there have been many theoretical explorations into the design of DP algorithms, few generically practical implementations of end-to-end DP engines exist. This paper presents a practical SQL-based engine that provides privacy guarantees with respect to groups of records, possibly spanning multiple tables, owned by a single entity. To date there has been little work to provide this type of protection for multiple rows in the same table or joins more generally. The engine utilizes a novel algorithm that evaluates query aggregations using a two-step process to enforce DP per owning entity. We limit the query sensitivity impact of joins by restricting and propagating a row-owner identifier at all steps, which allows us to limit row-owner contribution. For testing, we present a semi-decidable stochastic model-checking system, used to ensure privacy for the engine’s full range of statistical functions. This model provides stronger guarantees on privacy than existing systems with comparable accuracy. The result is a general purpose SQL engine, capable of answering typical analysis questions with little or no modification to existing queries. View details
    Hierarchical organization of urban mobility and its connection with city livability
    Aleix Bassolas
    Hugo Barbosa-Filho
    Brian Dickinson
    Xerxes Dotiwalla
    Paul Eastham
    Riccardo Gallotti
    Gourab Ghoshal
    Surendra A. Hazarie
    Henry Kautz
    Onur Kucuktunc
    Allison Lieber
    Adam Sadilek
    José J. Ramasco
    Nature Communications (2019)
    Preview abstract The recent trend of rapid urbanization makes it imperative to understand urban characteristics such as infrastructure, population distribution, jobs, and services that play a key role in urban livability and sustainability. A healthy debate exists on what constitutes optimal structure regarding livability in cities, interpolating, for instance, between mono- and poly-centric organization. Here anonymous and aggregated flows generated from three hundred million users, opted-in to Location History, are used to extract global Intra-urban trips. We develop a metric that allows us to classify cities and to establish a connection between mobility organization and key urban indicators. We demonstrate that cities with strong hierarchical mobility structure display an extensive use of public transport, higher levels of walkability, lower pollutant emissions per capita and better health indicators. Our framework outperforms previous metrics, is highly scalable and can be deployed with little cost, even in areas without resources for traditional data collection. View details
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